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    Key Technologies and Prospects of Laser Weeding Robots
    YU Zhongyi, WANG Hongyu, HE Xiongkui, ZHAO Lei, WANG Yuanyuan, SUN Hai
    Smart Agriculture    2025, 7 (2): 132-145.   DOI: 10.12133/j.smartag.SA202410031
    Abstract2709)   HTML87)    PDF(pc) (6774KB)(784)       Save

    [Significance] Grass damage in farmland seriously restricts the quality and yield of crop planting and production, and promotes the occurrence of pests and diseases. Weed control is a necessary measure for high yield and high quality of crops. Currently, there are five main weed control methods: Manual, biological, thermal, mechanical, and chemical weed control. Traditional chemical weed control methods are gradually limited due to soil pollution and ecological balance disruption. Intelligent laser weeding technology, with the characteristics of environmental protection, high efficiency, flexibility, and automation, as an emerging and promising ecological and environmental protection new object control method for field weeds, has become the core direction to replace chemical weeding in recent years. The laser weeding robot is the carrier of laser weeding technology, an important manifestation of the development of modern agriculture towards intelligence and precision, and has great application and promotion value. [Progress] Laser weeding is currently a research hotspot to develop and study key technologies and equipment for smart agriculture, and has achieved a series of significant results, greatly promoting the promotion and application of intelligent laser weeding robots in the field. Laser weed control technology achieves precise weed control through thermal, photochemical, and photodynamic effects. In this article, the research background of laser weeding was introduced, its key technologies, operation system and equipment were discussed in details, covering aspects such as operating principles, system architecture, seedling, weed recognition and localization, robot navigation and path planning, as well as actuator control technologies. Then, based on the current research status of laser weeding robots, the existing problems and development trends of intelligent laser weeding robots were prospected. [Conclusion and Prospect] Based on the different field grass conditions in different regions, a large number of indoor and outdoor experiments on laser weed control should be carried out in the future to further verify the technical effectiveness and feasibility of laser field weed control, providing support for the research and application of laser weed control equipment technology. Despite facing challenges such as high costs and poor environmental adaptability, with the integration of technologies such as artificial intelligence and the Internet of Things, as well as policy support, laser weeding is expected to become an important support for sustainable agricultural development.

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    Spectral Technology in Vegetable Production Detection: Research Progress, Challenges and Suggestions
    BAI Juekun, CHEN Huaimeng, DONG Daming, LIU Yachao, YUE Xiaolong, DU Xiuke
    Smart Agriculture    2025, 7 (4): 1-17.   DOI: 10.12133/j.smartag.SA202504027
    Abstract2131)   HTML58)    PDF(pc) (1474KB)(117)       Save

    [Significance] Vegetables are indispensable to global food security and human nutrition, yet approximately 33% of the annual 1.2 billion-ton harvest is lost or wasted, largely because of undetected biotic and abiotic stresses, poor post-harvest management, and chemical safety hazards. Conventional analytical workflows, based on wet chemistry and chromatography, are destructive, labour-intensive, and difficult to scale, creating an urgent need for rapid, non-invasive sensing tools that can operate across the full production-to-consumption continuum. Optical spectroscopy, spanning near-infrared (NIR), Raman, fluorescence, laser-induced breakdown spectroscopy (LIBS), and UV-Vis modalities, offers label-free, multiplexed, and second-scale measurements directly on living plants or minimally processed products. Existing reviews have concentrated on isolated techniques or single application niches, leaving critical knowledge gaps regarding hardware robustness under open-field conditions, algorithmic generalisability across cultivars and climates, data interoperability, and cost-driven adoption barriers for smallholders. [Progress] This paper presents a holistic, chain-wide appraisal of spectroscopic sensing in vegetable production. It shows that hardware evolution has been dominated by miniaturisation and functional integration. Hand-held NIR units (e.g., Neospectra MEMS, NirVana AG410) now weigh <300 g and achieve R2 > 0.95 for soluble solids and moisture in tomato, zucchini, and pepper. Palm-top Raman systems (9 × 7 × 4 cm) equipped with 1 064 nm lasers and InGaAs detectors suppress fluorescence sufficiently to quantify lycopene (RMSE = 1.14 mg/100 g) and classify ripeness stages with 100% accuracy. Battery-powered fluorescence sensors coupled with smartphones wirelessly stream data to cloud-based convolutional neural networks (CNNs), delivering 93%~100% correct cultivar identification for spinach, onion, and tomato seeds within 5 s per sample. Methodological advances combine advanced chemometrics and deep learning. Transfer learning enables a model trained on greenhouse tomatoes to predict field-grown cherry tomatoes with only 10% recalibration samples, cutting data acquisition costs by 70%. SERS substrates, fabricated as flexible "place-and-play" nano-mesh films, boost Raman signals by 106~108, pushing limits of detection for carbaryl, imidacloprid, and thiamethoxam below 1 mg/kg on pak-choi and lettuce. Multi-modal fusion (LIBS-NIR) simultaneously quantifies macro-elements (Ca, K, Mg) and micro-elements (Fe, Mn) with relative errors <5%. Chain-wide demonstrations span five critical stages: (i) breeding—NIR screens seed viability via starch and moisture signatures; (ii) cultivation—portable Raman "leaf-clip" sensors detect nitrate deficiency (1 045 cm-1 peak) and early pathogen attack (LsoA vs. LsoB, 80% accuracy) in lettuce and tomato before visible symptoms emerge; (iii) harvest—non-invasive lycopene monitoring in tomato and carotenoid profiling in chilli guides optimal picking time and reduces post-harvest losses by 15%; (iv) storage—chlorophyll fluorescence tracks water loss and senescence in black radish and carrot over six-month cold storage, enabling dynamic shelf-life prediction; (v) market entry—LIBS inspects incoming crates for Pb and Cd in seconds, while fluorescence-SVM pipelines simultaneously verify pesticide residues, ensuring compliance with EU and Chinese MRLs. Data governance initiatives are emerging but remain fragmented. Several consortia have released open spectral libraries (e.g., VegSpec-1.0 with 50 000 annotated spectra from 30 vegetable species), yet differences in acquisition parameters, preprocessing pipelines, and metadata schemas hinder cross-study reuse. [Conclusions and Prospects] Spectroscopic sensing has matured from laboratory proof-of-concept to robust field prototypes capable of guiding real-time decisions across the entire vegetable value chain. Nevertheless, four priority areas must be addressed to unlock global adoption: Model generalisation—curate large-scale, multi-environment, multi-cultivar spectral repositories and embed meta-learning algorithms that continuously adapt to new genotypes and climates with minimal retraining. Hardware resilience—develop self-calibrating sensors with adaptive optics and real-time environmental compensation (temperature, humidity, ambient light) to maintain laboratory-grade SNR in dusty, humid, or high-irradiance field settings. Standardisation and interoperability—establish ISO-grade protocols for hardware interfaces, data formats, calibration transfer, and privacy-preserving data sharing, enabling seamless integration of devices, clouds, and decision-support platforms. Cost-effective commercialisation—pursue modular, open-hardware designs leveraging printed optics and economies of scale to reduce unit costs below USD 500, and introduce service-based models (leasing, pay-per-scan) tailored to smallholder economics. If these challenges are met, spectroscopy-based digital twins of vegetable production systems could become a reality, delivering safer food, reduced waste, and climate-smart agriculture within the next decade.

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    Smart Supply Chains for Agricultural Products: Key Technologies, Research Progress and Future Direction
    HAN Jiawei, YANG Xinting
    Smart Agriculture    2025, 7 (3): 1-16.   DOI: 10.12133/j.smartag.SA202501006
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    [Significance] The smart transformation of agricultural product supply chains is an essential solution to the challenges faced by traditional supply chains, such as information asymmetry, high logistics costs, and difficulties in quality traceability. This transformation also serves as a vital pathway to modernize agriculture and enhance industrial competitiveness. By integrating technologies such as the Internet of Things (IoT), big data, and artificial intelligence (AI), smart supply chains facilitate precise production and processing, efficient logistics distribution, and transparent quality supervision. As a result, they improve circulation efficiency, ensure product safety, increase farmers' incomes, and promote sustainable agricultural development. Furthermore, in light of global shifts in agricultural trade, this transformation bolsters the international competitiveness of China's agricultural products and propels the agricultural industrial chain toward higher value-added segments. This paper systematically examines the conceptual framework, technological applications, and future trends of smart supply chains, aiming to provide a theoretical foundation for industry practices and insights for policymaking and technological innovation. [Progress] In the production phase, IoT and remote sensing technologies enable real-time monitoring of crop growth conditions, including soil moisture, temperature, and pest infestation, facilitating precision irrigation, fertilization, and pest management. Big data analysis, coupled with AI algorithms, helps in predicting crop yields, optimizing resource allocation, and minimizing waste. Additionally, AI-driven smart pest control systems can dynamically adjust pesticide application, reducing chemical usage and environmental impact. The processing stage leverages advanced technologies for efficient sorting, grading, cleaning, and packaging. Computer vision and hyperspectral imaging technologies enhance the sorting efficiency and quality inspection of agricultural products, ensuring only high-quality products proceed to the next stage. Novel cleaning techniques, such as ultrasonic and nanobubble cleaning, effectively remove surface contaminants and reduce microbial loads without compromising product quality. Moreover, AI-integrated systems optimize processing lines, reduce downtime and enhance overall throughput. Warehousing employs IoT sensors to monitor environmental conditions like temperature, humidity, and gas concentrations, ensure optimal storage conditions for diverse agricultural products. AI algorithms predict inventory demand, optimize stock levels to minimize waste and maximize freshness. Robotics and automation in warehouses improve picking, packing, and palletizing efficiency, reduce labor costs and enhance accuracy. The transportation sector focuses on cold chain innovations to maintain product quality during transit. IoT-enabled temperature-controlled containers and AI-driven scheduling systems ensure timely and efficient delivery. Additionally, the integration of blockchain technology provides immutable records of product handling and conditions, enhances transparency and trust. The adoption of new energy vehicles, such as electric and hydrogen-powered trucks, further reduces carbon footprints and operating costs. In the distribution and sales stages, big data analytics optimize delivery routes, reducing transportation time and costs. AI-powered demand forecasting enables precise inventory management, minimizes stockouts and excess inventories. Moreover, AI and machine learning algorithms personalize marketing efforts, improve customer engagement and satisfaction. Blockchain technology ensures product authenticity and traceability, enhances consumer trust. [Conclusions and Prospects] As technological advancements and societal demands continue to evolve, the smart transformation of agricultural product supply chains has become increasingly urgent. Future development should prioritize unmanned operations to alleviate labor shortages and enhance product quality and safety. Establishing information-sharing platforms and implementing refined management practices are crucial for optimizing resource allocation, improving operational efficiency, and enhancing international competitiveness. Additionally, aligning with the "dual-carbon" strategy by promoting clean energy adoption, optimizing transportation methods, and advocating for sustainable packaging will drive the supply chain toward greater sustainability. However, the application of emerging technologies in agricultural supply chains faces challenges such as data governance, technical adaptability, and standardization. Addressing these issues requires policy guidance, technological innovation, and cross-disciplinary collaboration. By overcoming these challenges, the comprehensive intelligent upgrade of agricultural product supply chains can be achieved, ultimately contribute to the modernization and sustainable development of the agricultural sector.

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    Grading Asparagus officinalis L. Using Improved YOLOv11
    YANG Qilang, YU Lu, LIANG Jiaping
    Smart Agriculture    2025, 7 (4): 84-94.   DOI: 10.12133/j.smartag.SA202501024
    Abstract1829)   HTML76)    PDF(pc) (1922KB)(169)       Save

    [Objective] Asparagus officinalis L. is a perennial plant with a long harvesting cycle and fast growth rate. The harvesting period of tender stems is relatively concentrated, and the shelf life of tender stems is very short. Therefore, the harvested asparagus needs to be classified according to the specifications of asparagus in a short time and then packaged and sold. However, at this stage, the classification of asparagus specifications basically depends on manual work, and it is difficult for asparagus of different specifications to rely on sensory grading, which requires a lot of money and labor. To save labor costs, an algorithm based on asparagus stem diameter classification was developed using deep learning and computer vision technology. YOLOv11 was selected as the baseline model and several improvements were made to propose a lightweight model for accurate grading of post-harvest asparagus. [Methods] Dataset was obtained by cell phone photography of post-harvest asparagus using fixed camera positions. In order to improve the generalization ability of the model, the training set was augmented with data by increasing contrast, mirroring, and adjusting brightness. The data-enhanced training set included a total of 2 160 images for training the model, and the test set and validation set included 90 and 540 images respectively for inference and validation of the model. In order to enhance the performance of the improved model, the following four improvements were made to the baseline model, respectively. First, the efficient channel attention (ECA) module was added to the twelfth layer of the YOLOv11 backbone network. The ECA enhanced asparagus stem diameter feature extraction by dynamically adjusting channel weights in the convolutional neural network and improved the recognition accuracy of the improved model. Second, the bi-directional feature pyramid network (BiFPN) module was integrated into the neck network. This module modified the original feature fusion method to automatically emphasize key asparagus features and improved the grading accuracy through multi-scale feature fusion. What's more, BiFPN dynamically adjusted the importance of each layer to reduce redundant computations. Next, the slim-neck module was applied to optimize the neck network. The slim-neck module consisted of GSConv and VoVGSCSP. The GSConv module replaced the traditional convolutional. And the VoVGSCSP module replaced the C2k3 module. This optimization reduced computational costs and model size while improving the recognition accuracy. Finally, the original YOLOv11 detection head was replaced with an EfficientDet Head. EfficientDet Head had the advantages of light weight and high accuracy. This head co-training with BiFPN to enhance the effect of multi-scale fusion and improve the performance of the model. [Results and Discussions] In order to verify the validity of the individual modules introduced in the improved YOLOv11 model and the superiority of the performance of the improved model, ablation experiments and comparison experiments were conducted respectively. The results of the comparison test between different attentional mechanisms added to the baseline model showed that the ECA module had better performance than other attentional mechanisms in the post-harvest asparagus grading task. The YOLOv11-ECA had higher recognition accuracy and smaller model size, so the selection of the ECA module had a certain degree of reliability. Ablation experiments demonstrated that the improved YOLOv11 achieved 96.8% precision (P), 96.9% recall (R), and 92.5% mean average precision (mAP), with 4.6 GFLOPs, 1.67 × 10⁶ parameters, and a 3.6 MB model size. The results of the asparagus grading test indicated that the localization frames of the improved model were more accurate and had a higher confidence level. Compared with the original YOLOv11 model, the improved YOLOv11 model increased the precision, recall, and mAP by 2.6, 1.4, and 2.2 percentage points, respectively. And the floating-point operation, parameter quantity, and model size were reduced by 1.7 G, 9.1 × 105, and 1.6 MB, respectively. Moreover, various improvements to the model could increase the accuracy of the model while ensuring that the model was light weight. In addition, the results of the comparative tests showed that the performance of the improved YOLOv11 model was better than those of SSD, YOLOv5s, YOLOv8n, YOLOv11, and YOLOv12. Overall, the improved YOLOv11 had the best overall performance, but still had some shortcomings. In terms of the real-time performance of the model, the inference speed of the improved model was not optimal, and the inference speed of the improved YOLOv11 was inferior to that of YOLOv5s and YOLOv8n. The inference speed of improved YOLOv11 and YOLOv11 evaluate using the aggregate test. The results of the Wilcoxon signed-rank test showed that the improved YOLOv11 had a significant improvement in inference speed compared to the original YOLOv11 model. [Conclusions] The improved YOLOv11 model demonstrated better recognition, lower parameters and floating-point operations, and smaller model size in the asparagus grading task. The improved YOLOv11 could provide a theoretical foundation for intelligent post-harvest asparagus grading. Deploying the improved YOLOv11 model on asparagus grading equipment enables fast and accurate grading of post-harvest asparagus.

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    Monte Carlo Simulation of Light Propagation in Orah Mandarin Tissues and Optimization of Spectral Detection in Diffuse Reflection Mode
    OUYANG Aiguo, WANG Yang, LIU Yande, HOU Youfei, WANG Guantian
    Smart Agriculture    2025, 7 (4): 47-57.   DOI: 10.12133/j.smartag.SA202505029
    Abstract1794)   HTML19)    PDF(pc) (1690KB)(52)       Save

    [Objective] Visible light/near-infrared (Vis/NIR) spectroscopy serves as an effective method for quality assessment of orah mandarin. However, as a multi-layered thick-skinned fruit, the optical properties (OPs) of different tissue layers in orah mandarin affect quality evaluation, resulting in weak signals and difficulties in extracting pulp information when applying Vis/NIR spectroscopy in practical applications. This research utilizes Monte Carlo methods to reveal the light propagation mechanism within the multi-layered tissues of orah mandarin, clarify the optical properties of each tissue layer and their contributions to detection signals, and provide theoretical basis and technical support for optimizing spectral detection systems under diffuse reflectance mode. [Methods] Orah mandarin was selected as the research material. The optical parameters of its oil sac layer, albedo layer, and pulp tissue were measured in the 500~1 050 nm band using a single integrating sphere system combined with the Inverse Adding-Doubling method (Integrating Sphere-Inverse Adding-Doubling method, IS-IAD). Based on the optical parameters of different tissue layers, a three-layer concentric sphere model (oil sac layer, albedo layer, and pulp tissue) was established. The voxel-based Monte Carlo eXtreme (MCX) method was employed to study the transmission patterns of simulated photons in orah mandarin under diffuse reflectance mode, in order to optimize the configuration of detection devices. [Results and Discussions] The experimental results demonstrated that throughout the entire wavelength range, the oil sac layer and albedo layer exhibited identical variation trends in average absorption coefficient and average reduced scattering coefficient. The oil sac layer, rich in liposoluble pigments such as carotenoids, resulted in a peak absorption coefficient at 500 nm, while the porous structure of the albedo layer led to a higher reduced scattering coefficient, and the pulp tissue exhibited the lowest reduced scattering coefficient due to its translucent structure. Light penetration depth analysis revealed that in the 500~620 nm band, the light penetration depth of the oil sac layer was higher than that of the albedo layer, while at 980 nm, due to water molecule absorption, the light penetration depth of the pulp tissue showed a significant valley. Monte Carlo simulation results indicated that light was primarily absorbed within orah mandarin tissue, with transmitted photons accounting for less than 4.2%. As the source-detector distance increased, the average optical path and light attenuation in orah mandarin tissue showed an upward trend, while the contribution rates of the oil sac layer, albedo layer, and pulp tissue to the detected signal showed decreasing, decreasing, and increasing trends, respectively. Additionally, the optical diffuse reflectance decreased significantly with increasing source-detector distance. Based on the simulation results, it was recommended that the source-detector distance for orah mandarin quality detection devices should be set in the range of 13~15 mm. This configuration could maintain a high signal contribution rate from pulp tissue while obtaining sufficient diffuse reflectance signal strength, thereby improving detection accuracy and reliability. [Conclusions] The combination of Vis/NIR spectroscopy and Monte Carlo simulation methods systematically reveals the light propagation patterns and energy distribution within orah mandarin tissue, providing important theoretical basis and methodological support for non-destructive detection of orah mandarin. By employing a single integrating sphere system with the Inverse Adding-Doubling method to obtain optical parameters of each tissue layer and utilizing voxel-based Monte Carlo simulation to thoroughly investigate photon propagation patterns within the fruit, this research accurately quantifies the contribution rates of different tissue layers to diffuse reflectance signals and effectively optimizes key parameters of the detection system. These findings provide important references for developing more precise non-destructive detection methods and equipment for orah mandarin.

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    Acoustic-Vibration Detection Method for The Apple Moldy Core Disease Based on D-S Evidence Theory
    LIU Jie, ZHAO Kang, ZHAO Qinjun, SONG Ye
    Smart Agriculture    2025, 7 (4): 119-131.   DOI: 10.12133/j.smartag.SA202505032
    Abstract1741)   HTML9)    PDF(pc) (2371KB)(34)       Save

    [Objective] Moldy core disease is a common internal disease of the apple and is highly infectious. In the early storage stage, the mold symptoms are confined to the interior of the core. The apple tissue is in a sub-healthy state and still has commercial value, so early detection of moldy-core apples is critical. [Methods] In this study, a non-destructive acoustic vibration detection system was used to acquire acoustic vibration response signals. Symmetrized dot pattern (SDP), gramian angular field (GAF), and stockwell transform (ST) were applied to obtain multi-domain acoustic vibration spectra, including SDP images, gramian angular summation field (GASF) images, gramian angular difference field (GADF) images, and ST images. These images were uniformly converted to grayscale, transforming the time-domain signals into multi-domain visual spectra to facilitate subsequent feature analysis and recognition. Uniform local binary pattern (ULBP) and gray-level-gradient co-occurrence matrix (GLGCM) methods were used to extract handcrafted features from the multi-domain visualized images. Subsequently, the maximum relevance and minimum redundancy (mRMR) criterion was applied to select the dominant features from each analysis domain that were sensitive to early disease information. Principal component analysis (PCA) was employed to reduce the dimensionality of the multi-domain spectral ULBP texture features. From the statistical features extracted from one-dimensional acoustic-vibration signals in the time and frequency domains, and the GLGCM texture features extracted from two-dimensional images in each domain, 5 to 8 features sensitive to early moldy core detection were selected. From the high-dimensional sensitive ULBP texture features extracted from each domain, 3 to 7 principal components were obtained through dimensionality reduction using principal component analysis. This selection aimed to maximize the relevance between features and class labels while minimizing redundancy among features, thereby identifying the most informative features for early mold core apple detection in each domain. Meanwhile, a ResNet50 feature extractor improved with a convolutional attention mechanism module and the Adam optimizer (Adam-IResNet50) was designed to automatically extract deep features from visualized images in each domain. The optimal shallow features were used to train a multiple support vector machine (MSVM) classifier, while the optimal deep features were used to train an extreme learning machine (ELM) classifier. The Adam-IResNet50 network was employed as a feature extractor. The deep features extracted from the time-domain and frequency-domain GADF images, as well as time-frequency images, resulted in higher sample matching scores (SC) and cluster compactness (CHS) values, along with lower inter-class overlap (DBI) values for the three apple categories. These results clearly indicate that the deep features extracted by the Adam-IResNet50 model from multi-domain images exhibit strong capability in identifying subhealth and moldy core apples. The preliminary outputs of the two classifiers were converted into basic probability assignments for independent evidence bodies. Dempster's combination rule and the associated decision criterion of Dempster-Shafer (D-S) theory were then applied to yield the final decision on early-stage moldy apples. Consequently, a decision-level fusion model was established for both shallow and deep features of the acoustic-vibration multi-domain spectra. [Results and Discussions] The constructed Adam-IResNet50-IPSO-ELM-DS model based on D-S evidence theory achieved a Kappa coefficient and Matthews Correlation Coefficient (MCC) slightly below 90% for multi-class classification of apples from known origins. The F1-Score and Overall Accuracy (OA) reached 93.01% and 93.22%, respectively. The classification accuracy for sub-healthy apples was 87.37%, while the misclassification rate for diseased apples was 8.33%. These results indicate that the model maintains a balanced precision and recall while achieving high detection accuracy for three classes of apples from unknown origins. After decision fusion, the IPSO-MSVM-DS and Adam-IResNet50-IPSO-ELM-DS models demonstrated significant performance improvements. Among them, the Adam-IResNet50-IPSO-ELM-DS model achieved an accuracy of 93.22%, which was significantly higher than that of other methods. This demonstrates that decision-level fusion could effectively enhance the model's discriminative ability and further improve classification performance. [Conclusions] The proposed acoustic vibration detection method for mold core apples, based on Dempster-Shafer evidence theory, provides technical support for future online batch detection of early mold core apples. Early screening of sub-healthy apples is of great significance for quality control during postharvest storage. In future work, the model will be further optimized to develop a rapid acoustic vibration-based prediction method for early detection of mold core, providing technical support for quality control during apple distribution.

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    Non-destructive Detection of Apple Water Core Disease Based on Hyperspectral and X-ray CT Imaging
    YU Xinyuan, WANG Zhenjie, YOU Sicong, TU Kang, LAN Weijie, PENG Jing, ZHU Lixia, CHEN Tao, PAN Leiqing
    Smart Agriculture    2025, 7 (4): 108-118.   DOI: 10.12133/j.smartag.SA202507022
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    [Objective] Apple "sugar-glazed core" (also known as watercore) is a common physiological disorder in apple fruits. Apples with watercore possess a distinctive flavor and are highly favored by consumers. However, severely affected apples are prone to mold growth during storage, posing potential food safety risks. Currently, the primary method for detecting sugar-glazed core in apple relies on manual destructive inspection, which is inefficient for large-scale applications and fails to meet the demands of modern automated and intelligent industrial production. To achieve rapid and non-destructive detection of apples with varying watercore severity levels, effective grading and soluble solids content (SSC) prediction models were developed in this study. [Methods] The Xinjiang Aksu Red Fuji apples were used as the research subject. A total of 230 apple samples were selected, comprising 113 normal, 61 mild, 47 moderate, and 9 severe watercore apples. The watercore severity was quantified through image processing of the apples' cross-sectional images. X-ray computed tomography (X-ray CT) data were acquired, and SSC values were measured. A hyperspectral imaging system was used to collect reflectance spectra within the 400~1 000 nm range. After performing black-and-white correction and selecting regions of interest (ROI), the Sample Set Partitioning based on Joint X-Y Distances (SPXY) algorithm was applied to divide the dataset into modeling (training) and prediction sets at a 3:1 ratio. Using the iToolbox in MATLAB, discriminant models were constructed based on partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and convolutional neural network (CNN) algorithms with reflectance spectral data as the input. Regression models for predicting SSC across different watercore severity levels were also established. Feature wavelength selection was carried out using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE) methods. [Results and Discussions] The results indicated that as watercore severity increased, the SSC of Red Fuji apples exhibited an upward trend. The average SSC values were 13.4% for normal apples, 14.9% for mild watercore apples, 15.0% for moderate watercore apples, and 16.0% for severe watercore apples. X-ray CT imaging revealed that the average tissue density of watercore-affected regions was higher than that of healthy tissues. Three-dimensional reconstruction algorithms allowed visualization of the internal spatial distribution of watercore tissues at different severity levels. The spatial volume proportions of watercore tissues were 3.92% in mild, 6.11% in moderate, and 10.23% in severe watercore apples. Apples with severe watercore demonstrated higher spectral reflectance. The PLS-DA-based grading model achieved accuracies of 98.7% in the training set and 95.9% in the test set. The model based on feature wavelengths selected by the UVE algorithm also showed high precision, with accuracies of 95.67% in the training set and 86.06% in the test set. For SSC regression modeling, the partial least squares regression (PLSR) model performed best, with a coefficient of determination for calibration (RC2) of 0.962, root mean square error of calibration (RMSEC) of 0.264, coefficient of determination for prediction (RP2) of 0.879, and root mean square error of prediction (RMSEP) of 0.435. The model based on feature wavelengths selected by the SPA algorithm exhibited further improved prediction performance, yielding RC2 0.846, RMSEC 0.532, RP2 0.792, RMSEP 0.576, coefficient of determination for cross-validation (RCV2) 0.781, and root mean square error of cross-validation (RMSECV) 0.637. [Conclusions] This study leveraged hyperspectral imaging and X-ray CT technologies to analyze differences in optical reflectance and microstructural characteristics of apple tissues across different watercore severity levels. The developed grading model effectively predicted watercore severity in apples, providing critical technical support for the development of intelligent post-harvest sorting equipment. The SSC regression model accurately predicted SSC values in apples with varying watercore severity, offering an efficient method for non-destructive detection and quality assessment of watercore-affected apples.

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    Advances in the Application of Multi-source Data Fusion Technology in Non-Destructive Detection of Apple
    GUO Qi, FAN Yixuan, YAN Xinhuan, LIU Xuemei, CAO Ning, WANG Zhen, PAN Shaoxiang, TAN Mengnan, ZHENG Xiaodong, SONG Ye
    Smart Agriculture    2025, 7 (4): 31-46.   DOI: 10.12133/j.smartag.SA202505036
    Abstract1635)   HTML21)    PDF(pc) (1579KB)(51)       Save

    [Significance] Apple industry is a prominent agricultural sector that is of considerable importance globally. Ensuring the highest standards of quality and safety is paramount for achieving consumer satisfaction. Non-destructive testing technologies have emerged as a powerful tool, enabling rapid and objective evaluation of fruit attributes. However, individual non-destructive testing technologies methods frequently possess inherent limitations, proving insufficient for comprehensive assessment. The synergistic application of multi-source data fusion technology in the non-destructive testing integrates information from multiple sensors to overcome the shortcomings of single-modality systems. The integration of disparate data streams constitutes the foundational technological framework that enables the advancement of apple quality control. This technological framework facilitates enhanced detection of defects and diseases, thereby contributing to the intelligent transformation of the apple industry value chain in its entirety. [Progress] This paper presents a systematic and comprehensive examination of recent advancements in multi-source data fusion for apple non-destructive testing. The principles, advantages, and typical application scenarios of five mainstream non-destructive testing technologies are first introduced: near-infrared (NIR) spectroscopy, particularly adept at quantifying internal chemical compositions such as soluble solids content (SSC) and firmness by analyzing molecular vibrations; hyperspectral imaging (HSI), which combines spectroscopy and imaging to provide both spatial and spectral information, making it ideal for visualizing the distribution of chemical components and identifying defects like bruises; electronic nose (E-nose) technology, a method for detecting unique patterns of volatile organic compounds (VOCs) to profile aroma and detect mold; machine vision, a process that analyzes external features such as color, size, shape, and texture for grading and surface defect identification; and nuclear magnetic resonance (NMR), a technique that provides detailed insights into internal structures and water content, useful for detecting internal defects such as core rot. A critical evaluation of the fundamental methodologies in data fusion is conducted, with these methodologies categorized into three distinct levels. Data-level fusion entails the direct concatenation of raw data from homogeneous sensors or preprocessed heterogeneous sensors. This approach is straightforward. It can result in high dimensionality and is susceptible to issues related to data co-registration. Feature-level fusion, the most prevalent strategy, involves extracting salient features from each data source (e.g., spectral wavelengths, textural features, gas sensor responses) and subsequently combining these feature vectors prior to model training. This intermediate approach effectively reduces redundancy and noise, and enhances model robustness. Decision-level fusion operates at the highest level of abstraction, where independent models are trained for each data modality, and their outputs or predictions are integrated using algorithms such as weighted averaging, voting schemes, or fuzzy logic. This strategy offers maximum flexibility for integrating highly disparate data types. The paper also thoroughly elaborates on the practical implementation of these strategies, and presents case studies on the fusion of different spectral data (e.g., NIR and HSI), the integration of spectral and E-nose data for combined internal quality and aroma assessment, and the powerful combination of machine vision with spectral data for simultaneous evaluation of external appearance and internal composition. [Conclusions and Prospects] The integration of multi-source data fusion technology has driven significant advancements in the field of apple non-destructive testing. This progress has substantially improved the accuracy, reliability, and comprehensiveness of quality evaluation and control systems. By synergistically combining the strengths of different sensors, it enables a holistic assessment that is unattainable with any single technology. However, the field faces persistent challenges, including the effective management of data heterogeneity (i.e., varying scales, dimensions, and physical meanings), the high computational complexity of sophisticated fusion models, and the poor portability of current multi-sensor laboratory equipment—all of which hinder online industrial applications. Future research should prioritize several key areas. First, developing automated, user-friendly fusion platforms is imperative to simplify data processing and model deployment. Second, optimizing and developing lightweight algorithms (e.g., through model compression and knowledge distillation) is critical to enhancing real-time performance for high-throughput sorting lines. Third, creating compact, cost-effective, integrated hardware that combines multiple detection technologies into a single portable device will improve stability and accessibility. Additionally, new application frontiers should be explored, such as in-field monitoring of fruit maturation and predicting post-harvest shelf life. The innovative integration of advanced algorithms and hardware holds the potential to provide substantial support for the intelligent and sustainable development of the global apple industry.

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    A Lightweight Model for Detecting Small Targets of Litchi Pests Based on Improved YOLOv10n
    LI Zusheng, TANG Jishen, KUANG Yingchun
    Smart Agriculture    2025, 7 (2): 146-159.   DOI: 10.12133/j.smartag.SA202412003
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    [Objective] The accuracy of identifying litchi pests is crucial for implementing effective control strategies and promoting sustainable agricultural development. However, the current detection of litchi pests is characterized by a high percentage of small targets, which makes target detection models challenging in terms of accuracy and parameter count, thus limiting their application in real-world production environments. To improve the identification efficiency of litchi pests, a lightweight target detection model YOLO-LP (YOLO-Litchi Pests) based on YOLOv10n was proposed. The model aimed to enhance the detection accuracy of small litchi pest targets in multiple scenarios by optimizing the network structure and loss function, while also reducing the number of parameters and computational costs. [Methods] Two classes of litchi insect pests (Cocoon and Gall) images were collected as datasets for modeling in natural scenarios (sunny, cloudy, post-rain) and laboratory environments. The original data were expanded through random scaling, random panning, random brightness adjustments, random contrast variations, and Gaussian blurring to balance the category samples and enhance the robustness of the model, generating a richer dataset named the CG dataset (Cocoon and Gall dataset). The YOLO-LP model was constructed after the following three improvements. Specifically, the C2f module of the backbone network (Backbone) in YOLOv10n was optimized and the C2f_GLSA module was constructed using the global-to-local spatial aggregation (GLSA) module to focus on small targets and enhance the differentiation between the targets and the backgrounds, while simultaneously reducing the number of parameters and computation. A frequency-aware feature fusion module (FreqFusion) was introduced into the neck network (Neck) of YOLOv10n and a frequency-aware path aggregation network (FreqPANet) was designed to reduce the complexity of the model and address the problem of fuzzy and shifted target boundaries. The SCYLLA-IoU (SIoU) loss function replaced the Complete-IoU (CIoU) loss function from the baseline model to optimize the target localization accuracy and accelerate the convergence of the training process. [Results and Discussions] YOLO-LP achieved 90.9%, 62.2%, and 59.5% for AP50, AP50:95, and AP-Small50:95 in the CG dataset, respectively, and 1.9%, 1.0%, and 1.2% higher than the baseline model. The number of parameters and the computational costs were reduced by 13% and 17%, respectively. These results suggested that YOLO-LP had a high accuracy and lightweight design. Comparison experiments with different attention mechanisms validated the effectiveness of the GLSA module. After the GLSA module was added to the baseline model, AP50, AP50:95, and AP-Small50:95 achieved the highest performance in the CG dataset, reaching 90.4%, 62.0%, and 59.5%, respectively. Experiment results comparing different loss functions showed that the SIoU loss function provided better fitting and convergence speed in the CG dataset. Ablation test results revealed that the validity of each model improvement and the detection performance of any combination of the three improvements was significantly better than the baseline model in the YOLO-LP model. The performance of the models was optimal when all three improvements were applied simultaneously. Compared to several mainstream models, YOLO-LP exhibited the best overall performance, with a model size of only 5.1 MB, 1.97 million parameters (Params), and a computational volume of 5.4 GFLOPs. Compared to the baseline model, the detection of the YOLO-LP performance was significantly improved across four multiple scenarios. In the sunny day scenario, AP50, AP50:95, and AP-Small50:95 increased by 1.9%, 1.0 %, and 2.0 %, respectively. In the cloudy day scenario, AP50, AP50:95, and AP-Small50:95 increased by 2.5%, 1.3%, and 1.3%, respectively. In the post-rain scenario, AP50, AP50:95, and AP-Small50:95 increased by 2.0%, 2.4%, and 2.4%, respectively. In the laboratory scenario, only AP50 increased by 0.7% over the baseline model. These findings indicated that YOLO-LP achieved higher accuracy and robustness in multi-scenario small target detection of litchi pests. [Conclusions] The proposed YOLO-LP model could improve detection accuracy and effectively reduce the number of parameters and computational costs. It performed well in small target detection of litchi pests and demonstrated strong robustness across different scenarios. These improvements made the model more suitable for deployment on resource-constrained mobile and edge devices. The model provided a valuable technical reference for small target detection of litchi pests in various scenarios.

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    Rapid Tea Identification and Polyphenol Detection Method in Fresh Tea Leaves Using Visible/Shortwave and Longwave Near-Infrared Spectroscopy
    XU Jinchai, LI Xiaoli, WENG Haiyong, HE Yong, ZHU Xuesong, LIU Hongfei, HUANG Zhenxiong, YE Dapeng
    Smart Agriculture    2025, 7 (4): 58-70.   DOI: 10.12133/j.smartag.SA202505034
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    [Objective] Tea polyphenols, as a key indicator for evaluating tea quality, possess significant health benefits. Traditional detection methods are limited by poor timeliness, high cost, and destructive sampling, making them difficult to meet the demands of tea cultivar breeding and real-time monitoring of tea quality. Meanwhile, rapid identification of tea cultivars and leaf positions is critical for guiding tea production. Therefore, this study aims to develop a non-destructive detection device for quality components of fresh tea leaves based on the combined technology of visible/short-wave near-infrared and long-wave near-infrared spectroscopy, to realize rapid non-destructive detection of tea polyphenol content and rapid identification of tea cultivars and leaf positions. [Methods] A rapid non-destructive detection device for quality components of fresh tea leaves was developed by combining visible/short-wave near-infrared spectroscopy (400~1 050 nm) and long-wave near-infrared spectroscopy (1 051~1 650 nm). The Savitzky-Golay (SG) convolution smoothing method was used for preprocessing the spectral data. The Folin-Ciocalteu method was employed to determine the tea polyphenol content, and abnormal samples were eliminated using the interquartile range (IQR) method. Data-level and feature-level fusion methods were adopted, with the competitive adaptive reweighted sampling (CARS) algorithm used to extract characteristic wavelengths. Prior to modeling, the Kennard-Stone algorithm was applied to partition the dataset into a training set and a prediction set at a ratio of 4∶1. Models such as principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM), extreme learning machine (ELM), and 1D convolutional neural network (1D-CNN) were constructed for the identification of 3 cultivars (Huangdan, Tieguanyin, and Benshan) and 4 leaf positions. For predicting tea polyphenol content, models including partial least squares regression (PLSR), least squares support vector regression (LS-SVR), ELM, and 1D-CNN were established for predicting the tea polyphenol content in fresh tea leaves. [Results and Discussions] The results showed that there were significant differences in tea polyphenol contents among different cultivars and leaf positions (P<0.05). Specifically, the tea polyphenol content of Huangdan was 17.54%±1.82%, which was 1.16 times and 1.04 times that of Tieguanyin (15.04%±1.22%) and Benshan (16.81%±1.24%), respectively. For each cultivar, the tea polyphenol content generally showed a decreasing trend from the 1st to 4th leaf positions, with the highest content in the 1st leaf position. Principal component analysis (PCA) revealed that for cultivar identification, the scatter distribution of the principal components of Huangdan, Tieguanyin, and Benshan, as well as their projections in the directions of PC1 and PC2, showed a clear trend of clustering into three groups, indicating a good classification effect, although there was still some overlap among individual samples. For leaf position identification, the scatter distributions of the principal components of the 1st, 2nd, 3rd, and 4th leaf positions overlapped with each other, with no obvious clustering among leaf positions. Compared with single-source data, models based on data fusion effectively improved prediction performance. Among them, the PLS-DA model established by combining SG preprocessing with feature-level fusion achieved prediction accuracies of 100% and 87.93% for the identification of 3 tea cultivars and 4 leaf positions, respectively. Furthermore, the 1D-CNN model based on data-level fusion exhibited superior performance in predicting tea polyphenol content, with a coefficient of determination (R2P), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) of 0.802 0, 0.636 8%, and 2.268 4, respectively, which outperformed models using only visible/short-wave near-infrared spectroscopy or long-wave near-infrared spectroscopy. [Conclusions] The developed detection device combining visible/short-wave near-infrared and long-wave near-infrared spectroscopy, mainly composed of spectrometers, Y-type optical fibers, plant probes, polymer lithium batteries, DC uninterruptible power supplies, voltage conversion modules, and aluminum alloy casings, could synchronously collect multi-source spectral data of visible/short-wave near-infrared and long-wave near-infrared from fresh tea leaves. Combined with data fusion methods and machine learning algorithms, it enabled rapid detection of tea polyphenol content and efficient identification of cultivars and leaf positions in fresh tea leaves, providing new insights for the application of multi-source data fusion technology in elite tea cultivar breeding and non-destructive detection of fresh tea leaf quality.

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    Rapid and Non-Destructive Analysis Method of Hawthorn Moisture Content Based on Hyperspectral Imaging Technology
    BAI Ruibin, WANG Hui, WANG Hongpeng, HONG Jiashun, ZHOU Junhui, YANG Jian
    Smart Agriculture    2025, 7 (4): 95-107.   DOI: 10.12133/j.smartag.SA202505033
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    [Objective] This study aimed to develop a rapid and non-destructive method for determining the moisture content of hawthorn fruits using hyperspectral imaging (HSI) integrated with machine learning algorithms. By evaluating the effects of different fruit orientations and spectral ranges, the research provides theoretical insights and technical support for real-time moisture monitoring and intelligent fruit sorting. [Methods] A total of 458 fresh hawthorn samples, representing various regions and cultivars, were collected to ensure diversity and robustness. Hyperspectral images were acquired in two spectral ranges: visible-near-infrared (VNIR, 400~1 000 nm) and short-wave infrared (SWIR, 940~2 500 nm). A threshold segmentation algorithm was used to extract the region of interest (ROI) from each image, and the average reflectance spectrum of the ROI served as the raw input data. To enhance spectral quality and reduce noise, five preprocessing techniques were applied: Savitzky-Golay (SG) smoothing, multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative (FD), and second derivative (SD). Four regression algorithms were then employed to build predictive models: partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and multilayer perceptron (MLP). The models were evaluated under varying fruit orientations (stem-side facing downward, upward, sideways, and a combined set of all three) and spectral ranges (VNIR, SWIR, and VNIR+SWIR). To further reduce the dimensionality of the hyperspectral data and minimize redundancy, four feature selection methods were applied: successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), variable iterative space shrinkage approach (VISSA), and discrete wavelet transform combined with stepwise regression (DWT-SR). The DWT-SR method utilized the Daubechies 6 (db6) wavelet basis function at a decomposition level of 1. [Results and Discussions] Both fruit orientation and spectral range had a significant impact on model performance. The optimal prediction results were achieved when the stem-side of the fruit was facing downward, using the SWIR range (940~2 500 nm) and FD preprocessing. Under these conditions, the SVR model exhibited the highest predictive accuracy, with a coefficient of determination (R2ₚ) of 0.860 5, mean absolute error (MAEₚ) of 0.711 1, root mean square error (RMSEₚ) of 0.914 2, and residual prediction deviation (RPD) of 2.677 6. Further feature reduction using the DWT-SR method resulted in the selection of 17 key wavelengths. Despite the reduced input size, the SVR model based on these features maintained strong predictive capability, achieving R2ₚ = 0.857 1, MAEₚ = 0.669 2, RMSEₚ = 0.925 2, and RPD = 2.645 7. These findings confirm that the DWT-SR method effectively balances dimensionality reduction with model performance. The results demonstrate that the SWIR range contains more moisture-relevant spectral information than the VNIR range, and that first derivative preprocessing significantly improves the correlation between spectral features and moisture content. The SVR model proved particularly well-suited for handling nonlinear relationships in small datasets. Additionally, the DWT-SR method efficiently reduced data dimensionality while preserving key information, making it highly applicable for real-time industrial use. [Conclusions] In conclusion, hyperspectral imaging combined with appropriate preprocessing, feature selection, and machine learning techniques offers a promising and accurate approach for non-destructive moisture determination in hawthorn fruits. This method provides a valuable reference for quality control, moisture monitoring, and automated fruit sorting in the agricultural and food processing industries.

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    Application of Photoacoustic Spectroscopy in Quality Assessment of Agricultural and Forestry Products
    XIE Weijun, CHEN Keying, QIAO Mengmeng, WU Bin, GUO Qing, ZHAO Maocheng
    Smart Agriculture    2025, 7 (4): 18-30.   DOI: 10.12133/j.smartag.SA202505026
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    [Significance] The quality assessment of agricultural and forestry products is a core process in ensuring food safety and enhancing product competitiveness. Traditional detection methods suffer from drawbacks such as sample destruction, expensive equipment, and poor adaptability. As an innovative analytical technique combining optical and acoustic detection principles, photoacoustic spectroscopy technology (PAS) overcomes the limitations of conventional detection techniques that rely on transmitted or reflected optical signals through its unique light-thermal-acoustic energy conversion mechanism. With its non-contact, high-sensitivity, and multi-form adaptability characteristics, PAS has been increasingly applied in the quality assessment of agricultural and forestry products in recent years, providing a new solution for the simultaneous detection of internal and external quality in these products. [Progress] In the specific applications of agricultural and forestry product testing, PAS has demonstrated practical value in multiple aspects. In seed testing, researchers have established quantitative relationship models between photoacoustic signals and seed viability also achieved dynamic assessment of seed health by monitoring respiratory metabolic gases (e.g., CO2 and ethylene). In fruit and vegetable quality analysis, PAS can capture characteristic substance changes during ripening. In the quality control of grain and oil products, Fourier-transform infrared PAS technology has been successfully applied to the rapid detection of protein content in wheat flour and aflatoxin in corn. In food safety monitoring, PAS has achieved breakthrough progress in heavy metal residue detection, pesticide residue analysis, and food authenticity identification. [Conclusions and Prospects] Despite its evident advantages, PAS technology still faces multiple challenges in practical implementation. ​Technically​​, the complex matrix of agricultural and forestry products causes non-uniform generation and propagation of photoacoustic signals, complicating data analysis. And environmental noise interference (e.g., mechanical vibrations, temperature fluctuations) compromises detection stability, while spectral peak overlap in multi-component systems limits quantitative analysis accuracy. ​​Equipment-wise​​, current PAS systems remain bulky and costly, primarily due to reliance on imported core components like high-power lasers and precision lock-in amplifiers, severely hindering widespread adoption. Moreover, the absence of standardized photoacoustic databases and universal analytical models restricts the technology's adaptability across diverse agricultural products. Looking forward, PAS development may focus on these key directions.​Firstly, multi-technology integration by combining with Raman spectroscopy, near-infrared spectroscopy, and other sensing methods to construct multidimensional data spaces for enhanced detection specificity. Moreover, ​​miniaturization​​ through developing chip-based detectors via micro-electromechanical technology, replacing conventional solid-state lasers with vertical-cavity surface-emitting lasers (VCSELs), and adopting 3D printing for integrated photoacoustic cell fabrication to significantly reduce system size and cost. Furthermore, intelligent algorithm innovation with incorporating advanced deep learning models like attention mechanisms and transfer learning to improve interpretation of complex photoacoustic spectra. As these technical bottlenecks are progressively overcome, PAS is poised to establish a quality monitoring network spanning the entire "field-to-market" chain—from ​​harvesting​​ to ​​processing/storage​​ to ​​distribution​​—thereby transforming agricultural quality control from traditional sampling-based methods to ​​intelligent, standardized, full-process monitoring​​. This will provide technical support for ​​food safety assurance​​ and ​​agricultural industry advancement​​.

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    Grain Production Big Data Platform: Progress and Prospects
    YANG Guijun, ZHAO Chunjiang, YANG Xiaodong, YANG Hao, HU Haitang, LONG Huiling, QIU Zhengjun, LI Xian, JIANG Chongya, SUN Liang, CHEN Lei, ZHOU Qingbo, HAO Xingyao, GUO Wei, WANG Pei, GAO Meiling
    Smart Agriculture    2025, 7 (2): 1-12.   DOI: 10.12133/j.smartag.SA202409014
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    [Significance] The explosive development of agricultural big data has accelerated agricultural production into a new era of digitalization and intelligentialize. Agricultural big data is the core element to promote agricultural modernization and the foundation of intelligent agriculture. As a new productive forces, big data enhances the comprehensive intelligent management decision-making during the whole process of grain production. But it faces the problems such as the indistinct management mechanism of grain production big data resources, the lack of the full-chain decision-making algorithm system and big data platform for the whole process and full elements of grain production. [Progress] Grain production big data platform is a comprehensive service platform that uses modern information technologies such as big data, Internet of Things (IoT), remote sensing and cloud computing to provide intelligent decision-making support for the whole process of grain production based on intelligent algorithms for data collection, processing, analysis and monitoring related to grain production. In this paper, the progress and challenges in grain production big data, monitoring and decision-making algorithms are reviewed, as well as big data platforms in China and worldwide. With the development of the IoT and high-resolution multi-modal remote sensing technology, the massive agricultural big data generated by the "Space-Air-Ground" Integrated Agricultural Monitoring System, has laid an important foundation for smart agriculture and promoted the shift of smart agriculture from model-driven to data-driven. However, there are still some issues in field management decision-making, such as the requirements for high spatio-temporal resolution and timeliness of the information are difficult to meet, and the algorithm migration and localization methods based on big data need to be studied. In addition, the agricultural machinery operation and spatio-temporal scheduling algorithm based on remote sensing and IoT monitoring information to determine the appropriate operation time window and operation prescription, needs to be further developed, especially the cross-regional scheduling algorithm of agricultural machinery for summer harvest in China. Aiming to address the issues of non-bi-connected monitoring and decision-making algorithms in grain production, as well as the insufficient integration of agricultural machinery and information perception, a framework for the grain production big data intelligent platform based on digital twins is proposed. The platform leverages multi-source heterogeneous grain production big data and integrates a full-chain suit of standardized algorithms, including data acquisition, information extraction, knowledge map construction, intelligent decision-making, full-chain collaboration of agricultural machinery operations. It covers the typical application scenarios such as irrigation, fertilization, pests and disease management, emergency response to drought and flood disaster, all enabled by digital twins technology. [Conclusions and Prospects] The suggestions and trends for development of grain production big data platform are summarized in three aspects: (1) Creating an open, symbiotic grain production big data platform, with core characteristics such as open interface for crop and environmental sensors, maturity grading and a cloud-native packaging mechanism for core algorithms, highly efficient response to data and decision services; (2) Focusing on the typical application scenarios of grain production, take the exploration of technology integration and bi-directional connectivity as the base, and the intelligent service as the soul of the development path for the big data platform research; (3) The data-algorithm-service self-organizing regulation mechanism, the integration of decision-making information with the intelligent equipment operation, and the standardized, compatible and open service capabilities, can form the new quality productivity to ensure food safety, and green efficiency grain production.

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    Non-Destructive Inspection and Intelligent Grading Method of Fu Brick Tea at Fungal Fermentation Stage Based on Hyperspectral Imaging Technology
    HU Yan, WANG Yujie, ZHANG Xuechen, ZHANG Yiqiang, YU Huahao, SONG Xinbei, YE Sitan, ZHOU Jihong, CHEN Zhenlin, ZONG Weiwei, HE Yong, LI Xiaoli
    Smart Agriculture    2025, 7 (4): 71-83.   DOI: 10.12133/j.smartag.SA202505012
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    [Objective] Fu brick tea is a popular fermented black tea, and its "Jin hua" fermentation process determines the quality, flavor and function of the tea. Therefore, the establishment of a rapid and non-destructive detection method for the fungal fermentation stage is of great significance to improve the quality control and processing efficiency. [Methods] The variation trend of Fu brick tea was analyzed through the acquisition of visible-near-infrared (VIS-NIR) and near-infrared (NIR) hyperspectral images during the fermentation stage, and combined with the key quality indexes such as moisture, free amino acids, tea polyphenols, and tea pigments (including theaflavins, thearubigins, and theabrownines), the variation trend was analyzed. This study combined support vector machine (SVM) and convolutional neural network (CNN) to establish quantitative detection of key quality indicators and qualitative identification of the fungal fermentation stage. To enhance model performance, the squeeze-and-excitation (SE) attention mechanism was incorporated, which strengthens the adaptive weight adjustment of feature channels, resulting in the development of the Spectra-SE-CNN model. Additionally, t-distributed stochastic neighbor embedding (t-SNE) was used for feature dimensionality reduction, aiding in the visualization of feature distributions during the fermentation process. To improve the interpretability of the model, the Grad-CAM technique was employed for CNN and Spectra-SE-CNN visualization, helping to identify the key regions the model focuses on. [Results and Discussions] In the quantitative detection of Fu brick tea quality, the best models were all Spectra-SE-CNN, with R2p of 0.859 5, 0.852 5 and 0.838 3 for moisture, tea pigments and tea polyphenols, respectively, indicating a high correlation and modeling stability. These values suggest that the models were capable of accurately predicting these key quality indicators based on hyperspectral data. However, the R2p for free amino acids was lower (0.670 2), which could be attributed to their relatively minor changes during the fermentation process or a weak spectral response, making it more challenging to detect this component reliably with the current hyperspectral imaging approach. The Spectra-SE-CNN model significantly outperformed traditional CNN models, demonstrating the effectiveness of incorporating the SE attention mechanism. The SE attention mechanism enhanced the model's ability to extract and discriminate important spectral features, thereby improving both classification accuracy and generalization. This indicated that the Spectra-SE-CNN model excels not only in feature extraction but also in enhancing the model's robustness to variations in the fermentation stage. Furthermore, t-SNE revealed a clear separation of the different fungal fermentation stages in the low-dimensional space, with distinct boundaries. This visualization highlighted the model's ability to distinguish between subtle spectral differences during the fermentation process. The heatmap generated by Grad-CAM emphasized key regions, such as the fermentation location and edges, providing valuable insights into the specific features the model deemed important for accurate predictions. This improved the model's transparency and helped validate the spectral features that were most influential in identifying the fermentation stages. [Conclusions] A Spectra-SE-CNN model was proposed in this research, which incorporates the SE attention mechanism into a convolutional neural network to enhance spectral feature learning. This architecture adaptively recalibrates channel-wise feature responses, allowing the model to focus on informative spectral bands and suppress irrelevant signals. As a result, the Spectra-SE-CNN achieved improved classification accuracy and training efficiency compared to CNN models, demonstrating the strong potential of deep learning in hyperspectral spectral feature extraction. The findings validate Hyperspectral imaging technology(HIS) enables rapid, non-destructive, and high-resolution assessment of Fu brick tea during its critical fungal fermentation stage and the feasibility of integrating HSI with intelligent algorithms for real-time monitoring of the Fu brick tea fermentation process. Furthermore, this approach offers a pathway for broader applications of hyperspectral imaging and deep learning in intelligent agricultural product monitoring, quality control, and automation of traditional fermentation processes.

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    Detection of Amylose in Fresh Corn Ears Based on Near-Infrared Spectroscopy
    XUE Zhicheng, ZHANG Yongli, ZHANG Jianxing, CHEN Fei, HUAN Kewei, ZHAO Baishun
    Smart Agriculture    2025, 7 (4): 132-140.   DOI: 10.12133/j.smartag.SA202505030
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    [Objective] Fresh corn is increasingly an important choice in the daily diet of consumers due to its rich nutrition and sweet taste. With the improvement of living standards, people's quality requirements for fresh corn continue to improve, among which amylose content is a key indicator affecting the taste and flavor of corn, at present, the industry mainly uses chemical detection methods to determine amylose content, which is not only time-consuming and laborious, destroys samples, but also difficult to meet the needs of rapid detection in modern agricultural production and food processing. Therefore, the development of an efficient, accurate and non-destructive rapid detection technology for amylose has become a key issue in the field of agricultural product quality control. [Methods] In this study, a non-destructive detection model for amylose content in ears of fresh corn based on near-infrared spectroscopy technology was established. Taking Jinguan 597 fresh corn as the research object, the near-infrared spectroscopic detection system independently built by the laboratory was used to collect diffuse reflectance spectral data in the middle area of the complete corn ear to ensure that the detection process did not damage the integrity of the sample. At the same time, the physical and chemical values of amylose content in samples were determined with reference, and a standard database was established. In the data preprocessing stage, the Mahalanobis Distance method was used to screen the outliers of the original spectral data, and the abnormal samples caused by operating errors or sample defects were eliminated, and finally 90 representative fresh corn samples were retained for modeling analysis. In order to optimize the model performance, the effects of five mainstream spectral pretreatment methods were compared: standard normal variable (SNV) transform to eliminate the influence of optical path difference, multiplicative scatter correction (MSC) to reduce particle scattering interference, SavitZky-Golay smoothing (SGS) to remove random noise, first-order derivative (FD) to enhance spectral characteristic peaks, and detrending (DT) to eliminate baseline drift. Based on the partial least squares regression (PLSR) algorithm, a full-band amylose prediction model was constructed, and the robustness of the model was evaluated by cross-validation. In order to further improve the efficiency of model operation, the characteristic wavelengths with the strongest correlation with amylose content were selected from the whole spectrum by innovatively combining two variable selection methods, competitive adaptive reweighted sampling (CARS) and continuous successive projections algorithm (SPA), and a simplified characteristic band prediction model was established. [Results and Discussions] The results demonstrated that among the various combined models incorporating different preprocessing and feature wavelength selection methods, the "SNV-CARS-PLSR" model, which integrated SNV preprocessing with CARS feature extraction, exhibited superior performance. This model significantly outperformed alternative modeling approaches in predictive capability. The model achieved the following performance metrics: a calibration coefficient of determination (R2C) of 0.826, root mean square error of calibration (RMSEC) of 1.399, prediction coefficient of determination (R2P) of 0.820, root mean square error of prediction (RMSEP) of 1.081, and residual predictive deviation (RPD) of 2.426. Comparative analysis revealed that the "SNV-CARS-PLSR" model showed a 14.0% improvement in R2P compared to the full-band PLSR model with SNV preprocessing alone. This enhancement was primarily attributed to the CARS algorithm's effective identification of key feature wavelengths. Through its adaptive weighting and iterative optimization process, CARS successfully extracted 22 characteristic wavelengths that were strongly correlated with amylose content from the original 157 wavelength points in the full spectrum. This selective extraction process effectively eliminated redundant spectral information and noise interference, thereby significantly improving the model's predictive accuracy. [Conclusions] Combined SNV preprocessing with CARS feature selection, the study successfully established a rapid, non-destructive prediction model for amylose content in fresh maize ears utilizing near-infrared spectroscopy technology. The developed methodology demonstrated significant advantages, including rapid analysis capability and complete non destructiveness of samples. The reseach could provide technical support for rapid, non-destructive detection of amylose in fresh maize ears.

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    Graph Neural Networks for Knowledge Graph Construction: Research Progress, Agricultural Development Potential, and Future Directions
    YUAN Huan, FAN Beilei, YANG Chenxue, LI Xian
    Smart Agriculture    2025, 7 (2): 41-56.   DOI: 10.12133/j.smartag.SA202501007
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    [Significance] Graph neural networks (GNN) have emerged as a powerful tool in the realm of data analysis, particularly in knowledge graph construction. By capitalizing on the interaction and message passing among nodes in a graph, GNN can capture intricate relationships, making them widely applicable in various tasks, including knowledge representation, extraction, fusion, and inference. In the context of agricultural knowledge graph (AKG) development and knowledge service application, however, the agricultural domain presents unique challenges. These challenges encompass data with high multi-source heterogeneity, dynamic spatio-temporal changes in knowledge, complex relationships, and stringent requirements for interpretability. Given its strengths in graph structure data modeling, GNNs hold great promise in addressing these difficulties. For instance, in agricultural data, information from weather sensors, soil monitoring devices, and historical crop yield records varies significantly in format and type, and the ability of GNNs to handle such heterogeneity becomes crucial. [Progress] Firstly, this paper provides a comprehensive overview of the representation methods and fundamental concepts of GNNs was presented. The main structures, basic principles, characteristics, and application directions of five typical GNN models were discussed, including recursive graph neural networks (RGNN), convolutional graph neural networks (CGNN), graph auto-encoder networks (GAE), graph attention networks (GAT), and spatio-temporal graph neural networks(STGNN). Each of these models has distinct advantages in graph feature extraction, which are leveraged for tasks such as dynamic updates, knowledge completion, and complex relationship modeling in knowledge graphs. For example, STGNNs are particularly adept at handling the time-series and spatial data prevalent in agriculture, enabling more accurate prediction of crop growth patterns. Secondly, how GNN utilize graph structure information and message passing mechanisms to address issues in knowledge extraction related to multi-source heterogeneous data fusion and knowledge representation was elucidated. It can enhance the capabilities of entity recognition disambiguation and multi-modal data entity recognition. For example, when dealing with both textual descriptions of agricultural pests and corresponding image data, GNNs can effectively integrate these different modalities to accurately identify the pests. It also addresses the tasks of modeling complex dependencies and long-distance relationships or multi-modal relation extraction, achieving precise extraction of complex, missing information, or multi-modal events. Furthermore, GNNs possess unique characteristics, such as incorporating node or subgraph topology information, learning deep hidden associations between entities and relationships, generating low-dimensional representations encoding structure and semantics, and learning or fusing iterative non-linear neighborhood feature relationships on the graph structure, make it highly suitable for tasks like entity prediction, relation prediction, denoising, and anomaly information inference. These applications significantly enhance the construction quality of knowledge graphs. In an agricultural setting, this means more reliable predictions of disease outbreaks based on the relationships between environmental factors and crop health. Finally, in-depth analyses of typical cases of intelligent applications based on GNNs in agricultural knowledge question answering, recommendation systems, yield prediction, and pest monitoring and early warning are conducted. The potential of GNNs for constructing temporal agricultural knowledge models is explored, and its ability to adapt to the changing nature of agricultural data over time is highlighted. [Conclusions and Prospects] Research on constructing AKGs using GNNs is in its early stages. Future work should focus on key technologies like deep multi-source heterogeneous data fusion, knowledge graph evolution, scenario-based complex reasoning, and improving interpretability and generalization. GNN-based AKGs are expected to take on professional roles such as virtual field doctors and agricultural experts. Applications in pest control and planting decisions will be more precise, and intelligent tools like smart agricultural inputs and encyclopedia retrieval systems will be more comprehensive. By representing and predicting entities and relationships in agriculture, GNN-based AKGs can offer efficient knowledge services and intelligent solutions for sustainable agricultural development.

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    Research Progress on Remote Sensing Monitoring and Intelligent Decision-Making Algorithms for Rice Production
    ZHAO Bingting, HUA Chuanhai, YE Chenyang, XIONG Yuchun, QIAN Tao, CHENG Tao, YAO Xia, ZHENG Hengbiao, ZHU Yan, CAO Weixing, JIANG Chongya
    Smart Agriculture    2025, 7 (2): 57-72.   DOI: 10.12133/j.smartag.SA202501002
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    [Significance] Rice is a staple food crop worldwide, and ccurate monitoring of its growth is crucial for global food security. Remote sensing serves as a powerful tool in modern agriculture. By integrating remote sensing with intelligent decision-making algorithms, farmers can achieve more precise and sustainable rice cultivation. To provide actionable insights and guidance for researchers in this field, this review examines the latest advancements in remote sensing and smart algorithms for rice farming, while addressing current challenges and future trends. [Progress] Currently, remote sensing-based monitoring systems for rice production have been comprehensively implemented across the entire production cycle. For planting distribution identification, optical remote sensing and synthetic aperture radar (SAR) technologies complement each other to enhance accuracy through data fusion. Regarding growth period monitoring, a robust technical framework has been established, incorporating the empirical threshold method, shape model approach, and machine learning classification techniques. Dynamic evaluation of growth status is enabled by constructing correlation models between remote sensing features and biophysical parameters. Disaster monitoring systems provide rapid responses to various natural disasters. Yield and quality predictions integrate crop models, remote sensing data, and machine learning algorithms. Intelligent decision-making algorithms are deeply embedded in all stages of rice production. For instance, during planting planning, the integration of geographic information systems (GIS) and multi-criteria evaluation methods facilitates regional suitability assessments and farm-level quantitative designs. In topdressing management, nitrogen-based intelligent algorithms have significantly improved fertilization precision. Irrigation optimization achieves water conservation and emission reduction by synthesizing soil moisture and meteorological data. Finally, precise pesticide application prescriptions are generated using remote sensing and unmanned aerial vehicle (UAV) technologies. [Conclusions and Prospects] Despite significant progress, current research faces persistent challenges, including difficulties in multi-source data fusion, complexities in acquiring prior knowledge, insufficient model standardization, and barriers to large-scale technology implementation. Future efforts should prioritize the following six directions: (1) Technological innovation: Advance collaborative analysis of multi-source remote sensing data, design optimized data fusion algorithms, and construct an integrated air-space-ground monitoring network; (2) Intelligent algorithms: Explore cutting-edge techniques such as generative adversarial networks (GANs) and federated learning to enhance model adaptability across diverse environments; (3) Research scale: Establish a global rice growth monitoring system and develop multi-factor coupling models to assess climate change impacts; (4) Technology dissemination: Strengthen demonstration projects, reduce equipment costs, and cultivate interdisciplinary professionals; (5) Standards and protocols: Promote internationally unified standards for monitoring and decision - making frameworks; (6) System integration: Leverage technologies such as digital twins and blockchain to develop smart agriculture platforms for end-to-end intelligent management. Through multi-dimensional innovation, these advancements will significantly elevate the intelligence of rice production, offering robust support for global food security and sustainable agricultural development.

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    Advances, Problems and Challenges of Precise Estrus Perception and Intelligent Identification Technology for Cows
    ZHANG Zhiyong, CAO Shanshan, KONG Fantao, LIU Jifang, SUN Wei
    Smart Agriculture    2025, 7 (3): 48-68.   DOI: 10.12133/j.smartag.SA202305005
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    [Significance] Estrus monitoring and identification in cows is a crucial aspect of breeding management in beef and dairy cattle farming. Innovations in precise sensing and intelligent identification methods and technologies for estrus in cows are essential not only for scientific breeding, precise management, and smart breeding on a population level but also play a key supportive role in health management, productivity enhancement, and animal welfare improvement at the individual level. The aims are to provide a reference for scientific management and the study of modern production technologies in the beef and dairy cattle industry, as well as theoretical methodologies for the research and development of key technologies in precision livestock farming. [Progress] Based on describing the typical characteristics of normal and abnormal estrus in cows, this paper systematically categorizes and summarizes the recent research progress, development trends, and methodological approaches in estrus monitoring and identification technologies, focusing on the monitoring and diagnosis of key physiological signs and behavioral characteristics during the estrus period. Firstly, the paper outlines the digital monitoring technologies for three critical physiological parameters, body temperature, rumination, and activity levels, and their applications in cow estrus monitoring and identification. It analyzes the intrinsic reasons for performance bottlenecks in estrus monitoring models based on body temperature, compares the reliability issues faced by activity-based estrus monitoring, and addresses the difficulties in balancing model generalization and robustness design. Secondly, the paper examines the estrus sensing and identification technologies based on three typical behaviors: feeding, vocalization, and sexual desire. It highlights the latest applications of new artificial intelligence technologies, such as computer vision and deep learning, in estrus monitoring and points out the critical role of these technologies in improving the accuracy and timeliness of monitoring. Finally, the paper focuses on multi-factor fusion technologies for estrus perception and identification, summarizing how different researchers combine various physiological and behavioral parameters using diverse monitoring devices and algorithms to enhance accuracy in estrus monitoring. It emphasizes that multi-factor fusion methods can improve detection rates and the precision of identification results, being more reliable and applicable than single-factor methods. The importance and potential of multi-modal information fusion in enhancing monitoring accuracy and adaptability are underlined. The current shortcomings of multi-factor information fusion methods are analyzed, such as the potential impact on animal welfare from parameter acquisition methods, the singularity of model algorithms used for representing multi-factor information fusion, and inadequacies in research on multi-factor feature extraction models and estrus identification decision algorithms. [Conclusions and Prospects] From the perspectives of system practicality, stability, environmental adaptability, cost-effectiveness, and ease of operation, several key issues are discussed that need to be addressed in the further research of precise sensing and intelligent identification technologies for cow estrus within the context of high-quality development in digital livestock farming. These include improving monitoring accuracy under weak estrus conditions, overcoming technical challenges of audio extraction and voiceprint construction amidst complex background noise, enhancing the adaptability of computer vision monitoring technologies, and establishing comprehensive monitoring and identification models through multi-modal information fusion. It specifically discusses the numerous challenges posed by these issues to current technological research and explains that future research needs to focus not only on improving the timeliness and accuracy of monitoring technologies but also on balancing system cost-effectiveness and ease of use to achieve a transition from the concept of smart farming to its practical implementation.

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    Artificial Intelligence for Agricultural Science (AI4AS): Key Elements, Challenges and Pathways
    ZHAO Ruixue, YANG Xiao, ZHANG Dandan, LI Jiao, HUANG Yongwen, XIAN Guojian, KOU Yuantao, SUN Tan
    Smart Agriculture    2025, 7 (3): 35-47.   DOI: 10.12133/j.smartag.SA202502019
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    [Significance] Artificial intelligence for science (AI4S), as an emerging paradigm that deeply integrates artificial intelligence(AI) with scientific research, has triggered profound transformations in research methodologies. By accelerating scientific discovery through AI technologies, it is driving a shift in scientific research from traditional approaches reliant on experience and intuition to methodologies co-driven by data and AI. This transition has spurred innovative breakthroughs across numerous scientific domains and presents new opportunities for the transformation of agricultural research. With its powerful capabilities in data processing, intelligent analysis, and pattern recognition, AI can transcend the cognitive limitations of researchers in the field and is gradually emerging as an indispensable tool in modern agricultural scientific research, injecting new impetus into the intelligent, efficient, and collaborative development of agricultural scientific research. [Progress] This paper systematically reviews the current advancements in AI4S and its implications for agricultural research. It reveals that AI4S has triggered a global race among countries around the world vying for the commanding heights of a new round of scientific and technological strategies. Developed nations in Europe and America, for instance, have laid out the frontier areas in AI4S and rolled out relevant policies. Meanwhile, some top universities and research institutions are accelerating related research, and tech giants are actively cultivating related industries to advance the application and deployment of AI technologies in scientific research. In recent years, AI4S has achieved remarkable development, showing great potential across multiple disciplines and finding widespread application in data mining, model construction, and result prediction. In the field of agricultural scientific research, AI4S has played an important role in accelerating multi-disciplinary integration, promoting the improvement of the scientific research efficiency, facilitating the breakthrough of complex problems, driving the transformation of the scientific research paradigm, and upgrading scientific research infrastructure. The continuous progress of information technology and synthetic biology has made the interdisciplinary integration of agriculture and multiple disciplines increasingly closer. The deep integration of AI and agricultural scientific research not only improves the application level of AI in the agricultural field but also drives the transformation of traditional agricultural scientific research models towards intelligence, data-driven, and collaborative directions, providing new possibilities for agricultural scientific and technological innovation. The new agricultural digital infrastructure is characterized by intelligent data collection, edge computing power deployment, high-throughput network transmission, and distributed storage architecture, aiming to break through the bottlenecks of traditional agricultural scientific research facilities in terms of real-time performance, collaboration, and scalability. Taking emerging disciplines such as Agrinformatics and climate-focused Agriculture-Forestry-AI (AgFoAI) as examples, they focus on using AI technology to analyze agricultural data, construct crop growth models, and climate change models, etc., to promote the development and innovation of agricultural scientific research. [Conclusions and Prospects] With its robust capabilities in data processing, intelligent analysis, and pattern recognition, AI is increasingly becoming an indispensable tool in modern agricultural scientific research. To address emerging demands, core domains, and research processes in agricultural research, the concept of agricultural intelligent research is proposed, characterized by human-machine collaboration and interdisciplinary integration. This paradigm employs advanced data analytics, pattern recognition, and predictive modeling to perform in-depth mining and precise interpretation of multidimensional, full-lifecycle, large-scale agricultural datasets. By comprehensively unraveling the intrinsic complexities and latent patterns of research subjects, it autonomously generates novel, scientifically grounded, and high-value research insights, thereby driving agricultural research toward greater intelligence, precision, and efficiency. The framework's core components encompass big science infrastructure (supporting large-scale collaborative research), big data resources (integrating heterogeneous agricultural datasets), advanced AI model algorithms (enabling complex simulations and predictions), and collaborative platforms (facilitating cross-disciplinary and cross-institutional synergy). Finally, in response to challenges related to data resources, model capabilities, research ecosystems, and talent development, actionable pathways and concrete recommendations are outlined from the perspectives of top-level strategic planning, critical technical ecosystems, collaborative innovation ecosystems, disciplinary system construction, and interdisciplinary talent cultivation, aiming to establish a new AI4S-oriented agricultural research framework.

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    Knowledge Graph Driven Grain Big Data Applications: Overview and Perspective
    YANG Chenxue, LI Xian, ZHOU Qingbo
    Smart Agriculture    2025, 7 (2): 26-40.   DOI: 10.12133/j.smartag.SA202501004
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    [Significance] Grain production spans multiple stages and involves numerous heterogeneous factors, including agronomic inputs, natural resources, environmental conditions, and socio-economic variables. However, the associated data generated throughout the entire production process, ranging from cultivation planning to harvest evaluation, remains highly fragmented, unstructured, and semantically diverse. This complexity data, combined with the lack of integrated core algorithms to support decision-making, has severely limited the potential of big data to drive innovation in grain production. Knowledge graph technology, by offering structured and semantically-rich representations of complex data, enables the integration of multi-source and heterogeneous data, enhances semantic mining and reasoning capabilities, and provides intelligent, knowledge-driven support for sustainable grain production, thereby addressing these challenges effectively. [Progress] This paper systematically reviewed the current research and application progress of knowledge graphs in the grain production big data. A comprehensive knowledge graph driven framework was proposed based on a hybrid paradigm combining data-driven modeling and domain knowledge guidance to support the entire grain production lifecycle and addressed three primary dimensions of data complexity: Structural diversity, relational heterogeneity, and semantic ambiguity. The key techniques of constructing multimodal knowledge map and temporal reasoning for grain production were described. First, an agricultural ontology system for grain production was designed, incorporating domain-specific concepts, hierarchical relationships, and attribute constraints. This ontology provided the semantic foundation for knowledge modeling and alignment. Second, multimodal named entity recognition (NER) techniques were employed to extract entities such as crops, varieties, weather conditions, operations, and equipment from structured and unstructured data sources, including satellite imagery, agronomic reports, Internet of Things sensor data, and historical statistics. Advanced deep learning models, such as bidirectional encoder representations from transformers (BERT) and vision-language transformers, were used to enhance recognition accuracy across text and image modalities. Third, the system implemented multimodal entity linking and disambiguation, which connected identical or semantically similar entities across different data sources by leveraging graph embeddings, semantic similarity measures, and rule-based matching. Finally, temporal reasoning modules were constructed using temporal knowledge graphs and logical rules to support dynamic inference over time-sensitive knowledge, such as crop growth stages, climate variations, and policy interventions. The proposed knowledge graph driven system enabled the development of intelligent applications across multiple stages of grain production. In the pre-production stage, knowledge graphs supported decision-making in resource allocation, crop variety selection, and planting schedule optimization based on past data patterns and predictive inference. During the in-production stage, the system facilitated precision operations, such as real-time fertilization and irrigation by reasoning over current field status, real-time sensor inputs, and historical trends. In the post-production stage, it enabled yield assessment and economic evaluation through integration of production outcomes, environmental factors, and policy constraints. [Conclusions and Prospects] Knowledge graph technologies offer a scalable and semantically-enhanced approach for unlocking the full potential of grain production big data. By integrating heterogeneous data sources, representing domain knowledge explicitly, and supporting intelligent reasoning, knowledge graphs can provide visualization, explainability, and decision support across various spatial scales, including national, provincial, county-level, and large-scale farm contexts. These technologies are of great scientific and practical significance in supporting China's national food security strategy and advancing the goals of storing grain in the land and storing grain in technology. Future directions include the construction of cross-domain agricultural knowledge fusion systems, dynamic ontology evolution mechanisms, and federated knowledge graph platforms for multi-region data collaboration under data privacy constraints.

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    Embodied Intelligent Agricultural Robots: Key Technologies, Application Analysis, Challenges and Prospects
    WEI Peigang, CAO Shanshan, LIU Jifang, LIU Zhenhu, SUN Wei, KONG Fantao
    Smart Agriculture    2025, 7 (4): 141-158.   DOI: 10.12133/j.smartag.SA202505008
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    [Significance] Most current agricultural robots lack the ability to adapt to complex agricultural environments and still have limitations when facing variable, uncertain and unstructured agricultural scenarios. With the acceleration of agricultural intelligent transformation, embodied intelligence, as an intelligent system integrating environment perception, information cognition, autonomous decision-making and action, is giving agricultural robots stronger autonomous perception and complex environment adaptation ability, and becoming an important direction to promote the development of agricultural intelligent robots. In this paper, the technical system and application practice of embodied intelligence are sorted out systematically in the field of agricultural robots, its important value is revealed in improving environmental adaptability, decision-making autonomy and operational flexibility, and theoretical and practical references are provided to promote the development of agricultural robots to a higher level. [Progress] Firstly, the key supporting technologies of embodied intelligent agricultural robots are systematically sorted out, focusing on four aspects, namely, multimodal fusion perception, intelligent autonomous decision-making, autonomous action control and feedback autonomous learning. In terms of multimodal fusion perception, the modular artificial intelligence (AI) algorithm architecture and multimodal large model architecture are summarised. In terms of intelligent autonomous decision-making, two types of approaches based on artificial programming and dedicated task algorithms, and on large-scale pre-trained models are outlined. In terms of autonomous action control, three types of approaches based on the fusion of reinforcement learning and mainstream transformer, large model-assisted reinforcement learning, end-to-end mapping of semantics to action and action end-to-end mapping are summarised. In the area of feedback autonomous learning, the focus is on the related technological advances in the evolution of large model-driven feedback modules. Secondly, it analysed the typical application scenarios of embodied intelligence in agriculture, constructed a technical framework with "embodied perception - embodied cognition - embodied execution - embodied evolution" as the core, and discussed the implementation paths of each module according to the agricultural scenarios. The paths of each module are classified and discussed. Finally, the key technical bottlenecks and application challenges are analysed in depth, mainly including the high complexity of system integration, the significant gap between real and virtual data, and the limited ability of cross-scene generalisation. [Conclusions and Prospects] The future development trend of embodied intelligent agricultural robots is summarised and prospected from the construction of high-quality datasets and simulation platforms, the application of domain large model fusion, and the design of layered collaborative architectures, etc. It mainly focuses on the following aspects. Firstly, the construction of high-quality agricultural scenarios of embodied intelligence datasets is a key prerequisite to realise the embodied intelligence landing in agriculture. The development of embodied intelligent agricultural robots needs to rely on rich and accurate agricultural scene task datasets and highly realistic simulators to support physical interaction and behavioural learning. Secondly, the fusion of basic big model and agricultural domain model is the accelerator of intelligent perception and decision-making of agricultural robots. The in-depth fusion of general basic models in agricultural scenarios will bring stronger perception, understanding and reasoning capabilities to the embodied-intelligent agricultural robots. Thirdly, the "big model high-level planning + small model bottom-level control" architecture is an effective solution to balance intelligence and efficiency. Although large models have advantages in semantic understanding and global strategy planning, their reasoning latency and arithmetic demand can hardly meet the real-time and low-power requirements of agricultural robots. The use of large models for high-level task decomposition, scene semantic parsing and decision making, coupled with lightweight small models or traditional control algorithms to complete the underlying sensory response and motion control, can achieve the complementary advantages of the two.

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    Lightweight Cattle Facial Recognition Method Based on Improved YOLOv11
    HAN Yu, QI Kangkang, ZHENG Jiye, LI Jinai, JIANG Fugui, ZHANG Xianglun, YOU Wei, ZHANG Xia
    Smart Agriculture    2025, 7 (3): 173-184.   DOI: 10.12133/j.smartag.SA202502010
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    [Objective] Beef cattle breeding constitutes a pivotal component of modern animal husbandry, where accurate individual identification serves as the cornerstone for advancing automated technologies, including intelligent weight measurement, body condition scoring, body conformation assessment, and behavior monitoring. However, practical breeding environments are riddled with challenges: soiled conditions, cluttered backgrounds, and constant animal movement collectively result in high variability in cattle facial data features. Furthermore, inconsistent lighting and diverse shooting angles often obscure key features, increasing the risk of misjudgment during detection. To tackle these issues, this study introduces an improved model, YOLO-PCW, which enhances detection performance while maintaining a lightweight structure, effectively addressing the complexities of precise cattle face recognition in challenging breeding settings. [Methods] The research leveraged the Cow Fusion dataset (CFD), a comprehensive collection of real-world cattle face images captured under variable lighting conditions, from multiple angles, and against complex backgrounds, for model training and validation. Concurrently, a Custom Cow Monitor Dataset (CMD) was created from video footage obtained through the a breeding farm's monitoring system, providing a robust basis for evaluating the model's generalization capabilities. The YOLOv11 architecture served as the foundational framework for implementing the following performance improvements. The partial convolution (PConv) was seamlessly integrated into the C3K2 module within the YOLOv11 head network. Utilizing the sparse convolutional properties of PConv on the feature maps, the convolutional structure was meticulously optimized, reducing computational redundancy and memory access while preserving the model's accuracy, rendering it highly suitable for real-time applications. Additionally, the convolutional block attention module (CBAM) was incorporated to enhance feature map processing through adaptive channel-wise and spatial attentions. This refinement enabled precise extraction of target regions by mitigating background interference, allowing the model to focus on critical anatomical features such as the eyes, mouth, and nose. Furthermore, the weighted intersection over union (WIoU) loss function was adopted to replace the CIoU, optimizing the weighted strategy for bounding box regression errors. This innovation reduced the adverse effects of large or outlier gradients in extreme samples, enabling the model to prioritize average-quality samples for refinement. The resulting improvement in key region localization accuracy bolstered the model's generalization capability and overall performance, establishing a state-of-the-art cattle face recognition framework. [Results and Discussion] The YOLO-PCW model achieved a remarkable accuracy rate (P) of 96.4%, recall rate (R) of 96.7%, and mean average precision (mAP) of 98.7%. With 2.3 million parameters and a computational load of 5.6 GFLOPs, it not only improved accuracy, recall, and mAP by 3.6, 5, and 4.4 percentage point respectively, but also achieved a significant reduction in floating-point computational load and parameter size, down to 88.9% and 88.5% of the original model's, respectively. Ablation studies revealed that the CBAM module enhanced precision from 92.8% to 95.2%. The WIoU loss function optimized target positioning accuracy, achieving a precision of 93.8%. The PConv module substantially reduced computational load from 6.3 GFLOPs to 5.5 GFLOPs, thereby lightening the model's computational burden significantly. The synergistic collaboration of these components provided robust support for enhancing the performance of the cattle face recognition model. Comparative experiments demonstrated that under identical conditions, the YOLO-PCW model outperformed algorithms such as Faster-RCNN, SSD, YOLOv5, YOLOv7-tiny, and YOLOv8 under identical conditions, exhibiting the most outstanding performance. It effectively balanced recognition accuracy with computational efficiency, achieving optimal utilization of computational resources. [Conclusions] The improved YOLO-PCW model, featuring a lightweight architecture and optimized attention mechanism, could successfully improve detection accuracy while simplifies deployment. It can achieve precise cattle face recognition in real-world breeding environments, providing an efficient and practical solution for individual identification in applications such as animal welfare breeding, intelligent ranch management, smart ranch construction, and animal health monitoring.

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    Applications Research Progress and Prospects of Multi-Agent Large Language Models in Agricultural
    ZHAO Yingping, LIANG Jinming, CHEN Beizhang, DENG Xiaoling, ZHANG Yi, XIONG Zheng, PAN Ming, MENG Xiangbao
    Smart Agriculture    2025, 7 (5): 37-51.   DOI: 10.12133/j.smartag.SA202503026
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    [Significance] With the rapid advancement of large language models (LLM) and multi-agent systems, their integration, multi-agent large language models, is emerging as a transformative force in modern agriculture. Agricultural production involves complex, sequential, and highly environment-dependent processes, including tillage, planting, management, and harvesting. Traditional intelligent systems often struggle with the diversity, uncertainty, and coordination of these stages' demand. Multi-agent LLMs offer a new paradigm for agricultural intelligence by combining deep semantic understanding with distributed collaboration and adaptive coordination. Through role specialization, real-time perception, and cooperative decision-making, they can decompose complex workflows, adapt to changing conditions, and enable robust, full-process automation, making them well-suited to the challenges of modern agriculture. More importantly, their application marks a critical step toward the digital transformation, precision management, and sustainable development of agriculture. By enabling intelligent decision-making across the entire agricultural lifecycle, they provide both theoretical foundations and practical tools for building next-generation smart and unmanned farming systems. [Progress] The core concepts of multi-agent LLMs are first elucidated, covering the composition and characteristics of multi-agent systems as well as the development and training pipelines of LLMs. Then, the overall architecture of multi-agent systems is presented, encompassing both the environments in which agents operate and their internal structures. The collaborative patterns of multi-agent LLMs are then examined in terms of coordination structures and temporal organization. Following this, interaction mechanisms are discussed from multiple dimensions, including interactions between agents and the external environment, inter-agent communication, communication protocol frameworks, and communication security. To demonstrate the varying task specializations of different multi-agent frameworks, a comparative benchmark survey table is provided by synthesizing benchmark tasks and results reported in existing studies. The results show that different multi-agent large language model architectures tend to perform better on specific types of tasks, reflecting the influence of agent framework design characteristics such as role assignment strategies, communication protocols, and decision-making mechanisms. Furthermore, several representative architectures of multi-agent LLMs, as proposed in existing studies, are briefly reviewed. Based on their design features, their potential applicability to agricultural scenarios is discussed. Finally, current research progress and practical applications of LLMs, multimodal large models, and multi-agent LLMs in the agricultural domain are surveyed. The application architecture of agricultural LLMs is summarized, using rice cultivation as a representative scenario to illustrate the collaborative process of a multi-agent system powered by LLMs. This process involves data acquisition agents, data processing agents, task allocation and coordination agents, task execution agents, and feedback and optimization agents. The roles and functions of each kind of agent in enabling automated and intelligent operations throughout the entire agricultural lifecycle, including tillage, planting, management, and harvesting, are comprehensively described. In addition, drawing on existing research on multimodal data processing, the pseudocode is provided to illustrate the basic logic of the data processing agents. [Conclusions and Prospects] Multi-agent LLMs technology holds vast promise in agriculture but still confronts several challenges. First, limited model interpretability, stemming from opaque internal reasoning and high-dimensional parameter mappings, hinders decision transparency, traceability, user trust, and debugging efficiency. Second, model hallucination is significant, probabilistic generation may deviate from facts, leading to erroneous environmental perception and decisions that cause resource waste or crop damage. Third, multi-modal agricultural data acquisition and processing remain complex due to non-uniform equipment standards, heterogeneous data, and insufficient cross-modal reasoning, complicating data fusion and decision-making. Future directions include: (1) enhancing interpretability via chain-of-thought techniques to improve reasoning transparency and traceability; (2) reducing hallucinations by integrating knowledge bases, retrieval-augmented generation, and verification mechanisms to bolster decision reliability; and (3) standardizing data formats to strengthen cross-modal fusion and reasoning. These measures will improve system stability and efficiency, providing solid support for the advancement of smart agriculture.

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    Chilli-YOLO: An Intelligent Maturity Detection Algorithm for Field-Grown Chilli Based on Improved YOLOv10
    SI Chaoguo, LIU Mengchen, WU Huarui, MIAO Yisheng, ZHAO Chunjiang
    Smart Agriculture    2025, 7 (2): 160-171.   DOI: 10.12133/j.smartag.SA202411002
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    [Objective] In modern agriculture, the rapid and accurate detection of chillies at different maturity stages is a critical step for determining the optimal harvesting time and achieving intelligent sorting of field-grown chillies. However, existing target detection models face challenges in efficiency and accuracy when applied to the task of detecting chilli maturity, which limit their widespread use and effectiveness in practical applications. To address these challenges, a new algorithm, Chilli-YOLO, was proposed for achieving efficient and precise detection of chilli maturity in complex environments. [Methods] A comprehensive image dataset was collected, capturing chillis under diverse and realistic agricultural conditions, including varying lighting conditions, camera angles, and background complexities. These images were then meticulously categorized into four distinct maturity stages: Immature, transitional, mature, and dried. Data augmentation techniques were employed to expand the dataset and enhance the model's generalization capabilities. To develop an accurate and efficient chili maturity detection system, the YOLOv10s object detection network was chosen as the foundational architecture. The model's performance was further enhanced through strategic optimizations targeting the backbone network. Specifically, standard convolutional layers were replaced with Ghost convolutions. This technique generated more feature maps from fewer parameters, resulting in significant computational savings and improved processing speed without compromising feature extraction quality. Additionally, the C2f module was substituted with the more computationally efficient GhostConv module, further reducing redundancy and enhancing the model's overall efficiency. To improve the model's ability to discern subtle visual cues indicative of maturity, particularly in challenging scenarios involving occlusion, uneven lighting, or complex backgrounds, the partial self-attention (PSA) module within YOLOv10s was replaced with the second-order channel attention (SOCA) mechanism. SOCA leverages higher-order feature correlations to more effectively capture fine-grained characteristics of the chillis. This enabled the model to focus on relevant feature channels and effectively identify subtle maturity-related features, even when faced with significant visual noise and interference. Finally, to refine the precision of target localization and minimize bounding box errors, the extended intersection over union (XIoU) loss function was integrated into the model training process. XIoU enhances the traditional IoU loss by considering factors such as the aspect ratio difference and the normalized distance between the predicted and ground truth bounding boxes. By optimizing for these factors, the model achieved significantly improved localization accuracy, resulting in a more precise delineation of chillis in the images and contributing to the overall enhancement of the detection performance. The combined implementation of these improvements aimed to construct an effective approach to correctly classify the maturity level of chillis within the challenging and complex environment of a real-world farm. [Results and Discussion] The experimental results on the custom-built chilli maturity detection dataset showed that the Chilli-YOLO model performed excellently across multiple evaluation metrics. The model achieved an accuracy of 90.7%, a recall rate of 82.4%, and a mean average precision (mAP) of 88.9%. Additionally, the model's computational load, parameter count, model size, and inference time were 18.3 GFLOPs, 6.37 M, 12.6 M, and 7.3 ms, respectively. Compared to the baseline model, Chilli-YOLO improved accuracy by 2.6 percent point, recall by 2.8 percent point and mAP by 2.8 percent point. At the same time, the model's computational load decreased by 6.2 GFLOPs, the parameter count decreased by 1.67 M, model size reduced by 3.9 M. These results indicated that Chilli-YOLO strikes a good balance between accuracy and efficiency, making it capable of fast and precise detection of chilli maturity in complex agricultural environments. Moreover, compared to earlier versions of the YOLO model, Chilli-YOLO showed improvements in accuracy of 2.7, 4.8, and 5 percent point over YOLOv5s, YOLOv8n, and YOLOv9s, respectively. Recall rates were higher by 1.1, 0.3, and 2.3 percent point, and mAP increased by 1.2, 1.7, and 2.3 percent point, respectively. In terms of parameter count, model size, and inference time, Chilli-YOLO outperformed YOLOv5. This avoided the issue of YOLOv8n's lower accuracy, which was unable to meet the precise detection needs of complex outdoor environments. When compared to the traditional two-stage network Faster RCNN, Chilli-YOLO showed significant improvements across all evaluation metrics. Additionally, compared to the one-stage network SSD, Chilli-YOLO achieved substantial gains in accuracy, recall, and mAP, with increases of 16.6%, 12.1%, and 16.8%, respectively. Chilli-YOLO also demonstrated remarkable improvements in memory usage, model size, and inference time. These results highlighted the superior overall performance of the Chilli-YOLO model in terms of both memory consumption and detection accuracy, confirming its advantages for chilli maturity detection. [Conclusions] The proposed Chilli-YOLO model optimizes the network structure and loss functions, not only can significantly improve detection accuracy but also effectively reduce computational overhead, making it better suites for resource-constrained agricultural production environments. The research provides a reliable technical reference for intelligent harvesting of chillies in agricultural production environments, especially in resource-constrained settings.

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    Extracting Method of the Cultivation Aera of Rice Based on Sentinel-1/2 and Google Earth Engine (GEE): A Case Study of the Hangjiahu Plain
    E Hailin, ZHOU Decheng, LI Kun
    Smart Agriculture    2025, 7 (2): 81-94.   DOI: 10.12133/j.smartag.SA202502003
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    [Objective] Accurate monitoring of rice planting areas is vital for ensuring national food security, evaluating greenhouse gas emissions, optimizing water resource allocation, and maintaining agricultural ecosystems. In recent years, the integration of remote sensing technologies—particularly the fusion of optical and synthetic aperture radar (SAR) data—has significantly enhanced the capacity to monitor crop distribution, even under challenging weather conditions. However, many current studies still rely heavily on phenological features captured at specific key stages, such as the transplanting phase, while overlooking the complete temporal dynamics of vegetation and water-related indices throughout the entire rice growth cycle. There is an urgent need for a method that fully leverages the time-series characteristics of remote sensing indices to enable accurate, scalable, and timely rice mapping. [Methods] Focusing on the Hangjiahu Plain, a typical rice-growing region in eastern China, a novel approach—dynamic NDVI-SDWI Fusion method for rice mapping (DNSF-Rice) was proposed in this research to accurately extract rice planting areas by synergistically integrating Sentinel-1 SAR and Sentinel-2 optical imagery on the google earth engine (GEE) platform. The methodological framework included the following three steps: First, using Sentinel-2 imagery, a time series of the normalized difference vegetation index (NDVI) was constructed. By analyzing its temporal dynamics across key rice growth stages, potential rice planting areas were identified through a threshold-based classification method; Second, a time series of the Sentinel-1 dual-polarized water index (SDWI) was generated to analyze its dynamic changes throughout the rice growth cycle. A thresholding algorithm was then applied to extract rice field distribution based on microwave data, considering the significant irrigation involved in rice cultivation; Finally, the spatial intersection of the NDVI-derived and SDWI-derived results was intersected to generate the final rice planting map. This step ensures that only pixels exhibiting both vegetation growth and irrigation signals were classified as rice. The classification datasets spanned five consecutive years from 2019 to 2023, with a spatial resolution of 10 m. [Results and Discussions] The proposed method demonstrated high accuracy and robust performance in mapping rice planting areas. Over the study period, the method achieved an overall accuracy of over 96% and an F1-Score exceeding 0.96, outperforming several benchmark products in terms of spatial consistency and precision. The integration of NDVI and SDWI time-series features enabled effective identification of rice fields, even under the challenging conditions of frequent cloud cover and variable precipitation typical in the study area. Interannual analysis revealed a consistent increase in rice planting areas across the Hangjiahu Plain from 2019 to 2023. The remote sensing-based rice area estimates were in strong agreement with official agricultural statistics, further validating the reliability of the proposed method. The fusion of optical and SAR data proved to be a valuable strategy, effectively compensating for the limitations inherent in single-source imagery, especially during the cloudy and rainy seasons when optical imagery alone was often insufficient. Furthermore, the use of GEE facilitated the rapid processing of large-scale time-series data, supporting the operational scalability required for regional rice monitoring. This study emphasized the critical importance of capturing the full temporal dynamics of both vegetation and water signals throughout the entire rice growth cycle, rather than relying solely on fixed phenological stages. [Conclusions] By leveraging the complementary advantages of optical and SAR imagery and utilizing the complete time-series behavior of NDVI and SDWI indices, the proposed approach successfully mapped rice planting areas across a complex monsoon climate region over a five-year period. The method has been proven to be stable, reproducible, and adaptable for large-scale agricultural monitoring applications.

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    Extraction Method of Maize Plant Skeleton and Phenotypic Parameters Based on Improved YOLOv11-Pose
    NIU Ziang, QIU Zhengjun
    Smart Agriculture    2025, 7 (2): 95-105.   DOI: 10.12133/j.smartag.SA202501001
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    [Objective] Accurate extraction of maize plant skeletons and phenotypic parameters is fundamental for acquisition of plant growth data, morphological analysis, and agricultural management. However, leaf occlusion and complex backgrounds in dense planting environments pose significant challenges to skeleton and parameters extraction. A maize plant skeleton and phenotypic parameters extraction method suitable for dense field environments was proposed in this research to enhance the extraction precision and efficiency, and provide technical support for maize growth data acquisition. [Methods] An improved YOLOv11-Pose multi-object keypoint detection network was introduced, a top-down detection framework was adopted to detect maize plant keypoints and reconstruct skeletons. A uniform sampling algorithm was used to design a keypoint representation method tailored for maize skeletons and optimize task adaptability. Additionally, a single-head self-attention mechanism and a convolutional block attention module were incorporated to guide the model's focus on occluded regions and connected parts, thereby improve its adaptability to complex scenarios. [Results and Discussion] In dense field maize environments, experimental results showed that when the number of uniformly sampled keypoints was set to 10, the Fréchet distance reached its minimum value of 79.008, effectively preserving the original skeleton's morphological features while avoiding the negative impact of redundant points. Under this configuration, the improved YOLOv11-Pose model achieved a bounding box detection precision of 0.717. The keypoint detection mAP50 and mAP50-95 improved by 10.9% and 23.8%, respectively, compared to the original model, with an inference time of 52.7 ms per image. The results demonstrated the model's superior performance and low computational cost in complex field environments, particularly in keypoint detection tasks with enhanced accuracy and robustness. The study further combined the results of skeleton extraction and spatial geometric information to achieve a plant height measurement mean average error (MAE) of 2.435 cm, the detection error of leaf age was less than one growth period, and the measurement error of leaf length was 3.482%, verifying the effectiveness and practicability of the proposed method in the application of phenotypic parameter measurement. [Conclusion] The proposed improved YOLOv11-Pose model can efficiently and accurately extract maize plant skeletons, meeting the demands of ground-based maize growth data acquisition. The research could provide technical support for phenotypic data acquisition in grain production and precision agricultural management.

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    Agricultural Big Data Governance: Key Technologies, Applications Analysis and Future Directions
    GUO Wei, WU Huarui, ZHU Huaji, WANG Feifei
    Smart Agriculture    2025, 7 (3): 17-34.   DOI: 10.12133/j.smartag.SA202503020
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    [Significance] To provide a reference for advancing high-quality agricultural production driven by data, this paper focuses on the issues of inconsistent acquisition standards, incomplete data collection, and ambiguous governance mechanisms in China's agricultural production data, examines existing governance models for agricultural production big data, and clarifies the technical pathways for realizing the value of data elements through the integrated and innovative application of key big data governance technologies and tools in practical scenarios. [Progress] From the perspective of agricultural production big data governance, this paper explores 17 types of big data governance technologies and tools across six core processes: Data acquisition and processing, data storage and exchange, data management, data analysis, large models, and data security guarantee. It conducts in-depth research on the application methods of big data governance technologies in agricultural production, revealing that: Remote sensing, unmanned aerial vehicle(UAV), Internet of Things (IoT), and terminal data acquisition and processing systems are already reatively mature; data storage and exchange system are developing rapidly, data management technologies remain in the initial stage; data analysis technologies have been widely applied; large model technology systems have taken initially shape; and data security assurance systems are gradually being into parctice. The above technologies are effectively applied in scenarios through tools and middleware such as data matching, computing power matching, network adaptation, model matching, scenario matching, and business configuration. This paper also analyzes the data governance throughout the entire agricultural production chain, including pre-production, in-production, and post-production, stages, as well as service cases involving different types of agricultural parks, research institutes and universities, production entities, and farmers. It demonstrates that sound data governance can provide sufficient planning and input analysis prior to production, helping planting entities in making rational plans. In production, it can provide data-driven guidance for key scenarios such as agricultural machinery operations and agricultural technical services, thereby fully supporting decision-making in the production process; and based on massive data, it can achieve reliable results in yield assessment and production benefit evaluation. Additionally, the paper introduces governance experience from national-level industrial parks, provincial-level agricultural science and technology parks, and some single-product entities, and investigates domestic and international technologies, practices, and tools related to agricultural production big data governance, indicating that there is a need to break through the business chains and service model of agricultural production across regions, themes, and scenarios. [Conclusions and Prospects] This paper presents insights into the future development directions of agricultural production big data governance, encompassing the promotion of standard formulation and implementation for agricultural production big data governance, the establishment of a universal resource pool for such governance, the expansion of diversified application scenarios, adaptation to the new paradigm of large-model- and massive-data-driven agricultural production big data governance, and the enhancement of security and privacy protection for agricultural production big data.

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    Defogging Remote Sensing Images Method Based on a Hybrid Attention-Based Generative Adversarial Network
    MA Liu, MAO Kebiao, GUO Zhonghua
    Smart Agriculture    2025, 7 (2): 172-182.   DOI: 10.12133/j.smartag.SA202410011
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    [Objective] Remote sensing images have become an important data source in fields such as surface observation, environmental monitoring, and natural disaster prediction. However, the acquisition of remote sensing images is often affected by weather phenomena such as fog and clouds, which reduces the image quality and poses challenges to subsequent analysis and processing tasks. In recent years, the introduction of attention mechanisms has enabled models to better capture and utilize important features in images, thereby significantly improving defogging performance. However, traditional channel attention mechanisms usually rely on global average pooling to summarize feature information. Although this method simplifies the complexity of calculations, it is not satisfactory when dealing with images with significant local changes and sensitivity to outliers. In addition, remote sensing images usually cover a wide area, and the diverse terrain makes the fog pattern more complex. Therefore, to address this issue, a hybrid attention-based generative adversarial network hybrid attention-based generative adversarial network (HAB-GAN) was proposed in this research, which integrates an efficient channel attention (ECA) module and a spatial attention block (SAB). [Method] By merging feature extraction from both channel and spatial dimensions, the model effectively enhanced its ability to identify and recover hazy areas in remote sensing images. In HAB-GAN, the ECA module captured local cross-channel interactions, addressing the shortcomings of traditional global averaged pooling in terms of insufficient sensitivity to local detail information. The ECA module used a global average pooling strategy without dimensionality reduction, automatically adapting to the characteristics of each channel without introducing extra parameters, thereby enhancing the inter-channel dependencies. ECA emploied a one-dimensional convolution operation, which used a learnable kernel size to adaptively determine the range of channel interactions. This design effectively avoided the over-smoothing of global features common in traditional pooling layers, allowing the model to more precisely extract local detailed while maintaining low computational complexity. The SAB module introduced a weighted mechanism on the spatial dimension by constructing a spatial attention map to enhance the model's ability to identify hazy areas in the image. This module extracted feature maps through convolution operations and applies attention weighting in both horizontal and vertical directions, highlighting regions with severe haze, allowing the model to better capture spatial information in the image, thereby enhancing dehazing performance. The generator of HAB-GAN combined residual network structures with hybrid attention modules. It first extracted initial features from input images through convolutional layers and then passed these features through several residual blocks. The residual blocks effectively mitigated the vanishing gradient problem in deep neural networks and maintain feature consistency and continuity by passing input features directly to deeper network layers through skip connections. Each residual block incorporated ECA and SAB modules, enabling precise feature learning through weighted processing in both channel and spatial dimensions. After extracting effective features, the generator generated dehazed images through convolution operations. The discriminator adopted a standard convolutional neural network architecture, focusing on extracting local detail features from the images generated by the generator. It consisted of multiple convolutional layers, batch normalization layers, and Leaky ReLU activation functions. By extracting local features layer by layer and down-sampling, the discriminator progressively reduced the spatial resolution of the images, evaluating their realism at both global and local levels. The generator and discriminator were jointly optimized through adversarial training, where the generator aimed to produce increasingly realistic dehazed images, and the discriminator continually improved its ability to distinguish between real and generated images, thereby enhancing the learning effectiveness and image quality of the generator. [Results and Discussions] To validate the effectiveness of HAB-GAN, experiments were conducted on the remote sensing image scene classification 45 (RESISC45) dataset. The experimental results demonstrated that compared to existing dehazing models, HAB-GAN excels in key evaluation metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Specifically, compared to SpA GAN, HAB-GAN improved PSNR by 2.642 5 dB and SSIM by 0.012 2; Compared to HyA-GAN, PSNR improved by 1.138 dB and SSIM by 0.001 9. Additionally, to assess the generalization capability of HAB-GAN, further experiments were conducted on the RICE2 dataset to verify its performance in cloud removal tasks. The results showed that HAB-GAN also performs exceptionally well in cloud removal tasks, with PSNR improving by 3.593 2 dB and SSIM improving by 0.040 2. Compared to HyA-GAN, PSNR and SSIM increased by 1.854 dB and 0.012 4, respectively. To further explored the impact of different modules on the model's performance, ablation experiments were designed, gradually removing the ECA module, the SAB module, and the entire hybrid attention module. The experimental results showed that removing the ECA module reduced PSNR by 2.642 5 dB and SSIM by 0.012 2; Removing the SAB module reduced PSNR by 2.955 dB and SSIM by 0.008 7, and removing the entire hybrid attention module reduced PSNR and SSIM by 3.866 1 dB and 0.033 4, respectively. [Conclusions] The proposed HAB-GAN model not only performs excellently in dehazing and beclouding tasks but also significantly enhances the clarity and detail recovery of dehazed images through the synergistic effect of the ECA module and the SAB module. Additionally, its strong performance across different remote sensing datasets further validates its effectiveness and generalization ability, showcasing broad application potential particularly in fields such as agriculture, environmental monitoring, and disaster prediction, where high-quality remote sensing data is crucial. HAB-GAN is poised to become a valuable tool for improving data reliability and supporting more accurate decision-making and analysis.

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    Frontiers and Future Trends in Data Sensing Technologies for Opto-Intelligent Agriculture: From Optical Sensors to Intelligent Decision Systems
    CHEN Chengcheng, WU Jiaping, YU Helong
    Smart Agriculture    2025, 7 (5): 1-16.   DOI: 10.12133/j.smartag.SA202507049
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    [Significance] Opto-intelligent agriculture represents an emerging paradigm that deeply integrates optical sensing and intelligent decision-making within agricultural systems, aiming to transform production from experience-based management to data-driven precision cultivation. The core of this paradigm lies in exploiting the dual role of light: As an information carrier, it enables non-destructive sensing of crop physiological states through hyperspectral imaging, fluorescence, and other optical sensors; As a regulatory factor, it allows feedback-based manipulation of the light environment to precisely regulate crop growth. This establishes a closed-loop framework of "perception-decision-execution", which substantially enhances water and fertilizer use efficiency, enables early warning of pests and diseases, and supports quality-oriented production. Nevertheless, the transition of this technology from laboratory research to large-scale field application remains challenged by unstable signals under complex environments, weak model generalization, high equipment costs, a shortage of interdisciplinary talent, and insufficient policy support and promotion mechanisms. This paper systematically reviews the technological architecture, practical achievements, and intrinsic limitations of opto-intelligent agriculture, with the objective of providing theoretical guidance and practical directions for future development. [Progress] Opto-intelligent agriculture is evolving from isolated technological advances toward full-chain integration, characterized by significant progress in optical sensing, intelligent decision-making, and precision execution. At the optical sensing level, technological approaches have expanded from traditional spectral imaging to multi-scale, synergistic sensing networks. Hyperspectral imaging captures subtle spectral variations during the early stages of crop stress, chlorophyll fluorescence imaging enables ultra-early diagnosis of both biotic and abiotic stresses, LiDAR provides accurate three-dimensional phenotypic data, and emerging quantum-dot sensors have enhanced detection sensitivity down to the molecular scale. In terms of intelligent decision-making, recent advances focus on the deep integration of mechanistic and data-driven models, which compensates for the limited adaptability of purely mechanistic models while improving the interpretability of purely data-based ones. Through multi-source data fusion, the system jointly analyzes optical, environmental, and soil parameters to generate globally optimal strategies that balance yield, quality, and resource efficiency. At the execution stage, systems have developed into real-time feedback control loops. Dynamic light-spectrum LED systems and intelligent variable-spray drones transform decision outputs into precise actions, while continuous monitoring enables adaptive self-optimization. This mature technological chain has delivered measurable outcomes across the agricultural value chain, integrated solutions demonstrate even greater potential. Collectively, the achievements signify the transition of opto-intelligent agriculture from conceptual exploration to practical implementation. [Conclusions and Prospects] By synergizing optical perception with intelligent decision-making, opto-intelligent agriculture is driving a fundamental transformation in agricultural production. To achieve the transition from merely usable to genuinely effective, a comprehensive advancement framework integrating technology, equipment, talent, and policy must be established. Technologically, efforts should focus on enhancing sensing stability under open-field conditions, developing lightweight and interpretable models, and promoting the domestic development of core components. From a talent perspective, interdisciplinary education and agricultural technology training must be strengthened. From a policy standpoint, improving subsidy mechanisms, digital infrastructure, and innovation-oriented dissemination systems will be essential. Looking forward, through integration with emerging technologies such as 6G communication and digital twin systems, opto-intelligent agriculture is poised to become a cornerstone for ensuring both food security and ecological sustainability.

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    Accurate Detection of Tree Planting Locations in Inner Mongolia for The Three North Project Based on YOLOv10-MHSA
    XIE Jiyuan, ZHANG Dongyan, NIU Zhen, CHENG Tao, YUAN Feng, LIU Yaling
    Smart Agriculture    2025, 7 (3): 108-119.   DOI: 10.12133/j.smartag.SA202410010
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    [Objective] The traditional manual field investigation method of the tree planting locations is not only inefficient but also error-prone, and the low-altitude unmanned aerial vehicle (UAV) has become the best choice to solve these problems. To solve the problem of accuracy and efficiency in the detection of tree planting locations (tree pits) in Inner Mongolia of China's Three North Project, an accurate recognition and detection model of tree planting locations based on YOLOv10-MHSA was proposed. [Methods] A long-endurance, multi-purpose vertical take-off and landing (VTOL) fixed-wing UAV was used to collect images of tree planting locations. Equipped with a 26-megapixel camera with high spatial resolution, the UAV was well-suited for high-precision field mapping. Aerial photography was conducted between 11:00 and 12:00 on August 1, 2024. Flight parameters were set as follows: Altitude of 150 m (yielding a ground resolution of approximately 2.56 cm), course overlap rate of 75%, side overlap rate of 65%, and flight speed of 20 m/s. To prevent overfitting during network training, the original data set was enhanced. To improve the quality and efficiency of model training, different attention mechanisms and optimizing loss functions were introduced. Specifically, a more effective EIOU loss function was introduced, comprising three components: IOU loss, distance loss, and azimuth loss. This function directly minimizes the width and height discrepancies between the target frame and anchor, leading to faster convergence and more accurate positioning. Additionally, the Focal-EIOU loss function was adopted to address sample imbalance in bounding box regression tasks, further improving the model's convergence speed and positioning precision. [Results and Discussions] After the introduction of the multi-head self-attention mechanism (MHSA), the model achieved improvements of 1.4% and 1.7% in the evaluation metrics AP@0.5 and AP@0.5:0.95, respectively, and the accuracy and recall rate were also improved. This indicates that MHSA effectively aids the model in extracting the feature information of the target and improving the detection accuracy in complex background. Although the processing speed of the model decreases slightly after adding the attention mechanism, it could still meet the requirements of real-time detection. The experiment compared four loss functions: CIOU, SIOU, EIOU and Focal-EIOU. The results showed that the Focal-EIOU loss function yielded significant increases in precision and recall. This demonstrated that the Focal-EIOU loss function could accelerate the convergence speed of the model and improve the positioning accuracy when dealing with the sample imbalance problem in small target detection. Finally, an improved model, YOLOv10-MHSA, was proposed, incorporating MHSA attention mechanism, small target detection layer and Focal-EIOU loss function. The results of ablation experiments showed that AP@0.5 and AP@0.5:0.95 were increased by 2.2% and 0.9%, respectively, after adding only small target detection layer on the basis of YOLOv10n, and the accuracy and recall rate were also significantly improved. When the MHSA and Focal-EIOU loss functions were further added, the model detection effect was significantly improved. Compared with the baseline model YOLOv10n, the AP@0.5, AP@0.5:0.95, P-value and R-value were improved by 6.6%, 10.0%, 4.1% and 5.1%, respectively. Although the FPS was reduced, the detection performance of the improved model was significantly better than that of the original model in various complex scenes, especially for small target detection in densely distributed and occluded scenes. [Conclusions] By introducing MHSA and the optimized loss function (Focal-EIOU) into YOLOv10n model, the research significantly improved the accuracy and efficiency of tree planting location detection in the Three North Project in Inner Mongolia. The experimental results show that MHSA can enhance the ability of the model to extract local and global information of the target in complex background, and effectively reduce the phenomenon of missed detection and false detection. The Focal-EIOU loss function accelerates the convergence speed of the model and improves the positioning accuracy by optimizing the sample imbalance problem in the bounding box regression task. Although the model processing speed has decreased, the method proposed still meets the real-time detection requirements, provides strong technical support for the scientific afforestation of the Three North Project.

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    Vegetable Price Prediction Based on Optimized Neural Network Time Series Models
    HOU Ying, SUN Tan, CUI Yunpeng, WANG Xiaodong, ZHAO Anping, WANG Ting, WANG Zengfei, YANG Weijia, GU Gang, WU Shaodong
    Smart Agriculture    2025, 7 (5): 78-87.   DOI: 10.12133/j.smartag.SA202410037
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    [Objective] The price volatility of vegetables has profound implications for both farmers and consumers. Fluctuating prices directly impact farmers' earnings and pose challenges to market stability and consumer purchasing behaviors. These fluctuations are driven by a multitude of complex and interrelated factors, including supply and demand, seasonal cycles, climatic conditions, logistical efficiency, government policies, consumer preferences, and suppliers' trading strategies. As a result, vegetable prices tend to exhibit nonlinear and non-stationary patterns, which significantly complicate efforts to produce accurate price forecasts. Addressing these forecasting challenges holds considerable practical and theoretical value, as improved prediction models can support more stable agricultural markets, secure farmers' incomes, reduce cost-of-living volatility for consumers, and inform more precise and effective government regulatory strategies. [Methods] The study investigated the application of neural network-based time series forecasting models for the prediction of vegetable prices. In particular, a selection of state-of-the-art neural network architectures was evaluated for their effectiveness in modeling the complex dynamics of vegetable pricing. The selected models for the research included PatchTST and iTransformer, both of which were built upon the Transformer architecture, as well as SOFTS and TiDE, which leveraged multi-layer perceptron (MLP) structures. In addition, Time-LLM, a model based on a large language model architecture, was incorporated to assess its adaptability to temporal data characterized by irregularity and noise. To enhance the predictive performance and robustness of these models, an automatic hyperparameter optimization algorithm was employed. This algorithm systematically adjusted key hyperparameters such as learning rate, batch size, early stopping, and random seed. It utilized probabilistic modeling techniques to construct performance-informed distributions for guiding the selection of more effective hyperparameter configurations. Through iterative updates informed by prior evaluation data, the optimization algorithm increased the search efficiency in high-dimensional parameter spaces, while simultaneously minimizing computational costs. The training and validation process allocated 80% of the data to the training set and 20% to the validation set, and employed the mean absolute error (MAE) as the primary loss function. In addition to the neural network models, the study incorporated a traditional statistical model, the autoregressive integrated moving average (ARIMA), as a baseline model for performance comparison. The predictive accuracy of all models was assessed using three widely recognized error metrics: MAE, mean absolute percentage error (MAPE), and mean squared error (MSE). The model that achieved the most favorable performance across these metrics was selected for final vegetable price forecasting. [Results and Discussions] The experimental design of the study focused on four high-demand, commonly consumed vegetables: carrots, white radishes, eggplants, and iceberg lettuce. Both daily and weekly price forecasting tasks were conducted for each type of vegetable. The empirical results demonstrated that the neural network-based time series models provided strong fitting capabilities and produced accurate forecasts for vegetable prices. The integration of automatic hyperparameter tuning significantly improved the performance of these models. In particular, after tuning, the MSE for daily price prediction decreased by at least 76.3% for carrots, 94.7% for white radishes, and 74.8% for eggplants. Similarly, for weekly price predictions, the MSE reductions were at least 85.6%, 93.6%, and 64.0%, respectively, for the same three vegetables. These findings confirm the substantial contribution of the hyperparameter optimization process to enhancing model effectiveness. Further analysis revealed that neural network models performed better on vegetables with relatively stable price trends, indicating that the underlying consistency in data patterns benefited predictive modeling. On the other hand, Time-LLM exhibited stronger performance in weekly price forecasts involving more erratic and volatile price movements. Its robustness in handling time series data with high degrees of randomness suggests that model architecture selection should be closely aligned with the specific characteristics of the target data. Ultimately, the study identified the best-performing model for each vegetable and each prediction frequency. The results demonstrated the generalizability of the proposed approach, as well as its effectiveness across diverse datasets. By aligning model architecture with data attributes and integrating targeted hyperparameter optimization, the research achieved reliable and accurate forecasts. [Conclusions] The study verified the utility of neural network-based time series models for forecasting vegetable prices. The integration of automatic hyperparameter optimization techniques notably improved predictive accuracy, thereby enhancing the practical utility of these models in real-world agricultural settings. The findings provide technical support for intelligent agricultural price forecasting and serve as a methodological reference for predicting prices of other agricultural commodities. Future research may further improve model performance by integrating multi-source heterogeneous data. In addition, the application potential of more advanced deep learning models can be further explored in the field of price prediction.

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    Agricultural Drought Monitoring in Arid Irrigated Areas Based on TVDI Combined with ICEEMDAN-ARIMA Model
    WEI Yuxin, LI Qiao, TAO Hongfei, LU Chunlei, LUO Xu, MAHEMUJIANG Aihemaiti, JIANG Youwei
    Smart Agriculture    2025, 7 (2): 117-131.   DOI: 10.12133/j.smartag.SA202502005
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    [Objective] Drought, one of the most frequent natural disasters globally, is characterized by its extensive impact area, prolonged duration, and significant harm. Large scale irrigation areas, as important pillars of China's agricultural economy, often have their benefits severely restricted by drought disasters. Therefore, quickly and accurately grasping the regional drought situation is of great significance. It can not only effectively improve the utilization efficiency of water resources and reduce agricultural production losses but also promote the sustainable development of regional agriculture. [Methods] The Santun river irrigation area in Xinjiang, an arid - zone irrigation area, was taken as an research object. Based on Landsat TM/ETM+/OLI_TIRS series data, the temperature vegetation drought index (TVDI) and the vegetation temperature condition index (VTCI) were calculated. Using in situ the soil water content of the 0-10 cm soil layer in the study area measured by the Smart Soil Moisture Monitor, an applicability analysis of the drought monitoring effects of TVDI and VTCI was carried out to select the remote sensing monitoring index suitable for drought research in the study area. Based on the selected drought monitoring index, methods such as linear trend analysis and Theil - Sen + Mann - Kendall trend test were used to explore the temporal and spatial distribution characteristics and change trends of drought in the study area from 2005 to 2022. Meanwhile, with the help of machine learning algorithms, an ICEEMDAN - ARIMA combined model was constructed to predict the drought situation in the study area in spring, summer, and autumn of 2023. The prediction performance of the ICEEMDAN - ARIMA combined model was evaluated using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). [Results and Discussions] The research results show that there were varying degrees of linear correlations between the two drought indices, TVDI and VTCI, inverted from remote sensing data, and the soil water content of the 0-10 cm surface soil layer in the Santun river irrigation area of Xinjiang. The coefficient of determination between TVDI and the measured soil water content was greater than 0.51 in all periods, with an overall fitting coefficient of 0.57, and the slopes of the fitting equations were all negative, indicating a significant negative correlation. In contrast, the highest coefficient of determination of VTCI was only 0.33, and its overall monitoring effect was significantly weaker than that of TVDI. In terms of temporal and spatial distribution, the drought situation in the study area showed a slow-increasing trend from 2005 to 2022. The growth rate of TVDI was 0.01/10 a, and it had strong spatial heterogeneity, specifically manifested as the spatial distribution characteristic that the southern and southwestern regions of the irrigation area were drier than the northern and northeastern regions. The results of the drought trend analysis indicated that from 2005 to 2022, the distribution of Sen change rate data in the study area conforms to the normal distribution (P < 0.01), and the Sen slopes of more than 72.83% of the regions were greater than zero. At the same time, according to the classification criteria of the Sen + Mann - Kendall trend test, six types of drought change trends were divided. The area proportions of the extremely significant mitigation, significant mitigation, slight mitigation, extremely significant drying, significant drying, and slight drying categories were 0.73%, 1.78%, 24.31%, 5.33%, 9.43%, and 58.42%, respectively. The area proportions of the slight drying and slight mitigation categories were the largest, accounting for a total of 82.73% of the total area of the study area. The ICEEMDAN - ARIMA combined model constructed with the help of machine learning algorithms achieved good results in predicting the drought situation in the study area in 2023. The average value of R2 reached 0.962, demonstrating high robustness and good prediction performance. [Conclusions] The research results systematically characterizes the characteristics of agricultural drought changes in the Santun river irrigation area of Xinjiang over a long - time series, and reveals that the ICEEMDAN - ARIMA combined model has good prediction accuracy in agricultural drought prediction research. This study can provide important references for the construction of drought early warning and forecasting systems, water resource management, and the sustainable development of agriculture in arid-zone irrigation areas.

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    Analysis of the Spatial Temporal Evolution Pattern and Influencing Factors of Grain Production in Sichuan Province of China
    ZHENG Ling, MA Qianran, JIANG Tao, LIU Xiaojing, MOU Jiahui, WANG Canhui, LAN Yu
    Smart Agriculture    2025, 7 (2): 13-25.   DOI: 10.12133/j.smartag.SA202411013
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    [Objective] Sichuan province, recognized as a strategic core region for China's food security, exhibits spatiotemporal dynamics in grain production that have significant implications for regional resource allocation and national food strategies. Previous studies have primarily focused on spatial dimension of grain production, treating temporal and spatial characteristics separately. This approach neglected the non-stationary effects of time and failed to integrate the evolution patterns of time and space in a coherent manner. Consequently, the interrelationship between the temporal evolution and spatial distribution of grain production was not fully elucidated. A spatiotemporal integrated analysis framework along with a method for extracting spatiotemporal features was proposed in this study. The objective is to elucidate the spatial pattern temporal variations in Sichuan province, thereby providing a scientific basis for regional food security management. [Methods] The study was based on county-level panel data of Sichuan province spanning the years 2000 to 2019. Multiple spatiotemporal analysis techniques were employed to comprehensively examine the evolution of grain production and to identify its driving mechanisms. Initially, standard deviation ellipse analysis and the centroid migration trajectory model were applied to assess the spatial distribution of major grain-producing areas and their temporal migration trends. This analysis enabled the identification of spatial agglomeration patterns and the direction of change in grain production. Subsequently, a three-dimensional spatiotemporal framework was constructed based on the space-time cube model. This framework integrated both temporal and spatial information. Hotspot analysis and the local Moran's I statistic were then utilized to systematically identify the distribution of cold and hot spots as well as spatial clustering patterns in county-level grain output. This approach revealed the spatiotemporal hotspots, clustering characteristics, and the evolving trends of grain production over time. Finally, a spatiotemporal geographically weighted regression model was employed to quantitatively assess the influence of various factors on grain production. These factors included natural elements (such as topography, climate, and soil properties), agricultural factors (such as the total sown area, mechanization level, and irrigation conditions), economic factors (such as per capita gross domestic product and rural per capita disposable income), and human factors (such as rural population and nighttime light intensity). The analysis elucidated the spatial heterogeneity and evolution of the principal driving forces affecting grain production in the province. [Results and Discussions] A high-yield core area was established on the eastern Sichuan plain, with its spatial distribution exhibiting a pronounced northeast-southwest orientation. The production centroid consistently remained near Lezhi County, although it experienced significant shifts during the periods 2000-2001 and 2009-2010. In contrast, the grain production levels in the western Sichuan plateau and the central hilly regions were relatively low. Over the past two decades, the province demonstrated seven distinct patterns in the distribution of cold and hot spots and three clustering patterns in grain production. Specifically, grain output on the Chengdu Plain continuously increased, the decline in production on the western plateau decelerated, and production in the central region consistently decreased. Approximately 64.77% of the province exhibited potential for increased production, particularly in the western region, where improvements in natural conditions and the gradual enhancement of agricultural infrastructure contributed to significant yield growth potential. Conversely, roughly 16.93% of the areas, characterized by complex topography and limited resources, faced potential yield reductions due to resource scarcity and restrictive cultivation conditions. The analysis further revealed that agricultural factors served as the dominant determinants influencing the spatiotemporal characteristics of grain production. In this regard, the total sown area and the area of cultivated land acted as positive contributors. Natural factors, including slope, soil pH, and annual sunshine duration, exerted negative effects. Although human and economic factors had relatively minor influences, indicators such as population density and nighttime light intensity also played a moderating role in regional grain production. The maintenance of agricultural land area proved crucial in safeguarding and enhancing grain yields, while improvements in natural resource conditions further bolstered production capacity. These findings underscored the inherent spatiotemporal disparities in grain production within Sichuan province and revealed the impact of agricultural resource allocation, environmental conditions, and policy support on the heterogeneity of spatial production changes. [Conclusions] The proposed spatiotemporal integrated analysis framework provided a novel perspective for elucidating the dynamic evolution and driving mechanisms of grain production in Sichuan province. The findings demonstrated that the grain production pattern exhibited complex characteristics, including regional concentration, dynamic spatiotemporal evolution, and the interplay of multiple factors. Based on these results, future policies should emphasize the construction of high-standard farmland, the promotion of precision agriculture technologies, and the rational adjustment of agricultural resource allocation. Such measures are intended to enhance agricultural production efficiency and to improve the regional eco-agricultural system. Ultimately, these recommendations aim to furnish both theoretical support and practical guidance for the establishment of a stable and efficient grain production system and for advancing the development of Sichuan province as a key granary.

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    Improvement of HLM Modeling for Winter Wheat Yield Estimation Under Drought Conditions
    ZHAO Peiqin, LIU Changbin, ZHENG Jie, MENG Yang, MEI Xin, TAO Ting, ZHAO Qian, MEI Guangyuan, YANG Xiaodong
    Smart Agriculture    2025, 7 (2): 106-116.   DOI: 10.12133/j.smartag.SA202408009
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    [Objective] Winter wheat yield is crucial for national food security and the standard of living of the population. Existing crop yield prediction models often show low accuracy under disaster-prone climatic conditions. This study proposed an improved hierarchical linear model (IHLM) based on a drought weather index reduction rate, aiming to enhance the accuracy of crop yield estimation under drought conditions. [Methods] HLM was constructed using the maximum enhanced vegetation index-2 (EVI2max), meteorological data (precipitation, radiation, and temperature from March to May), and observed winter wheat yield data from 160 agricultural survey stations in Shandong province (2018-2021). To validate the model's accuracy, 70% of the data from Shandong province was randomly selected for model construction, and the remaining data was used to validate the accuracy of the yield model. HLM considered the variation in meteorological factors as a key obstacle affecting crop growth and improved the model by calculating the relative meteorological factors. The calculation of relative meteorological factors helped reduce the impact of inter-annual differences in meteorological data. The accuracy of the HLM model was compared with that of the random forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) models. The HLM model provided more intuitive interpretation, especially suitable for processing hierarchical data, which helped capture the variability of winter wheat yield data under drought conditions. Therefore, a drought weather index reduction rate model from the agricultural insurance industry was introduced to further optimize the HLM model, resulting in the construction of the IHLM model. The IHLM model was designed to improve crop yield prediction accuracy under drought conditions. Since the precipitation differences between Henan and Shandong provinces were small, to test the transferability of the IHLM model, Henan province sample data was processed in the same way as in Shandong, and the IHLM model was applied to Henan province to evaluate its performance under different geographical conditions. [Results and Discussions] The accuracy of the HLM model, improved based on relative meteorological factors (rMF), was higher than that of RF, SVR, and XGBoost. The validation accuracy showed a Pearson correlation coefficient (r) of 0.76, a root mean squared error (RMSE) of 0.60 t/hm2, and a normalized RMSE (nRMSE) of 11.21%. In the drought conditions dataset, the model was further improved by incorporating the relationship between the winter wheat drought weather index and the reduction rate of winter wheat yield. After the improvement, the RMSE decreased by 0.48 t/hm2, and the nRMSE decreased by 28.64 percentage points, significantly enhancing the accuracy of the IHLM model under drought conditions. The IHLM model also demonstrated good applicability when transferred to Henan province. [Conclusions] The IHLM model developed in this study improved the accuracy and stability of crop yield predictions, especially under drought conditions. Compared to RF, SVR, and XGBoost models, the IHLM model was more suitable for predicting winter wheat yield. This research can be widely applied in the agricultural insurance field, playing a significant role in the design of agricultural insurance products, rate setting, and risk management. It enables more accurate predictions of winter wheat yield under drought conditions, with results that are closer to actual outcomes.

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    Bi-Intentional Modeling and Knowledge Graph Diffusion for Rice Variety Selection and Breeding Recommendation
    QIAO Lei, CHEN Lei, YUAN Yuan
    Smart Agriculture    2025, 7 (2): 73-80.   DOI: 10.12133/j.smartag.SA202412025
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    [Objective] Selection of rice varieties requires consideration of several factors, such as yield, fertility, disease resistance and resistance to downfall. In order to meet the user's rice variety selection needs, help users quickly access to the rice varieties they need, improve efficiency, and further promote the informatization and intelligence of rice breeding work, the bi-intentional modeling and knowledge graph diffusion model, an advanced method was proposed. [Methods] The research work was mainly carried out at two levels: Data and methodology. At the data level, considering the current lack of relevant data support for rice variety selection and breeding recommendation, a certain amount of recommendation dataset was constructed. The rice variety selection recommendation dataset consisted of two parts: Interaction data and knowledge graph. For the interaction data, the rice varieties that had been planted in the region were collected on a region-by-region basis, and then a batch of users was simulated and generated from the region. The corresponding rice varieties were assigned to the generated users according to the random sampling method to construct the user-item interaction data. For the knowledge graph, detailed text descriptions of rice varieties were first collected, and then information was extracted from them to construct data in ternary format from multiple varietal characteristics, such as selection unit, varietal category, disease resistance, and cold tolerance. At the methodological level, a model of bi-intentional modeling and knowledge graph diffusion (BMKGD) was proposed. The intent factor in the interaction behavior and the denoising process of the knowledge graph were both taken into account by the BMKGD model. Intentions were usually considered from two perspectives: individual independence and conformity. A dual intent space was chosen to be built by the model to represent both perspectives. For the problem of noisy data in the knowledge graph, denoising was carried out by combining the idea of the diffusion model. Random noise was introduced to destroy the original structure when the knowledge graph was initialized, and the original structure was restored through iterative learning. The denoising was completed in this process. After that, cross-view contrastive learning was carried out in both views. [Results and Discussions] The method proposed achieved optimal performance in the rice variety selection dataset, with recall and normalized discounted cumulative gain (NDCG) values improved by 2.9% and 3.7% compared to the suboptimal model. The performance improvement validated the effectiveness of the method to some extent, indicating that the BMKGD model was more suitable for rice variety recommendation. The Recall value of the BMKGD model on the rice variety selection dataset was 0.327 6, meeting the basic requirements of the recommendation system. The analysis revealed that the collaborative signals in the interaction data played a major role, while the quality of the constructed knowledge graph still had some room for improvement. The module variants with key components removed all exhibited a decrease in performance compared to the original model, which validated the effectiveness of the modules. The performance degradation of the model variants with each component removed varied, indicating that different components played different roles. The performance drop of the model variant with the cross-view contrastive learning module removed was small, indicating that there was some room for improvement in the module to fully utilize the collaborative relationship between the two views. Conclusions The BMKGD model proposed in this paper achieves good performance on the rice variety selection dataset and accomplishes the recommendation task well. It shows that the model can be used to support the rice variety selection and breeding work and help users to select suitable rice varieties.

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    Remote Sensing for Rice Growth Stages Monitoring: Research Progress, Bottleneck Problems and Technical Optimization Paths
    LI Ruijie, WANG Aidong, WU Huaxing, LI Ziqiu, FENG Xiangqian, HONG Weiyuan, TANG Xuejun, QIN Jinhua, WANG Danying, CHU Guang, ZHANG Yunbo, CHEN Song
    Smart Agriculture    2025, 7 (3): 89-107.   DOI: 10.12133/j.smartag.SA202412019
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    [Significance] ​The efficient and precise identification of rice growth stages through remote sensing technology holds critical significance for varietal breeding optimization and production management enhancement. Remote sensing, characterized by high spatial-temporal resolution and automated monitoring capabilities, provides transformative solutions for large-scale dynamic phenology monitoring, offering essential technical support to address climate change impacts and food security challenges in complex agroecosystems where precise monitoring of growth stage transitions enables yield prediction and stress-resilient cultivation management.​ [Progress] In recent years, the technical system for monitoring rice growth stages has achieved systematic breakthroughs in the perception layer, decision-making layer, and execution layer, forming a technological ecosystem covering the entire chain of "data acquisition-feature analysis-intelligent decision-making-precise operation". At the perception layer, a "space-air-ground" three-dimensional monitoring network has been constructed: High-altitude satellites (Sentinel-2, Landsat) realize regional-scale phenological dynamic tracking through wide-spectrum multi-temporal observations; low-altitude unmanned aerial vehicle (UAV) equipped with hyperspectral and light detection and ranging (LiDAR) sensors analyze the heterogeneity of canopy three-dimensional structure; near-ground sensor networks real-timely capture leaf-scale photosynthetic efficiency and nitrogen metabolism parameters. Radiometric calibration and temporal interpolation algorithms eliminate the spatio-temporal heterogeneity of multi-source data, forming continuous and stable monitoring capabilities. Innovations in technical methods show three integration trends: Firstly, multimodal data collaboration mechanisms break through the physical characteristic barriers between optical and radar data; secondly, deep integration of mechanistic models and data-driven approaches embeds the scattering by arbitrarily inclined leaves by arbitrary inclined leaves (PROSPECT + SAIL, PROSAIL) radiative transfer model into the long short-term memory (LSTM) network architecture; thirdly, cross-scale feature analysis technology breaks through by constructing organ-population association models based on dynamic attention mechanisms, realizing multi-granularity mapping between panicle texture features and canopy leaf area index (LAI) fluctuations. The current technical system has completed three-dimensional leaps: From discrete manual observations to full-cycle continuous perception, with monitoring frequency upgraded from weekly to hourly; from empirical threshold-based judgment to mechanism-data hybrid-driven, the cross-regional generalization ability of the model can be significantly improved; from independent link operations to full-chain collaboration of "perception-decision-execution", constructing a digital management closed-loop covering rice sowing to harvest, providing core technical support for smart farm construction. [Conclusions and Prospects] Current technologies face three-tiered challenges in data heterogeneity, feature limitations and algorithmic constraints. Future research should focus on three aspects: 1) Multi-source data assimilation systems to reconcile spatiotemporal heterogeneity through UAV-assisted satellite calibration and GAN-based cloud-contaminated data reconstruction; 2) Cross-scale physiological-spectral models integrating 3D canopy architecture with adaptive soil-adjusted indices to overcome spectral saturation; 3) Mechanism-data hybrid paradigms embedding thermal-time models into LSTM networks for environmental adaptation, developing lightweight CNNs with multi-scale attention for occlusion-resistant panicle detection, and implementing transfer learning for cross-regional model generalization. The convergence of multi-source remote sensing, intelligent algorithms, and physiological mechanisms will establish a full-cycle dynamic monitoring system based on agricultural big data.

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    Detection Method of Ectropis Grisescens Larvae in Canopy Environments Based on YOLO and Diffusion Models
    LUO Xuelun, GOUDA Mostafa, SONG Xinbei, HU Yan, ZHANG Wenkai, HE Yong, ZHANG Jin, LI Xiaoli
    Smart Agriculture    2025, 7 (5): 156-168.   DOI: 10.12133/j.smartag.SA202505023
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    [Objective] Tea has become one of the most important economic crops globally, driven by the growing popularity of tea-based beverages. However, tea production is increasingly threatened by biotic stressors, among which Ectropis grisescens stands out as a major defoliating pest. The larvae of this moth species cause substantial damage to tea plants by feeding on their leaves, thereby reducing yield and affecting the overall quality of the crop. The manual methods are not only time-consuming and labor-intensive but also suffer from low efficiency, high costs, and considerable subjectivity. In this context, the development of intelligent, accurate, and automated early detection techniques for Ectropis grisescens larvae is of vital significance. Such advancements hold the potential to enhance pest management strategies, reduce economic losses, and promote sustainable tea cultivation practices. [Methods] The recognition framework was proposed to achieve real-time and fine-grained identification of E. grisescens larvae at four distinct instar stages within complex tea canopy environments. To capture the varying morphological characteristics across developmental stages, a hierarchical three-level detection system was designed, consisting of: (1) full-instar detection covering all instars from the 1st to the 4th, (2) grouped-stage detection that classified larvae into early (1st-2nd) and late (3rd-4th) instar stages, and (3) fine-grained detection targeting each individual instar stage separately. Given the challenges posed by limited, imbalanced, and noisy training data—common issues in field-based entomological image datasets— a semi-automated dataset optimization strategy was introduced to enhance data quality and improve class representation. Building upon this refined dataset, a controllable diffusion model was employed to generate a large number of high-resolution, labeled synthetic images that emulated real-world appearances of Ectropis grisescens larvae under diverse environmental conditions. To ensure the reliability and utility of the generated data, a novel high-quality image filtering strategy was developed that automatically evaluated and selected images containing accurate, detailed, and visually realistic larval instances. The filtered synthetic images were then strategically integrated into the real training dataset, effectively augmenting the data and enhancing the diversity and balance of training samples. This comprehensive data augmentation pipeline led to substantial improvements in the detection performance of multiple YOLO-series models (YOLOv8, YOLOv9, YOLOv10, and YOLOv11). [Results and Discussions] Experimental results clearly demonstrated that the YOLO series models exhibited strong and consistent performance across a range of detection tasks involving Ectropis grisescens larvae. In the full-instar detection task, which targeted the identification of all larval stages from 1st to 4th instars, the best-performing YOLO model achieved an impressive average mAP@50 of 0.904, indicating a high level of detection precision. In the grouped instar-stage detection task, where larvae were classified into early (1st–2nd) and late (3rd–4th) instar groups, the highest mAP@50 recorded was 0.862, reflecting the model's ability to distinguish developmental clusters with reasonable accuracy. For the more challenging fine-grained individual instar detection task—requiring the model to discriminate among each instar stage independently—the best mAP@50 reached 0.697, demonstrating the feasibility of detailed stage-level classification despite subtle morphological differences. The proposed semi-automated data optimization strategy contributed significantly to performance improvements, particularly for the YOLOv8 model. Specifically, YOLOv8 showed consistent gains in mAP@50 across all three detection tasks, with absolute improvements of 0.024, 0.027, and 0.022 for full-instar, grouped-stage, and fine-grained detection tasks, respectively. These enhancements underscored the effectiveness of the dataset refinement process in addressing issues related to data imbalance and noise. Furthermore, the incorporation of the controllable diffusion model led to a universal performance boost across all YOLO variants. Notably, YOLOv10 exhibited the most substantial gains among the evaluated models, with its average mAP@50 increasing from 0.811 to 0.821 across the three detection tasks. This improvement was statistically significant, as confirmed by a paired t-test (p < 0.05), suggesting that the synthetic images generated by the diffusion model effectively enriched the training data and improved model generalization. Among all evaluated models, YOLOv9 achieved the best overall performance in detecting Ectropis grisescens larvae. It attained top mAP@50 scores of 0.909, 0.869, and 0.702 in the full-instar, grouped-stage, and fine-grained detection tasks, respectively. When averaged across all tasks, YOLOv9 reached a mean mAP@50 of 0.826, accompanied by a macro F1-Score of 0.767, highlighting its superior balance between precision and recall. [Conclusions] This study demonstrated that the integration of a controllable diffusion model with deep learning enabled accurate field-level instar detection of Ectropis grisescens, providing a reliable theoretical and technical foundation for intelligent pest monitoring in tea plantations.

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    Multi-Objective Planting Planning Method Based on Connected Components and Genetic Algorithm: A Case Study of Fujin City
    XU Menghua, WANG Xiujuan, LENG Pei, ZHANG Mengmeng, WANG Haoyu, HUA Jing, KANG Mengzhen
    Smart Agriculture    2025, 7 (5): 136-145.   DOI: 10.12133/j.smartag.SA202504012
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    [Objective] In the advancement of intensive agriculture, the contradiction between soil degradation and the demand for large-scale production has become increasingly pronounced, particularly in the core region of black soil in Northeast China. Long-term single-cropping patterns have caused soil structure damage and nutrient imbalance, severely threatening agricultural sustainability. Intensive rice cultivation has led to significant soil degradation, while the city must also balance national soybean planting mandates with large-scale production efficiency. However, existing planting planning methods predominantly focus on area optimization at the regional scale, lacking fine-grained characterization of plot-level spatial distribution, which easily results in fragmented layouts. Against this backdrop, a plot-scale multi-objective planting planning approach is developed to synergistically optimize contiguous crop distribution, soil restoration, practical production, and economic benefits, while ensuring national soybean planting tasks. This approach bridges macro-policy guidance and micro-production practices, providing scientific decision support for planting structure optimization and high-standard farmland construction in major grain-producing areas of Northeast China. [Methods] The multi-objective optimization model was established within a genetic algorithm framework, integrating connected component analysis to address plot-level spatial layout challenges. The model incorporated five indicators: economic benefit, soybean planting area, contiguous planting, crop rotation benefits, and the number of paddy-dryland conversions. The economic benefit objective was quantified by calculating the total income of crop combinations across all plots. A rigid threshold for soybean planting area was set to fulfill national mandates. The contiguous planting was evaluated using a connected-component-based method. The crop rotation benefits were scored according to predefined rotation rules. The paddy-dryland conversions were determined by counting changes in plot attributes. The model employed linear weighted summation to transform multi-objectives into a single objective for solution, generated high-quality initial populations via Latin Hypercube Sampling, and enhanced algorithm performance through connected-component-based crossover strategies and hybrid mutation strategies. Specifically, the crossover strategy was constructed based on connected component analysis: Adjacent plots with the same crop were divided into connected regions, and partial regions were randomly selected for crop gene exchange between parent generations, ensuring that the offspring inherited spatial coherence from parents, avoiding layout fragmentation caused by traditional crossover, and improving the rationality of contiguous planting. The mutation strategies included three types: Soybean threshold guarantee, plot-based crop rotation rule adaptation, and connected components-based crop rotation rule adaptation, which synergistically ensured mutation diversity and policy objective adaptability. Taking the Fujin city, Heilongjiang province—a crucial national commercial grain base—as an example, optimization was implemented using the distributed evolutionary algorithms in python (DEAP) library and validated through the simulation results of the four-year planting plan from 2020 to 2023. [Results and Discussions] Four years of simulation results demonstrated significant multi-objective balance in the optimized scheme. The contiguity index increased sharply from 0.477 in 2019 to 0.896 in 2020 and stabilized above 0.9 in subsequent years, effectively alleviating plot fragmentation and enhancing the feasibility of large-scale production. The economic benefits remained dynamically stable without significant decline, verifying the model's effectiveness in safeguarding economic efficiency. The soybean planting area stably met national thresholds while achieving strategic expansion, strengthening food security. The simulation results of crop rotation benefits reached 0.998 in 2023, indicating effective promotion of scientific rotation patterns and enhanced soil health and sustainable production capacity. The optimization objective of minimizing paddy-dryland conversions took practical production factors into account, achieving a good balance with crop rotation benefits and reflecting effective consideration of real-world production constraints. The evolutionary convergence curve showed the algorithm converged near the optimal solution, validating its convergence stability for this problem. In comparative experiments, this method outperformed traditional plot-based strategies in all optimization indicators except soybean planting area. Compared with the nondominated sorting genetic algorithm-Ⅱ (NSGA-II) multi-objective algorithm, it showed significant advantages in contiguous planting and crop rotation benefits. Although minor gaps existed in economic benefits and paddy-dryland conversions compared to NSGA-II, the planting layout was more regular and less fragmented. [Conclusions] The multi-objective planting planning method based on connected components and genetic algorithms proposed in this study bridges macro policies and micro layouts, effectively balancing black soil protection and production benefits through intelligent algorithms. By embedding spatial topology constraints into genetic operations, it solves the fragmentation problem in traditional methods while adapting to policy-driven planting scenarios via single-objective weighting strategies. Four years of simulations and comparative experiments show that this method significantly improves contiguous planting, ensures soybean production, stabilizes economic benefits, optimizes rotation patterns, and reduces paddy-dryland conversions, providing a scientific and feasible planning scheme for agricultural production. Future research can be expanded in three directions. First, further optimizing genetic algorithm parameters and introducing technologies such as deep reinforcement learning to enhance algorithm performance. Second, integrating multi-source heterogeneous data to build dynamic parameter systems and strengthen model generalization. Third, extending the method to more agricultural regions such as southern hilly areas, adjusting constraints according to local topography and crop characteristics to achieve broader application value. The research findings can provide decision support for planting structure optimization and high-standard farmland construction in major grain-producing areas of Northeast China.

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    Progress and Prospects of Research on Key Technologies for Agricultural Multi-Robot Full Coverage Operations
    LU Zaiwang, ZHANG Yucheng, MA Yike, DAI Feng, DONG Jie, WANG Peng, LU Huixian, LI Tongbin, ZHAO Kaibin
    Smart Agriculture    2025, 7 (5): 17-36.   DOI: 10.12133/j.smartag.SA202507040
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    [Significance] With the deepening of intelligent agriculture and precision agriculture, the agricultural production mode is gradually transforming from traditional manual experience based operations to a modern model driven by data, intelligent decision-making, and autonomous execution. In this context, improving agricultural operation efficiency and achieving large-scale continuous and seamless operation coverage have become key requirements for promoting the modernization of agriculture. The multi-robot full coverage operation technology, with its significant advantages in operation efficiency, system robustness, scalability, and resource utilization efficiency, provides practical and feasible intelligent solutions for key links such as sowing, plant protection, and harvesting in large-scale farmland. This technology, through the collaborative work of multi-robot systems, can not only effectively reduce the repetition rate of tasks and avoid omissions, but also achieve efficient and accurate continuous operations in complex and dynamic agricultural environments, greatly improving the automation and intelligence level of agricultural production. [Progress] Starting from the global perspective of systems engineering, an integrated closed-loop technology framework of "perception-decision-execution" is constructed. It systematically sorts out and deeply analyzes the technological development status and research methods of each key link in the full-coverage operations of agricultural multi robot. At the level of perception and recognition, it focus on exploring the application of multi-source information fusion and collaborative perception technology. By integrating multi-source sensor data, multi-level fusion of data level, feature level, and decision level is achieved, and a refined global environment model is constructed to provide accurate crop status, obstacle distribution, and terrain information for the robot system. Especially in the field of multi-robot collaborative perception, research has covered advanced models such as distributed simultaneous localization and mapping (SLAM) and ground to ground collaboration. Through information sharing and complementary perspectives, the system's perception ability and modeling accuracy in wide area, unstructured agricultural environments have been improved. At the decision-making and planning level, three key aspects are analyzed: task allocation, global path planning, and local path adjustment. Task allocation has evolved from traditional deterministic methods to market mechanisms, heuristic algorithms, and intelligent methods that integrate reinforcement learning and graph neural networks to address the challenges of dynamic and complex resource constraints in agricultural scenarios. The global path planning system analyzes the characteristics of geometric decomposition, grid method, global planning, and learning methods in terms of path redundancy, computational efficiency, and terrain adaptability. Local path planning emphasizes the combination of real-time perception in dynamic environments, using methods such as graph search, sampling optimization, model predictive control, and end-to-end reinforcement learning to achieve real-time obstacle avoidance and trajectory smoothing. At the control execution level, the focus is on model-based trajectory tracking and control technology, aiming to accurately convert planned paths into robot motion. Traditional control methods such as PID, LQR, sliding mode control, etc. are continuously optimized to cope with terrain undulations and system disturbances. In recent years, intelligent methods such as fuzzy control, neural network control, reinforcement learning, and multi machine collaborative strategies have been gradually applied, further improving the control accuracy and collaborative operation capability of the system in dynamic environments. [Conclusions and Prospects] The closed-loop technical framework is systematically constructed for agricultural multi-robot full coverage operations, and in-depth analysis of key modules is conducted, providing some understanding and suggestions, and providing theoretical references and technical paths for related research. However, the technology still faces many challenges, including perceptual uncertainty, dynamic changes in tasks, vast and irregular work areas, unpredictable dynamic obstacles, communication and collaboration barriers, and energy endurance issues. In the future, this field will further strengthen the integration with artificial intelligence, the Internet of Things, edge computing and other technologies, focusing on promoting the following directions, including the development of intelligent dynamic task allocation mechanism; optimize global and local path planning algorithms to enhance their efficiency and adaptability in large-scale complex scenarios; enhance the real-time perception and response capability of the system to dynamic environments; promote software hardware collaboration and intelligent system integration to achieve efficient communication and integrated task management; develop high-efficiency power systems and intelligent energy consumption strategies to ensure long-term continuous operation capability. Through these efforts, agricultural multi-robot systems will gradually achieve higher levels of precision, automation, and intelligence, providing key technological support for the transformation of modern agriculture.

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    Electrochemical Sensors for Plant Active Small Molecule Detection: A review
    ZHANG Le, LI Aixue, CHEN Liping
    Smart Agriculture    2025, 7 (3): 69-88.   DOI: 10.12133/j.smartag.SA202502023
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    [Significance] Plant active small molecules play an indispensable role in plants. They form the basis of the core physiological mechanisms that regulate the plant growth and development and enhance resilience to environmental stress. Achieving highly precise quantitative analysis of these active small molecules is therefore vital for promoting precise management practice in the agricultural and accelerating the development of smart agriculture. Currently, various technologies exist for the detection and analysis of these small molecules in plants. Among them, electrochemical sensing platforms have attracted extensive attention due to their significant advantages, including high sensitivity, excellent selectivity, and low cost. These advantages enable them to effectively detect trace levels of various active small molecules in plant samples. They also have the potential for real-time and in-situ detection. [Progress] Based on a comprehensive review of relevant academic literature, this article systematically summarizes the current research progress and status of electrochemical sensors applied in detecting plant active small molecule. Based on this, the article further analyzes the core sensing mechanisms of different electrochemical sensors types, signal amplification technologies for enhancing detection performance, and their huge potential in practical applications. Furthermore, this paper explores a notable development direction in this field: Sensor technology is evolving from the traditional in vitro detection mode to more challenging in vivo detection and in-situ real-time monitoring methods. Meanwhile, the article particularly emphasizes and elaborates in detail the indispensable and significant role of nanomaterials in key links such as constructing high-performance sensing interfaces and significantly enhancing detection sensitivity and selectivity. Finally, it prospectively discusses the innovative integration of electrochemical sensors with cutting-edge flexible electronic technology and powerful artificial intelligence (AI)-based data analysis, along with their potential for broad application. [Conclusions and Prospects] This article comprehensively identifies and summarizes the core technical challenges that electrochemical sensors currently face in detecting plant active small molecule. In terms of environmental detection, due to the influence of the complex matrix within plants, the response signal of the sensor is prone to drift, and its stability and sensitivity show a decline. Regarding electrolytes, the external application of liquid electrolytes dilutes the target molecules concentration in plant samples, lowering the detection accuracy. Furthermore, the transition from principle development to mature productization and industrialization of electrochemical sensors is relatively lengthy, and there are few types of sensors available for the detection of plant physiological indicators: Limiting their application in actual agricultural production. On this basis, the article prospectively analyzes the key directions of future research. First, continuously improving sensor performance indicators such as sensitivity, selectivity and reliability. Second, exploring and optimizing electrolyte material systems with stronger adaptability to significantly improve detection accuracy and long-term stability. Third, promoting deeper integration and innovation of sensor technology with advanced micro-nano electronic technology and powerful AI algorithms. The core objective of this review is to provide a theoretical guidance framework for in-depth research and systematic performance optimization of electrochemical sensing technology for plant active small molecules, as well as practical guidance for the actual application of related sensors in complex plant substrate environments.

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    High-Precision Fish Pose Estimation Method Based on Improved HRNet
    PENG Qiujun, LI Weiran, LIU Yeqiang, LI Zhenbo
    Smart Agriculture    2025, 7 (3): 160-172.   DOI: 10.12133/j.smartag.SA202502001
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    [Objective] Fish pose estimation (FPE) provides fish physiological information, facilitating health monitoring in aquaculture. It aids decision-making in areas such as fish behavior recognition. When fish are injured or deficient, they often display abnormal behaviors and noticeable changes in the positioning of their body parts. Moreover, the unpredictable posture and orientation of fish during swimming, combined with the rapid swimming speed of fish, restrict the current scope of research in FPE. In this research, a FPE model named HPFPE is presented to capture the swimming posture of fish and accurately detect their key points. [Methods] On the one hand, this model incorporated the CBAM module into the HRNet framework. The attention module enhanced accuracy without adding computational complexity, while effectively capturing a broader range of contextual information. On the other hand, the model incorporated dilated convolution to increase the receptive field, allowing it to capture more spatial context. [Results and Discussions] Experiments showed that compared with the baseline method, the average precision (AP) of HPFPE based on different backbones and input sizes on the oplegnathus punctatus datasets had increased by 0.62, 1.35, 1.76, and 1.28 percent point, respectively, while the average recall (AR) had also increased by 0.85, 1.50, 1.40, and 1.00, respectively. Additionally, HPFPE outperformed other mainstream methods, including DeepPose, CPM, SCNet, and Lite-HRNet. Furthermore, when compared to other methods using the ornamental fish data, HPFPE achieved the highest AP and AR values of 52.96%, and 59.50%, respectively. [Conclusions] The proposed HPFPE can accurately estimate fish posture and assess their swimming patterns, serving as a valuable reference for applications such as fish behavior recognition.

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    Spatiotemporal Pattern and Multi-Scenario Simulation of Land Use Conflicts: A Case Study of Shandong Section of the Yellow River Basin
    DONG Guanglong, YIN Haiyang, YAO Rongyan, YUAN Chenzhao, QU Chengchuang, TIAN Yuan, JIA Min
    Smart Agriculture    2025, 7 (2): 183-195.   DOI: 10.12133/j.smartag.SA202409007
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    [Objective] The frequent occurrence of land use conflicts, such as the occupation of arable land by urban construction land expansion, non-grain use of arable land, and the shrinking of ecological space, poses multiple pressures on the Shandong section of the Yellow River in terms of economic development, arable land protection, and ecological conservation. Accurately identifying and predicting future trends of land-use conflicts in the Shandong section of the Yellow River under various scenarios will provide a reference for the governance of land use conflicts, rational land resource utilization, and optimization of the national land spatial pattern in this region. [Methods] The data used mainly includs land use data, elevation data, basic geographic information data, meteorological data, protected area data, and socio-economic data. Drawing from the concept of ecological risk assessment, an "External Pressure + Vulnerability-Stability" model was constructed. Indicators such as area-weighted average patch fractal dimension, landscape value of land use types, and patch density were used to quantify and characterize land use conflicts in the Shandong section of the Yellow River from 2000 to 2020. Subsequently, the CA-Markov model was employed to establish cellular automata transition rules, with a 10-year simulation period using a default 5×5 cellular filter matrix, projecting 2030 land use conflict patterns under natural development, cultivated land protection, and ecological conservation scenarios. [Results and Discussions] From 2000 to 2020, significant changes in land use were observed in the Shandong section of the Yellow River, mainly characterized by rapid expansion of urban construction land and a reduction in grassland and arable land. Urban construction land increased by 4 346 km2, with its proportion rising from 13.50% in 2000 to 18.67% in 2020. During the study period, the level of land use conflict showed a mitigating trend, with the average land use conflict index decreasing from 0.567 in 2000 to 0.522 in 2020. Medium conflict has been the dominant type of land use conflict in the Shandong section of the Yellow River, followed by low conflict, while high conflict accounted for the smallest proportion. This indicates that land use conflicts in the region were generally controllable. The spatial pattern of land use conflicts in the Shandong section of the Yellow River remained relatively stable. Low conflicts were mainly distributed in areas with high concentration of arable land and water bodies, as well as in urban built-up areas. Medium conflicts were most widespread, especially in the transitional zones between arable land and rural settlements, and between arable land and forest land. The proportion of high conflict decreased from 19.34% in 2000 to 8.61% in 2020, mainly clustering in the transitional zones between urban construction land and other land types, the land type interlacing belt in the Central Shandong Hills, and along the Yellow River. The multi-scenario land use simulation results for 2030 showed significant differences in land use changes under different scenarios. Under the natural development scenario, the level of land use conflict was expected to deteriorate, with the most severe conflict situation. While both arable land protection and ecological conservation scenarios demonstrate partial conflict mitigation, the expansion of arable land occurs at the expense of ecological spaces, potentially compromising regional ecological security. In contrast, under the ecological conservation scenario, by prioritizing ecological protection and highlighting the protection of the ecological environment, the expansion of urban construction land and the reclamation of arable land, which cause ecological damage, were effectively curbed. Notably, this scenario exhibited the lowest proportion of high conflicts and demonstrated superior conflict mitigation effectiveness. [Conclusions] Land use conflicts in the Shandong section of the Yellow River have been somewhat mitigated, with the main form of conflict being the rapid expansion of urban construction land encroaching on arable land and ecological land. The ecological conservation scenario effectively balances the relationship between arable land protection, ecological security, and urbanization development, and is an optimal strategy for alleviating land use conflicts in the Shandong section of the Yellow River.

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    Pruning Point Recognition and Localization for Spindle-Shaped Apple Trees During Dormant Season Using an Improved U-Net Model
    LIU Long, WANG Ning, WANG Jiacheng, CAO Yuheng, ZHANG Kai, KANG Feng, WANG Yaxiong
    Smart Agriculture    2025, 7 (3): 120-130.   DOI: 10.12133/j.smartag.SA202501022
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    [Objective] To address the current issues of intelligent pruning robots, such as insufficient recognition accuracy of fruit tree branches and inaccurate localization of pruning points in complex field environments, a deep learning method based on the fusion of images and point clouds was proposed in this research. The method enables non-contact segmentation of dormant high-spindle apple tree branches and measurement of phenotypic parameter, which also achieving automatic identification and precise localization of pruning points. [Methods] Localized RGB-D data were collected from apple trees using a Realsense D435i camera, a device capable of accurate depth measurements within the range of 0.3~3.0 m. Data acquisition took place between early and mid-January 2024, from 9:00 AM to 4:00 PM daily. To maintain uniformity, the camera was mounted on a stand at a distance of 0.4~0.5 m from the main stems of the apple trees. Following data collection, trunks and branches were manually annotated using Labelme software. The OpenCV library was also employed for data augmentation, which helped prevent overfitting during model training. To improve segmentation accuracy of tree trunks and branches in RGB images, an enhanced U-Net model was introduced. This model utilized VGG16 (Visual Geometry Group 16) as its backbone feature extraction network and incorporated the convolutional block attention module (CBAM) at the up-sampling stage. Based on the segmentation results, a multimodal data processing pipeline was established. First, segmented branch mask maps were derived from skeleton lines extracted using OpenCV's algorithm. The first-level branch connection points were identified based on their positions relative to the trunk. Subsequently, potential pruning points were then searched within local neighborhoods through coordinate translation. An edge detection algorithm was applied to locate the nearest edge pixels relative to these potential pruning points. By extending the diameter line of branch pixel points in the images and integrating with depth information, the actual diameter of the branches could be estimated. Additionally, branch spacing was calculated using vertical coordinates differences of potential pruning points in the pixel coordinate system, alongside depth information. Meanwhile, trunk point cloud data were acquired by merging the trunk mask maps with the depth maps. Preprocessing of the point cloud enabled the estimation of the average trunk diameter in the local view through cylindrical fitting using the randomized sampling consistency (RANSAC) algorithm. Finally, an intelligent pruning decision-making algorithm was developed by investigating of orchardists' pruning experience, analyzing relevant literature, and integrating phenotypic parameter acquisition methods, thus achieving accurate prediction of apple tree pruning points. [Results and Discussions] The improved U-Net model proposed achieved a mean pixel accuracy (mPA) of 95.52% for branch segmentation, representing a 2.74 percent point improvement over the original architecture. Corresponding increases were observed in mean intersection over union (mIoU) and precision metrics. Comparative evaluations against DeepLabV3+, PSPNet, and the baseline U-Net were conducted under both backlight and front-light illumination conditions. The improved model demonstrated superior segmentation performance and robustness across all tested scenarios. Ablation experiments indicated that replacing the original feature extractor with VGG16 yielded a 1.52 percent point mPA improvement, accompanied by simultaneous gains in mIoU and precision. The integration of the CBAM at the up sampling stage further augmented the model's capacity to resolve fine branch structures. Phenotypic parameter estimation using segmented branch masks combined with depth maps showed strong correlations with manual measurements. Specifically, the coefficient of determination (R2) values for primary branch diameter, branch spacing, and trunk diameter were 0.96, 0.95, and 0.91, respectively. The mean absolute errors (MAE) were recorded as 1.33, 13.96, and 5.11 mm, surpassing the accuracy of visual assessments by human pruning operators. The intelligent pruning decision system achieved an 87.88% correct identification rate for pruning points, with an average processing time of 4.2 s per viewpoint. These results confirm the practical feasibility and operational efficiency of the proposed method in real-world agricultural settings. [Conclusions] An efficient and accurate method for identifying pruning points on apple trees was proposed, which integrates image and point cloud data through deep learning. The results indicate that this method could provide significant support for the application of intelligent pruning robots in modern agriculture. It not only offers high feasibility but also exhibits outstanding efficiency and accuracy in practical applications, thus laying a solid foundation for the advancement of agricultural automation.

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    Current Status and Trends of Application Scenarios and Industrial Development in the Agricultural Low-Altitude Economy
    HE Yong, DAI Fushuang, ZHU Jiangpeng, HE Liwen, WANG Yueying
    Smart Agriculture    2025, 7 (6): 1-17.   DOI: 10.12133/j.smartag.SA202507014
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    [Significance] The agricultural low-altitude economy has rapidly emerged as a transformative force in rural modernization, catalyzed by advancements in intelligent technologies and gradual improvements in low-altitude airspace governance. It encompasses a wide array of applications where aerial platforms—particularly unmanned aerial vehicles (UAVs)—play a pivotal role in reshaping agricultural production, environmental monitoring, and rural management systems. Systematically analyzing the development trajectory and implementation pathways of agricultural low-altitude economy holds considerable significance for promoting cross-sector integration, accelerating policy and technological innovation, and enabling the widespread adoption of intelligent solutions across agriculture-related industries. [Progress] This review draws upon an extensive body of literature and applies keyword clustering analysis to systematically explore the development of the agricultural low-altitude economy. Based on 13 570 peer-reviewed articles retrieved from the Web of Science database between 2000 and 2024, the study reveals a rapid growth trajectory, particularly since 2011, driven by technological breakthroughs in UAVs, sensors, and intelligent analytics. Five representative application domains were identified: smart farming, animal husbandry, forestry, fisheries, and rural governance. Through the integration of bibliometric tools and structured keyword combinations, the study captures both the evolution of research focus and the expansion of technical capabilities. The results demonstrate that agricultural low-altitude economy has progressed from early-stage feasibility validation toward large-scale, multi-functional applications. At the same time, it has catalyzed the development of an emerging industrial framework encompassing equipment manufacturing, aerial service provision, operational support systems, and talent development. These trends highlight the growing maturity and strategic relevance of agricultural low-altitude economy as a technological enabler for modern agriculture. In the context of smart farming, low-altitude technologies are extensively utilized for precision sowing, variable-rate fertilization, real-time crop health monitoring, and pest and disease detection. UAV-based remote sensing facilitates the creation of high-resolution field maps and spatially explicit data layers that support data-driven and site-specific decision-making in modern agricultural management. In smart livestock systems, drones are employed for livestock monitoring, early disease detection, and fence-line inspections, particularly in large, remote pasture areas with limited ground accessibility. Smart forestry applications include forest fire early warning, forest inventory updates, and dynamic monitoring of ecological changes, enabled by low-altitude hyperspectral, LiDAR, and thermal imaging technologies. For smart fisheries, UAVs and amphibious drones support water quality sensing, pond surveillance, and feeding behavior analysis, thereby enhancing aquaculture productivity, animal welfare, and environmental sustainability. In the broader context of smart rural governance, agricultural low-altitude economy technologies assist with infrastructure inspections, land use monitoring, rural logistics coordination, and even public security surveillance, contributing to comprehensive, intelligent rural revitalization. Alongside scenario-based applications, this paper also summarizes the current structure of the agricultural low-altitude economy industry chain, which is preliminarily composed of four key components: manufacturing, flight operations, supporting services, and integrated service platforms. In the manufacturing segment, the development and production of specialized agricultural UAVs, multi-rotor drones, and fixed-wing VTOL aircraft are advancing rapidly, enabling adaptation to various terrains and crop types. The flight operations segment is expanding with increasing market participation, offering aerial spraying, broadcasting, surveying, and inspection services with improved timeliness and precision. Supporting services include airspace coordination, meteorological forecasting, equipment maintenance, and safety supervision. Additionally, comprehensive service systems have emerged, integrating digital platform management, third-party evaluation, data analysis, and agricultural technical advisory services. Talent development and training form a crucial pillar of agricultural low-altitude economy's development. A growing number of vocational institutions and drone enterprises are providing structured training programs for agricultural drone pilots, technicians, and system operators. These programs aim to enhance operational capabilities, ensure safety compliance, and foster human capital that can support the sustainable growth of agricultural low-altitude economy applications across rural regions. [Conclusions and Prospects] Agricultural low-altitude economy is transitioning from isolated, technology-specific applications to an integrated, platform-oriented ecosystem that blends equipment, data, services, and governance. To fully realize its potential, several strategic directions should be pursued. First, there is a pressing need to refine regulatory frameworks to accommodate the unique operational characteristics of rural low-altitude airspace, including dynamic zoning policies, UAV registration standards, and risk management protocols. Second, sustained investment in core technologies, such as autonomous flight control, multi-modal sensing, and AI-based analytics, are essential for overcoming current technical limitations and unlocking new capabilities. Third, agricultural low-altitude economy's infrastructure backbone should be reinforced through the deployment of distributed drone ports, mobile ground control stations, and secure data networks, particularly in underserved regions. Additionally, establishing a national-scale training and certification framework is critical for ensuring an adequate supply of skilled professionals, while innovative funding models such as public-private partnerships and scenario-based insurance can enhance accessibility and scalability. Pilot projects and demonstration zones should also be expanded to validate and refine agricultural low-altitude economy systems under diverse agroecological and socio-economic conditions. In summary, agricultural low-altitude economy offers strategic leverage for advancing China's goals in smart agriculture, green development, and rural revitalization. With cross-sector collaboration and proactive policy support, agricultural low-altitude economy can evolve into a resilient and inclusive ecosystem that fosters agricultural transformation, boosts productivity, and enhances environmental sustainability.

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    Imbalanced Hyperspectral Viability Detection of Naturally Aged Soybean Germplasm Based on Semi-Supervised Deep Convolutional Generative Adversarial Network
    LI Fei, WANG Ziqiang, WU Jing, XIN Xia, LI Chunmei, XU Hubo
    Smart Agriculture    2025, 7 (5): 101-113.   DOI: 10.12133/j.smartag.SA202505013
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    [Objective] Germplasm resources are regarded as the "chips" of high-quality breeding, and evaluating the viability of soybean germplasm is essential for ensuring the secure preservation of genetic resources and promoting the healthy development of the soybean industry. Traditional viability detection methods are time-consuming, labor-intensive, and seed-consuming, highlighting the urgent need for non-destructive, intelligent, and high-throughput detection technologies. Hyperspectral imaging combined with deep learning offers a promising approach for the rapid, non-destructive assessment of soybean germplasm viability. Compared to artificially aged samples, naturally aged samples more accurately reflect the substance changes associated with the decline in germplasm viability. However, the imbalance in the number of viable and non-viable samples limits the generalization performance of viability prediction models. [Methods] In order to address the aforementioned challenges, a semi-supervised deep convolutional generative adversarial network (SDCGAN) was proposed in this research to generate high-quality hyperspectral data with associated viability labels. The SDCGAN framework consisted of three main components: a generator, a discriminator, and a classifier. The generator progressively transformed low-dimensional latent representations into hyperspectral data. This was achieved through four one-dimensional transposed convolutional layers, ensuring the output matched the dimensionality of real spectra. The discriminator adopted an optimization strategy based on the wasserstein distance, replacing the Jensen-Shannon divergence used in traditional GANs, thereby mitigating training instability and gradient vanishing. Additionally, a gradient penalty term was introduced to further stabilize model training. In the classifier, a unilateral margin loss function was employed to penalize only those samples near the decision boundary, effectively avoiding overfitting on well-separated samples and improving training efficiency. Furthermore, a spectral score fusion network (SSFNet) was developed to enable hyperspectral-based detection of soybean seed viability. SSFNet comprised two core modules: a spectral residual network and a spectral score fusion module. The spectral residual network extracted shallow-level features from the hyperspectral data, capturing local patterns within spectral sequences. The spectral score fusion module adaptively reweighted spectral channels to emphasize viability-related features and suppress redundant noise. Finally, the performance of the SDCGAN-generated spectra was evaluated using root mean square error (RMSE), while the viability detection performance of SSFNet was assessed using test accuracy, precision, area under the curve (AUC), and F1-Score. [Results and Discussions] In the performance analysis of SDCGAN, the model progressively learned and captured the key spectral features that distinguished viable and non-viable soybean seeds during the training process. The generated spectra gradually evolved from initial noisy fluctuations to smoother curves that closely resembled real spectra, demonstrating strong nonlinear modeling capability. Compared to other generative adversarial models, SDCGAN achieved the best performance in enhancing viability detection, and its generated data exhibited low error characteristics in RMSE analysis. By applying SDCGAN for data augmentation, three types of datasets were constructed: original spectra, generated spectra, and mixed spectral dataset. When using the multiple scatter correction-savitzky-golay-standardscaler (MSC-SG-SS) preprocessing strategy, SSFNet achieved the highest viability detection accuracies across all three datasets, reaching 89.50%, 90.83%, and 93.33%, respectively. In comparison with other viability detection models, SSFNet consistently outperformed alternative algorithms in all four evaluation metrics across all datasets. Particularly on the mixed dataset, SSFNet demonstrated the best performance, achieving a test accuracy of 93.33%, precision of 95.17%, AUC of 92.58%, and F1-Score of 94.83%. Notably, all models trained on the mixed dataset containing SDCGAN-generated samples achieved better performance than those trained on either original or generated datasets alone. This improvement was likely due to the increased sample diversity and balanced class distribution in the mixed dataset, which provided more comprehensive viability-related features, facilitated model convergence, and reduced overfitting. In transfer experiments, SSFNet also exhibited superior generalization capability compared to four baseline algorithms: support vector machine (SVM ), extreme gradient boosting (XGBoost), one-dimensional convolutional neural network (1D-CNN), and Transformer, achieving the highest classification accuracy of 73.67% on the mixed dataset. [Conclusions] This research constructs an integrated SDCGAN-SSFNet framework for robust viability detection of naturally aged soybean germplasm under imbalanced sample conditions. The SDCGAN component accurately learns the underlying distributional characteristics of real hyperspectral data from soybean seeds and generates realistic synthetic samples, effectively augmenting the spectral data of non-viable seeds and improving data diversity. Meanwhile, SSFNet explores inter-band spectral correlations to adaptively enhance features that are highly relevant to viability classification while effectively suppressing redundant and noisy information. This integrated approach enables rapid, nondestructive, and high-precision detection of soybean seed viability under challenging sample imbalance scenarios, providing an efficient and reliable method for seed quality assessment and agricultural decision-making.

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    Research Advances in Hyperspectral Imaging Technology for Fruit Quality Assessment
    ZHANG Zishen, CHENG Hong, GENG Wenjuan, GUAN Junfeng
    Smart Agriculture    2025, 7 (5): 52-66.   DOI: 10.12133/j.smartag.SA202507020
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    [Significance] Hyperspectral imaging (HSI) is an advanced sensing technique that simultaneously acquires high-resolution spatial data and continuous spectral information, enabling non-destructive, real-time evaluation of both external and internal fruit quality attributes. Despite its widespread application in agricultural product assessment, comprehensive reviews specifically addressing fruit quality evaluation using HSI are limited. This paper presents a comprehensive review of recent advancements in the application of HSI technology for fruit quality detection. [Progress] This paper provides a comprehensive review from three key dimensions: scenario adaptability, technological evolution trends, and industrial implementation bottlenecks, with a further analysis of the research outlook in HSI applications for fruit quality assessment. Specifically, by employing non-destructive and rapid spectral imaging techniques, HSI has markedly enhanced the accuracy of assessing various quality parameters, including external appearance, surface defects, internal quality (such as sugar content, acidity, and moisture), and ripeness. Furthermore, significant progress has been achieved in utilizing HSI for disease detection, variety classification, and origin traceability, thereby providing robust technical support for fruit quality control and supply chain management. In addition, bibliometric analysis is utilized to identify key research areas and emerging trends in the application of HSI technology for fruit quality assessment. [Conclusions and Prospects] Future research should focus on optimizing spectral dimensionality reduction techniques to enhance both the efficiency and accuracy of models. Transfer learning and incremental learning approaches should also be explored to improve the models' ability to generalize across various scenarios and fruit types. In parallel, developing lightweight system hardware and strengthening edge processing capabilities will be essential for enabling the practical deployment of HSI technology in real-world applications. Integrating lightweight deep learning networks and acceleration modules will support real-time inference, enhancing processing speed and facilitating faster data analysis. It is also crucial to establish standardized systems and protocols to promote the sharing of research findings and ensure broader application across different industries. Additionally, incorporating multimodal technologies, such as thermal imaging, gas sensors, and visual data, will improve the accuracy and robustness of detection platforms. This integration will allow for more precise and comprehensive assessments of fruit quality, further advancing the digitalization and intelligent application of HSI technology.

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    Multi Environmental Factor Optimization Strategies for Venlo-type Greenhouses Based on CFD
    NIE Pengcheng, CHEN Yufei, HUANG Lu, LI Xuehan
    Smart Agriculture    2025, 7 (3): 199-209.   DOI: 10.12133/j.smartag.SA202502002
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    [Objective] In the context of modern agricultural practices, regulating the indoor microclimate of Venlo-type greenhouses during the summer months through mechanical ventilation remains a significant challenge. This is primarily due to the nonlinear dynamics and strong coupling characteristics inherent in greenhouse environmental systems, where variables such as temperature, humidity, and CO₂ concentration interact in complex, interdependent ways. Traditional control strategies, which often rely heavily on empirical knowledge and manual intervention, are insufficient to cope with such dynamic conditions. These approaches tend to result in imprecise control, substantial time delays in response to environmental fluctuations, and unnecessarily high energy consumption during ventilation operations. Therefore, this study combines computational fluid dynamics (CFD) model with a multi-objective particle swarm optimization (MOPSO) algorithm to establish a joint optimization framework, aiming to address the issues of significant time delays and excessive operational energy consumption caused by vague environmental control strategies in this scenario. [Methods] To build a reliable simulation and optimization framework, environmental parameters such as temperature, wind speed, and CO₂ concentration were continuously collected via a network of environmental monitoring sensors installed at various positions inside the greenhouse. These real-time data served as validation benchmarks for the CFD model and supported the verification of mesh independence. In the CFD model construction, the internal structure of the Venlo-type greenhouse was precisely reconstructed, and appropriate boundary conditions were set based on empirical data. The airflow dynamics and thermal field were simulated using a finite volume-based solver. Four grid resolutions were evaluated for grid independence by comparing the variations in output metrics. The controllable parameters in the model included fan outlet wind speed and cooling pad condensation temperature. These parameters were systematically varied within predefined ranges. To evaluate the greenhouse environmental quality and energy consumption under different control conditions, three custom-defined objective functions were proposed: temperature suitability, CO2 uniformity, and fan operating energy consumption. The MOPSO algorithm was then applied to conduct iterative optimization over the defined parameter space. At each step, the objective functions were recalculated based on CFD outputs, and Pareto-optimal solutions were identified using non-dominated sorting. After iterative optimization using the algorithm, the conflicting objectives of environmental deviation and energy consumption were balanced, leading to the optimal range for the greenhouse environmental control strategy in this scenario. [Results and Discussions] The experimental results showed that the environmental field simulation accuracy of the CFD model was high, with an average relative error of 5.7%. In the grid independence test, three grid types, coarse, medium, and fine, were selected. The variations in the grid divisions were 1.7% and 0.6%, respectively. After considering both computational accuracy and efficiency, the medium grid division standard was adopted for subsequent simulations. The optimization strategy proposed in this study allows for closed-loop evaluation of the environment. The algorithm set the population size to 100 particles, and within the specified range of fan outlet wind speed and cooling pad condensation temperature, each particle iterates 5 times for optimization. The position updated in each iteration was used to calculate the values of the three objective evaluation functions, followed by non-dominated comparison and adaptation of the solutions, until the optimization was complete. In the Pareto surface fitted by the output results, the fan outlet wind speed ranges from 2.8 to 5.4 m/s, and the inlet temperature ranges from 295.3 to 299.7 K. Since the evaluation functions under the environmental control strategy were all in an ideal, non-dominated state, two sets of boundary control conditions were randomly selected for simulation: operating Condition A [296 K, 3.5 m/s] and operating Condition B [299 K, 5 m/s]. Post-processing contour plots showed that both operating conditions achieve good environmental optimization uniformity. The approximate ranges for each parameter were: temperature from 300.3 to 303.9 K, wind speed from 0.7 to 2.3 m/s, and CO2 concentration from 2.43 × 10-5 to 3.56 × 10-5 kmol/m3. Based on environmental uniformity optimization, operating Condition A focused on adjusting the suitable temperature for crops by lowering the cooling pad condensation temperature, but there was a relative stagnation of CO2. Operating Condition B, by increasing the fan outlet wind speed, focused on regulating CO2 flow and diffusion, but the gradient change of airflow near the two side walls was relatively abrupt. [Conclusions] This study complements the research on the systematic adjustment of greenhouse environmental parameters, while its closed-loop iterative features also enhance the simulation efficiency. The simulation results show that by arbitrarily combining the optimal solution set within the theoretical range of the strategy output, optimization of the targeted objectives can be achieved by appropriately discarding other secondary objectives, providing a reference for regulating the uniformity and economy of mechanical ventilation in greenhouses. Subsequent research can further quantify the coupling effects and weight settings of each objective function to improve the overall optimization of the functions.

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    Detection Method for Log-Cultivated Shiitake Mushrooms Based on Improved RT-DETR
    WANG Fengyun, WANG Xuanyu, AN Lei, FENG Wenjie
    Smart Agriculture    2025, 7 (5): 67-77.   DOI: 10.12133/j.smartag.SA202506034
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    [Objective] Shiitake mushroom is one of the most important edible and medicinal fungi in China, and its factory-based cultivation has become a major production model. Although mixing, bagging, sterilization, and inoculation have been largely automated, harvesting and grading still depend heavily on manual labor, which leads to high labor intensity, low efficiency, and inconsistency caused by subjective judgment, thereby restricting large-scale production. Furthermore, the clustered growth pattern of shiitake mushrooms, the high proportion of small targets, severe occlusion, and complex illumination conditions present additional challenges to automated detection. Traditional object detection models often struggle to balance accuracy, robustness, and lightweight efficiency in such environments. Therefore, there is an urgent need for a high-precision and lightweight detection model capable of supporting intelligent evaluation in mushroom harvesting. [Methods] To address these challenges, this study proposed an improved real-time detection model named FSE-DETR, based on the RT-DETR framework. In the backbone, the FasterNet Block was introduced to replace the original HGNetv2 structure. By combining partial convolution (PConv) for efficient channel reduction and pointwise convolution (PWConv) for rapid feature integration, the FasterNet Block reduced redundant computation and parameter size while maintaining effective multi-scale feature extraction, thereby improving both efficiency and deployment feasibility. In the encoder, a small object feature fusion network (SFFN) was designed to enhance the recognition of immature mushrooms and other small targets. This network first applied space-to-depth convolution (SPDConv), which rearranged spatial information into channel dimensions without discarding fine-grained details such as edges and textures. The processed features were then passed through the cross stage partial omni-kernel (CSPOmniKernel) module, which divided feature maps into two parts: one path preserved original information, while the other path underwent multi-scale convolutional operations including 1×1, asymmetric large-kernel, and frequency-domain transformations, before being recombined. This design enabled the model to capture both local structural cues and global semantic context simultaneously, improving its robustness under occlusion and scale variation. For bounding box regression, the Efficient Intersection over Union (EIoU) loss function was adopted to replace generalized IoU (GIoU). Unlike GIoU, EIoU explicitly penalized differences in center distance, aspect ratio, and scale between predicted and ground-truth boxes, resulting in more precise localization and faster convergence during training. The dataset was constructed from images collected in mushroom cultivation facilities using fixed-position RGB cameras under diverse illumination conditions, including direct daylight, low-light, and artificial lighting, to ensure realistic coverage. Four mushroom categories were annotated: immature mushrooms, flower mushrooms, smooth cap mushrooms, and defective mushrooms, following industrial grading standards. To address the limited size of raw data and prevent overfitting, extensive augmentation strategies such as horizontal and vertical flipping, random rotation, Gaussian and salt-and-pepper noise addition, and synthetic occlusion were applied. The augmented dataset consisted of 4 000 images, which were randomly divided into training, validation, and test sets at a ratio of 7:2:1, ensuring balanced distribution across all categories. [Results and Discussions] Experimental evaluation was conducted under consistent hardware and hyperparameter settings. The ablation study revealed that FasterNet effectively reduced parameters and computation while slightly improving accuracy, SFFN significantly enhanced the detection of small and occluded mushrooms, and EIoU improved bounding box regression. When integrated, these improvements enabled the final model to achieve an accuracy of 95.8%, a recall of 93.1%, and a mAP50 of 95.3%, with a model size of 19.1 M and a computational cost of 53.6 GFLOPs, thus achieving a favorable balance between precision and efficiency. Compared with mainstream detection models including Faster R-CNN, YOLOv7, YOLOv8m, and YOLOv12m, FSE-DETR consistently outperformed them in terms of accuracy, robustness, and model efficiency. Notably, the mAP for immature and defective mushrooms increased by 2.4 and 2.5 percentage points, respectively, compared with the baseline RT-DETR, demonstrating the effectiveness of the SFFN module for small-object detection. Visualization analysis further confirmed that FSE-DETR maintained stable detection performance under different illumination and occlusion conditions, effectively reducing missed detections, false positives, and repeated recognition, while other models exhibited noticeable deficiencies. These results verified the superior robustness and reliability of the proposed model in practical mushroom factory environments. [Conclusions] The proposed FSE-DETR model integrated the FasterNet Block, Small Object Feature Fusion Network, and EIoU loss into the RT-DETR framework, achieving state-of-the-art accuracy while maintaining lightweight characteristics. The model showed strong adaptability to small targets, occlusion, and complex illumination, making it a reliable solution for intelligent mushroom harvest evaluation. With its balance of precision and efficiency, FSE-DETR demonstrates great potential for deployment in real-world factory production and provides a valuable reference for developing high-performance, lightweight detection models for other agricultural applications.

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    Lightweight Apple Instance Segmentation Algorithm Based on SSW-YOLOv11n for Complex Orchard Environments
    HAN Wenkai, LI Tao, FENG Qingchun, CHEN Liping
    Smart Agriculture    2025, 7 (5): 114-123.   DOI: 10.12133/j.smartag.SA202505002
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    [Objective] In complex orchard environments, accurate fruit detection and segmentation are critical for autonomous apple-picking robots. Environmental factors severely degrade fruit visibility, challenging instance segmentation models across diverse field conditions. Apple-picking robots operate on embedded edge-computing platforms with stringent constraints on processing power, memory, and energy consumption. Limited computational resources preclude high-complexity deep-learning architectures, requiring segmentation models to balance real-time throughput and resource efficiency. This study introduces SSW-YOLOv11n, a lightweight instance segmentation model derived from YOLOv11n and tailored to orchard environments. SSW-YOLOv11n maintains high mask accuracy under adverse conditions—variable lighting, irregular occlusion, and background clutter—while delivering accelerated inference on resource-limited edge devices through three core design enhancements. [Methods] The SSW-YOLOv11n model first introduced GSConv and VoVGSCSP modules into its neck network, thereby constructing a highly compact yet computationally efficient "Slim-Neck" architecture. By integrating GSConv—an operation that employs grouped spatial convolutions and channel-shuffle techniques—and VoVGSCSP—a cross-stage partial module optimized for balanced depth and width—the model substantially reduced its overall floating-point operations while concurrently enhancing the richness of its feature representations. This optimized neck design facilitated more effective multi-scale information fusion, ensuring that semantic features corresponding to target regions were extracted comprehensively, all without compromising the model's lightweight nature. Subsequently, the authors embedded the SimAM self-attention mechanism at multiple output interfaces between the backbone and neck subnets. SimAM leveraged a parameter-free energy-based weighting strategy to dynamically amplify critical feature responses and suppress irrelevant background activations, thereby augmenting the model's sensitivity to fruit targets amid complex, cluttered orchard scenes. Finally, the original bounding-box regression loss was replaced with Wise-IoU, which incorporated a dynamic weighting scheme based on both center-point distance and geometric discrepancy factors. This modification further refined the regression process, improving localization precision and stability under variable environmental conditions. Collectively, these three innovations synergistically endowed the model with superior instance-segmentation performance and deployment adaptability, offering a transferable design paradigm for implementing deep-learning-based vision systems on resource-constrained agricultural robots. [Results and Discussions] Experimental results demonstrated that SSW-YOLOv11n achieved Box mAP50 and Mask mAP50 of 76.3% and 76.7%, respectively, representing improvements of 1.7 and 2.4 percentage points over the baseline YOLOv11n model. The proposed model reduced computational complexity from 10.4 to 9.1 GFLOPs (12.5% reduction) and achieved a model weight of 4.55 MB compared to 5.89 MB for the baseline (22.8% reduction), demonstrating significant efficiency gains. These results indicate that the synergistic integration of lightweight architecture design and attention mechanisms effectively addresses the trade-off between model complexity and segmentation accuracy. Comparative experiments showed that SSW-YOLOv11n outperformed Mask R-CNN, SOLO, YOLACT, and YOLOv11n with Mask mAP50 improvements of 23.2, 20.3, 21.4, and 2.4 percentage points, respectively, evidencing substantial advantages in segmentation precision within unstructured orchard environments. The superior performance over traditional methods suggests that the proposed approach successfully adapts deep learning architectures to agricultural scenarios with complex environmental conditions. Edge deployment testing on NVIDIA Jetson TX2 platform achieved 29.8 FPS inference rate, representing an 18.7% improvement over YOLOv11n (25.1 FPS), validating the model's real-time performance and suitability for resource-constrained agricultural robotics applications. [Conclusions] SSW-YOLOv11n effectively enhanced fruit-target segmentation accuracy while reducing computational overhead, thus providing a robust technical foundation for the practical application of autonomous apple-picking robots. By addressing the dual imperatives of high-precision perception and efficient inference within constrained hardware contexts, the proposed approach advanced the state of the art in intelligent agricultural robotics and offered a scalable solution for large-scale orchard automation.

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    Beef Cattle Object Detection Method Under Occlusion Environment Based on Improved YOLOv12
    LIU Yiheng, LIU Libo
    Smart Agriculture    2025, 7 (5): 182-192.   DOI: 10.12133/j.smartag.SA202503018
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    [Objective] With the rapid development of intelligent agriculture, computer vision-based livestock detection technology has become increasingly important in modern farming management. Among various livestocks, beef cattle play a crucial role in animal husbandry industry all over the world. Accurate detection and counting of beef cattle are essential for improving breeding efficiency, monitoring animal health, and supporting government subsidy distribution. However, in real-world farming environments, cattle often gather and move closely together, leading to frequent occlusions. These occlusions significantly degrade the performance of traditional object detection algorithms, resulting in missed detections, false positives, and poor robustness. Manual counting methods are labor-intensive, error-prone, and inefficient, while existing deep learning-based detection models still struggle with occlusion scenarios due to limited feature extraction capabilities and insufficient use of global contextual information. To address these challenges, an improved object detection algorithm named YOLOv12s-ASR, based on the YOLOv12s framework, was proposed in this research. The goal is to enhance detection accuracy and real-time performance in complex occlusion conditions, providing a reliable technical solution for intelligent beef cattle monitoring. [Methods] The proposed YOLOv12s-ASR algorithm introduced three key improvements to the baseline YOLOv12s model. First, part of the standard convolution layers with a modifiable kernel convolution module (AKConv) was replaced. Unlike traditional convolutions with fixed kernel shapes, AKConv could dynamically adjust the shape and size of the convolution kernel according to the input image content. This flexibility allowed the model to better capture local features of occluded cattle, especially in cases where only partial body parts were visible. Second, a self-ensembling attention mechanism (SEAM) was integrated into the Neck structure. SEAM combined spatial and channel attention through depthwise separable convolutions and consistency regularization, enabling the model to learn more robust and discriminative features. It enhanced the model's ability to perceive global contextual information, which was crucial for inferring the presence and location of occluded targets. Third, a repulsion loss function was introduced to supplement the original loss. This loss function included two components: RepGT, which pushed the predicted box away from nearby ground truth boxes, and RepBox, which encouraged separation between different predicted boxes. By reducing the overlap between adjacent predictions, the repulsion loss helped mitigate the negative effects of non-maximum suppression (NMS) in crowded scenes, thereby improving localization accuracy and reducing missed detections. The overall architecture maintained the lightweight design of YOLOv12s, ensuring that the model remained suitable for deployment on edge devices with limited computational resources. Extensive experiments were conducted on a self-constructed beef cattle dataset containing 2 458 images collected from 13 individual farms in Ningxia, China. The images were captured using surveillance cameras during daytime hours and included various occlusion scenarios. The dataset was divided into training, validation, and test sets in a 7:2:1 ratio, with annotations carefully reviewed by multiple experts to ensure accuracy. [Results and Discussions] The proposed YOLOv12s-ASR algorithm achieved a mean average precision (mAP) of 89.3% on the test set, outperforming the baseline YOLOv12s by 1.3 percent points. The model size was only 8.5 MB, and the detection speed reached 136.7 frames per second, demonstrating a good balance between accuracy and efficiency. Ablation studies confirmed the effectiveness of each component: AKConv improved mAP by 0.6 percent point, SEAM by 1.0 percent point and repulsion loss by 0.6 percent point. When all three modules were combined, the mAP increased by 1.3 percent points, validating their complementary roles. Furthermore, the algorithm was evaluated under different occlusion levels—slight, moderate, and severe. Compared to YOLOv12s, YOLOv12s-ASR improved mAP by 4.4, 2.9, and 4.4 percent points, respectively, showing strong robustness across varying occlusion conditions. Comparative experiments with nine mainstream detection algorithms, including Faster R-CNN, SSD, Mask R-CNN, and various YOLO versions, further demonstrated the superiority of YOLOv12s-ASR. It achieved the highest mAP while maintaining a compact model size and fast inference speed, making it particularly suitable for real-time applications in resource-constrained environments. Visualization results also showed that YOLOv12s-ASR could more accurately detect and localize cattle targets in crowded and occluded scenes, with fewer false positives and missed detections. [Conclusions] Experimental results show that YOLOv12s-ASR achieves state-of-the-art performance on a self-built beef cattle dataset, with high detection accuracy, fast processing speed, and a lightweight model size. These advantages make it well-suited for practical applications such as automated cattle counting, behavior monitoring, and intelligent farm management. Future work will focus on further enhancing the model's generalization ability in more complex environments and extending its application to multi-object tracking and behavior analysis tasks.

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    Small Target Detection Method of Maize Leaf Disease Based on DCC-YOLOv10n
    DANG Shanshan, QIAO Shicheng, BAI Mingyu, ZHANG Mingyue, ZHAO Chenyu, PAN Chunyu, WANG Guochen
    Smart Agriculture    2025, 7 (5): 124-135.   DOI: 10.12133/j.smartag.SA202504017
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    [Objective] Precise detection of maize leaf diseases plays a pivotal role in safeguarding maize yields and promoting sustainable agricultural development. However, existing detection algorithms often fall short in effectively capturing the intricate morphological details and shape characteristics of disease spots, particularly under challenging scenarios involving small disease targets. To overcome these challenges, a novel maize leaf disease detection algorithm, DCC-YOLOv10n, is presented in this research, which is specifically optimized for scenarios involving small-scale disease targets. [Methods] The core of the proposed method lay in three innovative architectural enhancements to the YOLOv10n detection framework. Firstly, a DRPAKConv module was designed, which built upon the arbitrary kernel convolution (AKConv). DRPAKConv replaced the conventional 3×3 convolutions that typically occupied a large proportion of the model's parameters. It featured two parallel branches: A dynamic sampling branch that adjusted the sampling shapes based on the spatial distribution of disease patterns, and a static convolution branch that adapted kernel sizes to retain spatial coverage and consistency. This design significantly enhanced the network's capability to recognize small-scale disease spots by dynamically modulating the receptive field and focusing on localized lesion details. Secondly, an improved feature fusion part was introduced by replacing the traditional C2f feature fusion module with a novel CBVoVGSCSP module. This redesigned module aimed to address the issue of gradient vanishing in deep feature fusion networks while reducing computational redundancy. CBVoVGSCSP preserved rich semantic information and improved the continuity of gradient flow across layers, which was critical for training deeper models. Furthermore, it enhanced multi-scale feature fusion and improved detection sensitivity for lesions of varying sizes and appearances. Thirdly, the convolutional attention-based feature map (CAFM) was incorporated into the neck network. This component enabled the model to effectively capture contextual relationships across multiple scales and enhanced the interaction between spatial and channel attention mechanisms. By selectively emphasizing or suppressing features based on their relevance to disease identification, the module allowed the model to more accurately distinguish between diseased and healthy regions. As a result, the model's representational capacity was improved, leading to enhanced detection accuracy in complex field environments. [Results and Discussions] Extensive experiments were conducted on a specialized maize leaf disease data set, which included annotated samples across multiple disease categories with diverse visual characteristics. Through ablation experiments and comparisons with different algorithms, it had been found that the DCC-YOLOv10n algorithm had exhibited good detection accuracy on the maize leaf disease dataset. Compared with YOLOv10n, the optimized algorithm demonstrated a reduction in computational complexity by 0.5 GFLOPs, with the model parameters compressed to merely 2.99 M. Significant improvements were observed in precision, recall, and mean average precision, which increased by 1.7, 2.6, and 1.7 percentage points respectively, and achieved 96.2%, 90.3%, and 94.1%. Based on the precision-recall curve comparison, the DCC-YOLOv10n algorithm had achieved more stable overall performance, with the mean average precision improved from 92.4% to 94.1% (an increase of 1.7 percentage points), which had fulfilled the detection requirements for small targets of maize leaf diseases. The findings underscored the robustness and adaptability of the DCC-YOLOv10n algorithm under challenging conditions. [Conclusions] The DCC-YOLOv10n algorithm presents a significant advancement in the field of agricultural disease diagnostics by addressing the limitations of existing methods with respect to small-target detection. The novel architectural components—DRPAKConv, CBVoVGSCSP, and CAFM integrated with attention fusion—not only significantly enhance the model's detection performance, but also advance the development of intelligent, data-efficient, and highly accurate disease monitoring systems tailored for modern agricultural applications. This research would serve as a valuable reference for future developments in lightweight, efficient, and accurate maize disease detection models, and offer practical significance for intelligent maize management.

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    Current Status and Development Trend of Low-Altitude Economy Industry in Orchards
    WANG Xuechang, XU Wenbo, ZHENG Yongjun, YANG Shenghui, LIU Xingxing, SU Daobilige, WANG Zimeng
    Smart Agriculture    2025, 7 (6): 35-57.   DOI: 10.12133/j.smartag.SA202506008
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    [Significance] The low-altitude economy in orchards represents a key emerging direction in the integrated development of new-quality productive forces in agriculture. As a burgeoning industry driving the high-quality development of the fruit sector, it relies on the integration of advanced equipment manufacturing, the application of smart agriculture technologies and the expansion of consumer-centric ecosystems. These elements contribute to building a full-cycle industrial chain encompassing orchard production, management and services. This fosters the coordinated development of the entire low-altitude value chain and supports the formation of a closed-loop industrial ecosystem. This paper systematically reviews the key technological pathways and development trends of the orchard low-altitude economy across three dimensions: upstream equipment manufacturing, midstream operational processes and downstream service systems. The aim is to provide strategic reference for technological innovation and industrial planning in related fields. [Progress] In the upstream segment, research and industrial development are increasingly focused on lightweight and multifunctional aerial platforms tailored to the complex terrain of mountainous orchards. By utilizing carbon fiber composites, high energy-density batteries and hybrid power systems, these platforms achieve significant reductions in weight and improvements in flight endurance. The integration of artificial intelligence (AI) computing chips, light detection and ranging (LiDAR) and multispectral sensors equips drones with advanced capabilities for precise fruit tree recognition, obstacle avoidance in complex landscapes and multimodal environmental perception. With centimeter-level real-time kinematic (RTK) positioning and multi-sensor fusion flight control algorithms, operational safety and autonomy have been greatly enhanced. Furthermore, low-altitude infrastructure, such as distributed takeoff and landing points and mobile battery-swapping stations, based on integrated 5G-Advanced and BeiDou navigation communication systems, is being systematically deployed. This provides strong support for continuous unmanned operations in hilly and mountainous orchards. The midstream segment, encompassing the pre-production, in-production and post-production stages, serves as the core scenario for value realization in the low-altitude economy. In the pre-production stage, high-resolution remote sensing imagery, combined with machine learning models such as extreme gradient boosting (XGBoost) and convolutional neural networks, enables detailed diagnostics of soil nutrients, micro-topography and vegetation cover. These insights support the precise planning of digital orchards. During the in-production stage, monitoring models based on indices such as normalized difference vegetation index (NDVI) and leaf area index (LAI) facilitate real-time assessment of tree vigor and early detection of pests and diseases, enhancing the management of plant health and growth conditions. Intelligent systems that integrate target recognition, path optimization and electric atomizing nozzles allow for precise, demand-driven application of pesticides and fertilizers, thereby improving resource efficiency and reducing environmental impact. Additionally, collaborative multi-UAV (unmanned aerial vehicle) operations and ground-aerial collaboration, optimized through genetic algorithms and digital twin models, further enhance task scheduling, flight path planning and energy utilization. In the post-production stage, drones equipped with robotic arms or vacuum suction grippers, coupled with thermal imaging, are increasingly effective in fruit identification and targeted harvesting, achieving higher levels of automation and reliability. At the same time, low-altitude logistics networks, supported by autonomous navigation and multi-sensor obstacle avoidance technologies, are addressing the last-mile challenges in cold-chain transportation. This significantly shortens the time window from field to sorting center, improving overall supply chain efficiency. At the downstream service level, the orchard low-altitude economy has evolved beyond single-equipment sales into a diversified service ecosystem. This emerging model centers on pilot training, drone insurance, equipment leasing and the integration of orchard tourism, forming a new type of business landscape. On one hand, standardized pilot training programs and operational quality evaluation systems have enhanced both talent development and safety assurance. On the other hand, risk control models developed by insurers based on operational data, along with "rent-to-own" financing schemes, have effectively lowered entry barriers for farmers. Moreover, the rise of integrated low-altitude agri-tourism models is steadily boosting the brand value of fruit products and generating new income streams through cultural and tourism-related activities. [Conclusions and Prospects] As a vital carrier of new-quality productive forces in agriculture, the orchard low-altitude economy has established a comprehensive industrial chain encompassing equipment manufacturing, operational systems and service platforms. This integrated structure is driving the transformation of orchard management toward greater intelligence, precision and sustainability. Despite current challenges such as limited equipment endurance and underdeveloped service systems, the sector is expected to achieve continuous breakthroughs through the development of high-payload aerial platforms, the integration of data-driven operational systems, the construction of diversified service ecosystems, and the refinement of relevant policies and standards. With the gradual opening of low-altitude airspace and the rapid iteration of core technologies, the orchard low-altitude economy is poised to become a key driver of agricultural modernization and rural revitalization.

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    Light-Trapping Rice Planthopper Detection Method by Combining Spatial Depth Transform Convolution and Multi-scale Attention Mechanism
    LI Wenzheng, YANG Xinting, SUN Chuanheng, CUI Tengpeng, WANG Hui, LI Shanshan, LI Wenyong
    Smart Agriculture    2025, 7 (5): 169-181.   DOI: 10.12133/j.smartag.SA202507024
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    [Objective] Planthoppers suck the sap from the phloem of rice plants, causing malnutrition and slow growth of the plants, resulting in large-scale yield reduction. Therefore, timely and effective monitoring of planthopper pests and analysis of their occurrence degree are of vital importance for the prevention of rice diseases. The traditional detection of planthopper pests mainly relies on manual methods for diagnosis and identification. However, due to the tiny size of planthopper pests, on-site manual investigation is not only time-consuming and labor-intensive but also greatly influenced by human subjectivity, making it easy to misjudge. In response to the above issues, the intelligent light traps can be used to assist in the work. When using intelligent light traps to detect dense and occluded low-resolution and small-sized planthopper pests, problems such as low accuracy, false detection, and missed detection are prone to occur. For this purpose, based on YOLOv11x, a light-trapping rice planthopper detection method by combining spatial depth transform convolution and multi-scale attention mechanism was proposed in this research. [Methods] The image data in this research were collected by multiple light-induced pest monitoring devices installed in the experimental rice fields. The images included two types of planthopper pests, the brown planthopper and the white-backed planthopper. The image sizes were both 5 472 pixels ×3 648 pixels, totaling 998 images. The original dataset was divided into a training set and a validation set in a 4:1 ratio. To enhance the learning efficiency of the model during training, two data augmentation operations, horizontal flipping and vertical flipping, were performed on the images in the training set. A total of 2 388 images in the training set were obtained for model training, and 200 images in the validation set were used for model inference validation. To improve the model performance, first of all, the C3k2 module in the original YOLOv11x network was improved by using the efficient multi-scale attention (EMA) mechanism to enhance the perception of the model and the fusion ability of small-volume pest features in dense and occlusions. Secondly, the space-to-depth-convolution (SPD-Conv) was used to replace the Conv common convolution module in the original model, further improving the extraction accuracy of the model for low-resolution and small-volume pest features and reducing the number of parameters. In addition, a P2 detection layer was added to the original network and the P5 detection layer was removed, thereby enhancing the model's detection performance for small targets in a targeted manner. Finally, by introducing the dynamic non-monotonic focusing mechanism loss function wise-intersection over union (WIoU)v3, the positioning ability of the model was enhanced, thereby reducing the false detection rate and missed detection rate. [Results and Discussions] The test results showed that the precision (P), recall (R), mean average precision at IoU equals 0.50 (mAP50) and the mean average precision at IoU thresholded from 0.50 to 0.95 with a step size of 0.05 (mAP50-95) of the improved model on the self-built rice planthopper dataset (dataset_Planthopper) reached 77.5%, 73.5%, 80.8%, and 44.9% respectively. Compared with the baseline model YOLOv11x, it has increased by 4.8, 3.5, 5.5 and 4.7 percent points, respectively. The number of parameters has been reduced from 56 M to 40 M, a reduction of 29%. Compared with the current mainstream object detection models YOLOv5x, YOLOv8x, YOLOv10x, YOLOv11x, YOLOv12x, Salience DETR-R50, Relation DETR-R50, RT-DETR-x, the mAP50 of the improved model was 6.8, 7.8, 8.6, 5.5, 5.6, 8.7, 6.9 and 6.9 percentage points higher, respectively, and it had the best comprehensive performance. [Conclusions] The improved YOLOv11x model effectively enhances the performance of detecting low-resolution and small-sized planthopper pests under dense and occluded insect conditions, and reduces the probability of missed detection and false detection. In practical applications, it could assist in achieving precise monitoring of farmland pests and scientific prevention and control decisions, thereby reducing the use of chemical pesticides and promoting the intelligent development of agriculture. Although this method has achieved significant improvements in multiple indicators, it still had certain limitations. Firstly, the species of planthoppers were numerous and their forms were diverse. The current models mainly targeted some typical species, and their generalization ability needed to be further verified. Secondly, due to the limitations of the data collection environment, there was still room for improvement in the performance of the model under extreme lighting changes and extremely occluded scenarios. Finally, although the number of parameters had decreased, the real-time detection speed still needed to be optimized to meet the requirements of some low-power edge devices. Future research can focus on expanding the generalization, robustness and lightweighting of more types of rice planthopper models in more complex situations.

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    Design and Test of Rotary Envelope Combing-Type Tobacco Leaf Harvesting Mechanism
    WANG Xiaohan, RAN Yunliang, GE Chao, GUO Ting, LIU Yihao, CHEN Du, WANG Shumao
    Smart Agriculture    2025, 7 (3): 210-223.   DOI: 10.12133/j.smartag.SA202501020
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    [Objective] China is a big tobacco producer in the world, where tobacco significantly contributes to the national economy. Among all production stages, the leaf harvesting process requires the most labor. Currently, tobacco leaf harvesting in China remains predominantly manual, characterized by low mechanization, high labor demand, a limited harvesting window, and high labor intensity. With the advancement of agricultural modernization, mechanized tobacco leaf harvesting has become increasingly essential. However, existing tobacco harvesters are oversized and cause substantial leaf damage, making them unsuitable for China's conditions. To address this, a rotary envelope combing-type harvesting mechanism is proposed to minimize leaf damage and loss during harvesting. [Methods] A tobacco plant model was developed based on morphological characteristics and assessed the mechanical properties of tobacco leaves using a digital push-pull force gauge to measure tensile and bending characteristics. Force measurements for leaf separation in various directions revealed minimal force requirements when separating leaves from top to bottom. Based on these findings, a rotary envelope comb-type harvesting mechanism was designed, featuring both a transmission mechanism and a picking wheel. During operation, the picking wheel rotates around the tobacco stem, employing inertial combing from top to bottom for efficient leaf separation. Analysis of interactions between the picking mechanism and tobacco leaves identified combing speed as the parameter with greatest impact on picking efficiency. The mechanism's structural parameters affecting the picking wheel's movement trajectory were examined, and an improved particle swarm optimization algorithm was applied using MATLAB to refine these parameters. Additionally, Abaqus finite element simulation software was utilized to optimize the wheel structure's mechanical combing process. Dynamic simulation tests using Adams software modeled the mechanism's process of enveloping the tobacco stem and separating leaves, validating suction efficiency and determining optimal envelope range and speed parameters at various traveling speeds. To evaluate the picking effect and effectiveness of the tobacco leaf picking mechanism designed in this study, a field experiment was conducted in Sanxiang town, Yiyang county, Henan province. The performance of the harvesting mechanism was analyzed based on two critical evaluation criteria: the rate of tobacco leaf damage and the leakage rate. [Results and Discussions] By optimizing the mechanism's structural parameters using MATLAB, horizontal movement was reduced by 50.66%, and the movement trajectory was aligned vertically with the tobacco leaves, significantly reducing the risk of collision during the picking process. Finite element analysis identified the diameter of the picking rod as the key structural parameter influencing picking performance. Following extensive simulations, the optimal picking rod diameter was determined to be 15 mm, offering an ideal balance between structural strength and functional performance. The optimal envelope circle diameter for the mechanism was established at 70 mm. Aluminum alloy was selected as the material for the picking rod due to its lightweight nature, high strength-to-weight ratio, and excellent corrosion resistance. Dynamics analysis further revealed that the combing speed should not exceed 2.5 m/s to minimize leaf damage. The ideal rotational speed range for the picking mechanism was determined to be between 120 and 210 r/min, balancing operational efficiency with leaf preservation. These findings provide crucial guidance for refining the design and enhancing the practical performance of the picking mechanism. Field tests confirmed that the mechanism significantly improved operational performance, achieving a leakage rate below 7% and a damage rate below 10%, meeting the requirements for efficient tobacco picking. It was observed that excessive leaf leakage primarily occurred when leaves were steeply inclined, which hindered effective stem envelopment by the harvesting mechanism. Consequently, the mechanism proved particularly effective for picking centrally positioned leaves, while drooping leaves resulted in higher leakage and damage rates. The primary cause of leaf damage was found to be mechanical contact between the harvesting mechanism and the leaves during operation. Notably, while increasing striking speed reduced leakage, it simultaneously led to a higher damage rate. Compared to the existing harvesting mechanism, this newly developed mechanism is more compact and supports layered leaf picking, making it especially well-suited for integration into small- and medium-sized harvesting machinery. [Conclusions] This study presents an effective and practical solution for tobacco leaf harvesting mechanization, specifically addressing the critical challenges of leaf damage and leakage. The proposed solution not only improves harvesting quality but also features a significantly simplified mechanical structure. By combining innovative technology with optimized design, this approach minimizes impact on delicate leaves, reduces leakage, and ensures higher yields with minimal human intervention. Analysis and testing demonstrate this mechanized solution's potential to significantly reduce production losses, offering both economic and operational benefits for the tobacco industry.

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    Chinese Tea Pest and Disease Named Entity Recognition Method Based on Improved Boundary Offset Prediction Network
    XIE Yuxin, WEI Jiangshu, ZHANG Yao, LI Fang
    Smart Agriculture    2025, 7 (5): 88-100.   DOI: 10.12133/j.smartag.SA202505007
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    [Objective] Named entity recognition (NER) is vital for many natural language processing (NLP) applications, including information retrieval and knowledge graph construction. While Chinese NER has advanced with datasets like ResumeNER, WeiboNER, and CLUENER (Chinese language understanding evaluation NER), most focus on general domains such as news or social media. However, there is a notable lack of annotated data in specialized fields, particularly agriculture. In the context of tea pest and disease, this shortage hampers progress in intelligent agricultural information extraction. These domain-specific texts pose unique challenges for NER due to frequent nested and long-span entities, which traditional sequence labeling models struggle to handle. Issues such as boundary ambiguity further complicate accurate entity recognition, leading to poor segmentation and labeling performance. Addressing these challenges requires targeted datasets and improved NER techniques tailored to the agricultural domain. [Methods] The proposed model comprises two core modules specifically designed to enhance performance in BOPN (Boundary-Oriented and Path-aware Named Entity Recognition) tasks, particularly within domains characterized by complex and fine-grained entity structures, such as tea pest and disease recognition. The boundary prediction module was responsible for identifying entity spans within input text sequences. It employed an attention-based mechanism to dynamically estimate the probability that consecutive tokens belong to the same entity, thereby addressing the challenge of boundary ambiguity. This mechanism facilitated more accurate detection of entity boundaries, which was particularly critical in scenarios involving nested or overlapping entities. The label enhancement module further refines entity recognition by employing a biaffine classifier that jointly models entity spans and their corresponding category labels. This joint modeling approach enabled the capture of intricate interactions between span representations and semantic label information, improving the identification of long or syntactically complex entities. The output of this module was integrated with conditionally normalized hidden representations, enhancing the model's capacity to assign context-aware and semantically precise labels. In order to reduce computational complexity while preserving model effectiveness, the architecture incorporated low-rank linear layers. These were constructed by integrating the adaptive channel weighting mechanism of Squeeze-and-Excitation Networks with low-rank decomposition techniques. The modified layers replace traditional linear transformations, yielding improvements in both efficiency and representational capacity. In addition to model development, a domain-specific NER corpus was constructed through the systematic collection and annotation of entity information related to tea pest and disease from scientific literature, agricultural technical reports, and online texts. The annotated entities in the corpus were categorized into ten classes, including tea plant diseases, tea pests, disease symptoms, and pest symptoms. Based on this labeled corpus, a Chinese NER dataset focused on tea pest and disease was developed, referred to as the Chinese tea pest and disease dataset. [Results and Discussions] Extensive experiments were conducted on the constructed dataset, comparing the proposed method with several mainstream NER approaches, including traditional sequence labeling models (e.g., BiLSTM-CRF), lexicon-enhanced models (e.g., SoftLexicon), and boundary smoothing strategies (e.g., Boundary Smooth). These comparisons aimed to rigorously assess the effectiveness of the proposed architecture in handling domain-specific and structurally complex entity types. Additionally, to evaluate the model's generalization capability beyond the tea disease and pest domain, the study performed comprehensive evaluations on four publicly available Chinese NER benchmark datasets: ResumeNER, WeiboNER, CLUENER, and Taobao. Results showed that the proposed model consistently achieved higher F1-Scores improved across all used datasets: 0.68% on the self-built dataset, 0.29% on ResumeNER, 0.96% on WeiboNER, 0.7% on CLUENER, and 0.5% on Taobao. With particularly notable improvements in the recognition of complex, nested, and long-span entities. These outcomes demonstrate the model's superior capacity for capturing intricate entity boundaries and semantics, and confirm its robustness and adaptability when compared to current state-of-the-art methods. [Conclusions] The study presents a high-performance NER approach tailored to the characteristics of Chinese texts on tea pest and disease. By simultaneously optimizing entity boundary detection and label classification, the proposed method significantly enhanced recognition accuracy in specialized domains. Experimental results demonstrated strong adaptability and robustness of the model across both newly constructed and publicly available datasets, indicating its broad applicability and promising prospects.

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    Estimation of Maize Aboveground Biomass Based on CNN-LSTM-SA
    WANG Yi, XUE Rong, HAN Wenting, SHAO Guomin, HOU Yanqiao, CUI Xitong
    Smart Agriculture    2025, 7 (4): 159-173.   DOI: 10.12133/j.smartag.SA202412004
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    [Objective] Maize is one of the most widely cultivated staple crops worldwide, and its aboveground biomass (AGB) serves as a crucial indicator for evaluating crop growth status. Accurate estimation of maize AGB is vital for ensuring food security and enhancing agricultural productivity. However, maize AGB is influenced by a multitude of dynamic factors, exhibiting complex spatial and temporal variations that pose significant challenges to precise estimation. At present, most studies on maize AGB estimation rely primarily on single-source remote sensing data and conventional machine learning algorithms, which limits the accuracy and generalizability of the models. To overcome these limitations, a model architecture that integrates convolutional neural networks (CNN), long short-term memory networks (LSTM), and a self-attention (SA) mechanism was developed in this research to estimate maize AGB at the field scale. [Methods] The research utilized vegetation indices, crop parameters, and meteorological data that were collected under varying gradient water treatments in the experimental area. First, an optimized CNN-LSTM-SA model was constructed. The model employed two-dimensional convolutional layers to extract both spatial and temporal features, while utilizing max-pooling and dropout techniques to mitigate overfitting. The LSTM module was used to capture temporal dependencies in the data. The SA mechanism was introduced to compute global attention weights, enhancing the representation of critical time steps. Nonlinear activation functions were applied to mitigate multicollinearity among features. A fully connected layer was used to output the estimated AGB values. Second, the Pearson correlation coefficients between influencing factors and maize AGB were analyzed, and the importance of multi-source data was validated. recursive feature elimination (RFE) was used to select the optimal input features. The local interpretable model-agnostic explanations (LIME) method was employed to interpret individual samples. Finally, ablation experiments were conducted to assess the effects of incorporating CNN and SA into the model, with performance comparisons made against random forest (RF) and support vector machine (SVM) models. [Results and Discussions] The correlation analysis revealed that crop parameters exhibited strong correlations with AGB. Among the vegetation indices, the improved normalized difference red edge index (NDREI) demonstrated the highest correlation (r = 0.63). To address multicollinearity issues, the visible atmospherically resistant index (VARI), soil adjusted vegetation index (SAVI), and normalized difference red edge index (NDRE) were excluded from the analysis. The CNN-LSTM-SA model integrated crop parameters, vegetation indices, and meteorological data and initially achieved a coefficient of determination (R2) of 0.89, a root mean square error (RMSE) of 129.38 g/m2, and a mean absolute error (MAE) of 65.99 g/m2. When only vegetation indices and meteorological data were included, the model yielded an R2 of 0.83, an RMSE of 161.36 g/m2, and an MAE of 89.37 g/m2. Using a single vegetation index further reduced model accuracy. Based on multi-source data integration, RFE removed redundant features. After excluding the 2-meter average wind speed, the model reached its best performance with R2 of 0.92, RMSE of 107.53 g/m2, and MAE of 55.19 g/m2. Using the LIME method to interpret feature contributions for individual maize samples, the analysis revealed that during the rapid growth stage, the model was primarily influenced by the current growth status and vegetation indices. For samples in the mid-growth stage, multi-day crop physiological characteristics had a substantial impact on model predictions. In the late growth stage, higher vegetation index values showed a clear suppressive effect on the model outputs. During the mid-growth stage of maize under varying moisture conditions, the model consistently demonstrated heightened sensitivity to low temperatures, moderate humidity levels, and optimal vegetation indices. The CNN-LSTM-SA model demonstrated more consistent fitting performance and accuracy across different growth stages and water conditions compared to the LSTM, LSTM-SA, and CNN-LSTM models. Additionally, it also exceeded the performance of the RF model and the SVM model in all evaluation metrics. [Conclusions] This study leveraged the feature extraction capabilities of CNN, the temporal modeling strength of LSTM, and the dynamic attention mechanism of the SA to enhance the accuracy of maize AGB estimation from a spatiotemporal perspective. The approach not only reduced estimation errors but also improved model interpretability. This research could provide valuable insights and references for the dynamic modeling of crop AGB.

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    Embedded Fluorescence Imaging Detection System for Fruit and Vegetable Quality Deterioration Based on Improved YOLOv8
    GAO Chenhong, ZHU Qibing, HUANG Min
    Smart Agriculture    2025, 7 (5): 146-155.   DOI: 10.12133/j.smartag.SA202505038
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    [Objective] Fresh fruits and vegetables are prone to quality deterioration during storage and transportation due to microbial proliferation and changes in enzyme activity. Although traditional quality detection methods (e.g., physicochemical analysis and microbial culture) offer high accuracy, they are destructive, time-consuming, and require expert operation, making them inadequate for the modern supply chain's demand for real-time, non-destructive detection. While advanced optical detection technologies like hyperspectral imaging provide non-destructive advantages, the equipment is expensive, bulky, and lacks portability. This study aimed to integrate fluorescence imaging technology, embedded systems, and lightweight deep learning models to develop an embedded detection system for fruit and vegetable quality deterioration, addressing the bottlenecks of high cost and insufficient portability in current technologies, and providing a low-cost, efficient solution for non-destructive quality detection of fruits and vegetables. [Methods] An embedded quality detection system based on fluorescence imaging and a ZYNQ platform was developed. The system adopted the Xilinx ZYNQ XC7Z020 heterogeneous SoC as the core controller and used 365 nm, 10 W ultraviolet LED beads as the excitation light source. A CMOS camera served as the image acquisition sensor to capture and process fluorescence images. Algorithmically, an improved, lightweight object detection model based on YOLOv8 was developed. The improved model replaced the original YOLOv8 backbone network with MobileNetV4 to reduce computational load. To further achieve lightweighting, a channel pruning technique based on the batch normalization (BN) layer's scaling factor (γ) was employed. During training, L1 regularization was applied to γ to induce sparsity, after which channels with small γ values were pruned according to a threshold (γ_threshold = 0.01), followed by fine-tuning of the pruned model. Finally, in accordance with the hardware characteristics of the ZYNQ platform, a dynamic 16-bit fixed-point quantization method was adopted to convert the model from 32-bit floating point to 16-bit fixed point, and the FPGA's parallel computing capability was utilized for hardware acceleration to improve inference speed. [Results and Discussions] Grapes and spinach were used as experimental samples in a controlled laboratory setting (26 °C; 20%~40% humidity) over an eight-day storage experiment. Fluorescence images were collected daily, and physicochemical indices were measured simultaneously to construct ground-truth labels (spinach: chlorophyll, vitamin C; grapes: titratable acidity, total soluble solids). K-means clustering combined with principal component analysis (PCA) was used to categorize quality into three levels, "fresh" "sub-fresh" and "spoiled", based on changes in physicochemical indices, and images were labeled accordingly. In terms of system performance, the improved YOLOv8-MobileNetV4 model achieved a mean average precision (mAP) of 95.91% for the three-level quality classification. Ablation results showed that using only the MobileNetV4 backbone or applying channel pruning to the original model each reduced average detection time (by 14.0% and 29.0%, respectively) but incurred some loss of accuracy. In contrast, combining both yielded a synergistic effect: precision reached 97.04%, while recall and mAP increased to 95.24% and 95.91%, respectively. Comparative experiments indicated that the proposed model (8.98 MB parameters) outperformed other mainstream lightweight models (e.g., Faster R-CNN and YOLOv8-Ghost) in mAP and also exhibited faster detection, demonstrating an excellent balance between accuracy and efficiency. [Conclusions] Targeting practical needs in detecting fruit and vegetable quality deterioration, this study proposed and implemented an efficient detection system based on fluorescence imaging and an embedded platform. By integrating the MobileNetV4 backbone with the YOLOv8 detection framework and introducing BN-based channel pruning, the model achieved structured compression and accelerated inference. Experimental results showed that the YOLOv8-MobileNetV4 plus pruning model significantly reduced model size and hardware resource consumption while maintaining detection accuracy, thereby enhancing real-time responsiveness. The system's low hardware cost, compact size, and portability make it a practical solution for rapid, non-destructive, real-time quality monitoring in fruit and vegetable supply chains. Future work will focus on expanding the sample library to include more produce types and mixed deterioration levels and further optimizing the algorithm to improve robustness in complex multi-target scenarios.

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    The Bee Pollination Recognition Model Based On The Lightweight YOLOv10n-CHL
    CHANG Jian, WANG Bingbing, YIN Long, LI Yanqing, LI Zhaoxin, LI Zhuang
    Smart Agriculture    DOI: 10.12133/j.smartag.SA202502033
    Online available: 06 June 2025

    The Lightweight Bee Pollination Recognition Model Based On YOLOv10n-CHL
    CHANG Jian, WANG Bingbing, YIN Long, LI Yanqing, LI Zhaoxin, LI Zhuang
    Smart Agriculture    2025, 7 (3): 185-198.   DOI: 10.12133/j.smartag.SA202503033
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    [Objective] Bee pollination is pivotal to plant reproduction and crop yield, making its identification and monitoring highly significant for agricultural production. However, practical detection of bee pollination poses various challenges, including the small size of bee targets, their low pixel occupancy in images, and the complexity of floral backgrounds. Aimed to scientifically evaluate pollination efficiency, accurately detect the pollination status of flowers, and provide reliable data to guide flower and fruit thinning in orchards, ultimately supports the scientific management of bee colonies and enhances agricultural efficiency, a lightweight recognition model that can effectively overcome the above obstacles was proposed, thereby advancing the practical application of bee pollination detection technology in smart agriculture. [Methods] A specialized bee pollination dataset was constructed comprising three flower types: strawberry, blueberry, and chrysanthemum. High-resolution cameras were used to record videos of the pollination process, which were then subjected to frame sampling to extract representative images. These initial images underwent manual screening to ensure quality and relevance. To address challenges such as limited data diversity and class imbalance, a comprehensive data augmentation strategy was employed. Techniques including rotation, flipping, brightness adjustment, and mosaic augmentation were applied, significantly expanding the dataset's size and variability. The enhanced dataset was subsequently split into training and validation sets at an 8:2 ratio to ensure robust model evaluation. The base detection model was built upon an improved YOLOv10n architecture. The conventional C2f module in the backbone was replaced with a novel cross stage partial network_multi-scale edge information enhance (CSP_MSEE) module, which synergizes the cross-stage partial connections from cross stage partial network (CSPNet) with a multi-scale edge enhancement strategy. This design greatly improved feature extraction, particularly in scenarios involving fine-grained structures and small-scale targets like bees. For the neck, a hybrid-scale feature pyramid network (HS-FPN) was implemented, incorporating a channel attention (CA) mechanism and a dimension matching (DM) module to refine and align multi-scale features. These features were further integrated through a selective feature fusion (SFF) module, enabling the effective combination of low-level texture details and high-level semantic representations. The detection head was replaced with the lightweight shared detail enhanced convolutional detection head (LSDECD), an enhanced version of the Lightweight shared convolutional detection head (LSCD) detection head. It incorporated detail enhancement convolution (DEConv) from DEA-Net to improve the extraction of fine-grained bee features. Additionally, the standard convolution_groupnorm (Conv_GN) layers were replaced with detail enhancement convolution_ groupnorm (DEConv_GN), significantly reducing model parameters and enhancing the model's sensitivity to subtle bee behaviors. This lightweight yet accurate model design made it highly suitable for real-time deployment on resource-constrained edge devices in agricultural environments. [Results and Discussions] Experimental results on the three bee pollination datasets: strawberry, blueberry, and chrysanthemum, demonstrated the effectiveness of the proposed improvements over the baseline YOLOv10n model. The enhanced model achieved significant reductions in computational overhead, lowering the computational complexity by 3.1 GFLOPs and the number of parameters by 1.3 M. The computational cost of the improved model reached 5.1 GFLOPS, and the number of parameters was 1.3 M. These reductions contribute to improved efficiency, making the model more suitable for deployment on edge devices with limited processing capabilities, such as mobile platforms or embedded systems used in agricultural monitoring. In terms of detection performance, the improved model showed consistent gains across all three datasets. Specifically, the recall rates reached 82.6% for strawberry flowers, 84.0% for blueberry flowers, and 84.8% for chrysanthemum flowers. Corresponding mAP50 (Mean Average Precision at IoU threshold of 0.5) scores were 89.3%, 89.5%, and 88.0%, respectively. Compared to the original YOLOv10n model, these results marked respective improvements of 2.1% in recall and 1.7% in mAP50 on the strawberry dataset, 2.0% and 2.6% on the blueberry dataset, and 2.1% and 2.2% on the chrysanthemum dataset. [Conclusions] The proposed YOLOv10n-CHL lightweight bee pollination detection model, through coordinated enhancements at multiple architectural levels, achieved notable improvements in both detection accuracy and computational efficiency across multiple bee pollination datasets. The model significantly improved the detection performance for small objects while substantially reducing computational overhead, facilitating its deployment on edge computing platforms such as drones and embedded systems. This research could provide a solid technical foundation for the precise monitoring of bee pollination behavior and the advancement of smart agriculture. Nevertheless, the model's adaptability to extreme lighting and complex weather conditions remains an area for improvement. Future work will focus on enhancing the model's robustness in these scenarios to support its broader application in real-world agricultural environments.

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    Dynamic Prediction Model of Crop Canopy Temperature Based on VMD-LSTM
    WANG Yuxi, HUANG Lyuwen, DUAN Xiaolin
    Smart Agriculture    2025, 7 (3): 143-159.   DOI: 10.12133/j.smartag.SA202502015
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    [Objective] Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production. This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature. [Methods] A dynamic prediction model for crop canopy temperature was developed based on Long Short-Term Memory (LSTM), Variational Mode Decomposition (VMD), and the Rime Ice Morphology-based Optimization Algorithm (RIME) optimization algorithm, named RIME-VMD-RIME-LSTM (RIME2-VMD-LSTM). Firstly, crop canopy temperature data were collected by an inspection robot suspended on a cableway. Secondly, through the performance of multiple pre-test experiments, VMD-LSTM was selected as the base model. To reduce cross-interference between different frequency components of VMD, the K-means clustering algorithm was applied to cluster the sample entropy of each component, reconstructing them into new components. Finally, the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM, enhancing the model's prediction accuracy. [Results and Discussions] The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) (0.360 1 and 0.254 3 °C, respectively) in modeling different noise environments than the comparator model. Furthermore, the R2 value reached a maximum of 0.994 7. [Conclusions] This model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.

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    U-Net Greenhouse Sweet Cherry Image Segmentation Method Integrating PDE Plant Temporal Image Contrastive Learning and GCN Skip Connections
    HU Lingyan, GUO Ruiya, GUO Zhanjun, XU Guohui, GAI Rongli, WANG Zumin, ZHANG Yumeng, JU Bowen, NIE Xiaoyu
    Smart Agriculture    2025, 7 (3): 131-142.   DOI: 10.12133/j.smartag.SA202502008
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    [Objective] Within the field of plant phenotyping feature extraction, the accurate delineation of small targets boundaries and the adequate recovery of spatial details during upsampling operations have long been recognized as significant obstacles hindering progress. To address these limitations, an improved U-Net architecture designed for greenhouse sweet cherry image segmentation. [Methods] Taking temporal phenotypic images of sweet cherries as the research subject, the U-Net segmentation model was employed to delineate the specific organ regions of the plant. This architecture was referred to as the U-Net integrating self-supervised contrastive learning method for plant time-series images with priori distance embedding (PDE) pre-training and graph convolutional networks (GCN ) skip connection for greenhouse sweet cherry image segmentation. To accelerate model convergence, the pre-trained weights derived from the PDE plant temporal image contrastive learning method were transferred to. Concurrently, the incorporation of a GCN local feature fusion layer was incorporated as a skip connection to optimize feature fusion, thereby providing robust technical support for image segmentation task. The PDE plant temporal image contrastive learning method pre-training required the construction of image pairs corresponding to different phenological periods. A classification distance loss function, which incorporated prior knowledge, was employed to construct an Encoder with adjusted parameters. Pre-trained weights obtained from the PDE plant temporal image contrastive learning method were effectively transferred and and applied to the semantic segmentation task, enabling the network to accurately learn semantic information and detailed textures of various sweet cherry organs. The Encoder module performs multi-scale feature extraction by convolutional and pooling layers. This process enabled the hierarchical processing of the semantic information embedded in the input image to construct representations that progress transitions from low-level texture features to high-level semantic features. This allows consistent extraction of semantic features from images across various scales and abstraction of underlying information, enhancing feature discriminability and optimizing modeling of complex targets. The Decoder module was employed to conduct up sampling operations, which facilitated the integration of features from diverse scales and the restoration of the original image resolution. This enabled the results to effectively reconstruct spatial details and significantly improve the efficiency of model optimization. At the interface between the Encoder and Decoder modules, a GCN layer designed for local feature fusion was strategically integrated as a skip connection, enabling the network to better capture and learn the local features in multi-scale images. [Results and Discussions] Utilizing a set of evaluation metrics including accuracy, precision, recall, and F1-Score, an in-depth and rigorous assessment of the model's performance capabilities was conducted. The research findings revealed that the improved U-Net model achieved superior performance in semantic segmentation of sweet cherry images, with an accuracy of up to 0.955 0. Ablation experiments results further revealed that the proposed method attained a precision of 0.932 8, a recall of 0.927 4, and an F1-Score of 0.912 8. The accuracy of improved U-Net is higher by 0.069 9, 0.028 8, and 0.042 compared to the original U-Net, U-Net with PDE plant temporal image contrastive learning method, and U-Net with GCN skip connections, respectively. Meanwhile the F1-Score is 0.078 3, 0.033 8, and 0.043 8 higher respectively. In comparative experiments against DeepLabV3, Swin Transformer and Segment Anything Model segmentation methods, the proposed model surpassed the above models by 0.022 2, 0.027 6 and 0.042 2 in accuracy; 0.063 7, 0.147 1 and 0.107 7 in precision; 0.035 2, 0.065 4 and 0.050 8 in recall; and 0.076 8, 0.127 5 and 0.103 4 in F1-Score. [Conclusions] The incorporation of the PDE plant temporal image contrastive learning method and the GCN techniques was utilized to develop an advanced U-Net architecture that is specifically designed and optimized for the analysis of sweat cherry plant phenotyping. The results demonstrate that the proposed method is capable of effectively addressing the issues of boundary blurring and detail loss associated with small targets in complex orchard scenarios. It enables the precise segmentation of the primary organs and background regions in sweet cherry images, thereby enhancing the segmentation accuracy of the original model. This improvement provides a solid foundation for subsequent crop modeling research and holds significant practical importance for the advancement of agricultural intelligence.

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    A Transfer Learning-Based Multimodal Model for Grape Detection and Counting
    XU Wenwen, YU Kejian, DAI Zexu, WU Yunzhi
    Smart Agriculture    2025, 7 (4): 174-186.   DOI: 10.12133/j.smartag.SA202504005
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    [Objective] As one of the world's largest cash crops in terms of total production value, grape has a yield whose accurate estimation is crucial for agricultural and economic development. However, at present, grape yield prediction is difficult and costly, detection of green grape varieties with similar colors of grape berries and grape leaves has limitations, and detection of grape bunches with small berries is ineffective. In order to solve the above problems, a multimodal detection framework is proposed based on transfer learning, which aims to realize the detection and counting of different varieties of grapes, so as to provide reliable technical support for grape yield prediction and intelligent management of orchards. [Methods] A multimodal grape detection framework based on transfer learning was proposed. This transfer learning utilized the feature representation capabilities of pretrained models, requiring only a small number of grape images for fine-tuning to adapt to the task. This approach not only reduced labeling costs but also enhanced the ability to capture grape features effectively. The multimodal framework adopted a dual-encoder-single-decoder structure, consisting of three core modules: the image and text feature extraction and enhancement module, the language-guided query selection module, and the cross-modality decoder module. In the feature extraction stage, the framework employed pretrained models from public datasets for transfer learning, which significantly reduced the training time and costs of the model on the target task while effectively improving the capability to capture grape features. By introducing a feature enhancement module, the framework achieved cross-modality fusion effects between grape images and text. Additionally, the attention mechanism was implemented to enhance both image and text features, facilitating cross-modality feature learning between images and text. During the cross-modality query selection phase, the framework utilized a language-guided query selection strategy that enabled the filtering of queries from grape images. This strategy allowed for a more effective use of input text to guide the object in target detection, selecting features that were more relevant to the input text as queries for the decoder. The cross-modality decoder combined the features from grape images and text modalities to achieve more accurate modality alignment, thereby facilitating a more effective fusion of grape image and text information, ultimately producing the corresponding grape prediction results. Finally, to comprehensively evaluate the model's performance, the mean average precision (mAP) and average recall (AR) were adopted as evaluation metrics for the detection task, while the counting task was quantified using the mean absolute error (MAE) and root mean square error (RMSE) as assessment indicators. [Results and Discussions] This method exhibited optimal performance in both detection and counting when compared to nine baseline models. Specifically, a comprehensive evaluation was conducted on the WGISD public dataset, where the method achieved an mAP50 of 80.3% in the detection task, representing a 2.7 percentage points improvement over the second-best model. Additionally, it reached 53.2% mAP and 58.2% mAP75, surpassing the second-best models by 13.4 and 22 percent points, respectively, and achieved an mAR of 76.5%, which was 9.8 percent points increase over the next best model. In the counting task, the method realized a MAE of 1.65 and an RMSE of 2.48, outperforming all other baseline models in counting effectiveness. Furthermore, experiments were conducted using a total of nine grape varieties from both the WGISD dataset and field-collected data, resulting in an mAP50 of 82.5%, an mAP of 58.5%, an mAP75 of 64.4%, an mAR of 77.1%, an MAE of 1.44, and an RMSE of 2.19. These results demonstrated the model's strong adaptability and effectiveness across diverse grape varieties. Notably, the method not only performed well in identifying large grape clusters but also showed superior performance on smaller grape clusters, achieving an mAP_s of 74.2% in the detection task, which was 9.5 percent points improvement over the second-best model. Additionally, to provide a more intuitive assessment of model performance, this study selected grape images from the test set for visual comparison analysis. The results revealed that the model's detection and counting outcomes for grape clusters closely aligned with the original annotation information from the label dataset. Overall, this method demonstrated strong generalization capabilities and higher accuracy under various environmental conditions for different grape varieties. This technology has the potential to be applied in estimating total orchard yield and reducing pre-harvest measurement errors, thereby effectively enhancing the precision management level of vineyards. [Conclusions] The proposed method achieved higher accuracy and better adaptability in detecting five grape varieties compared to other baseline models. Furthermore, the model demonstrated substantial practicality and robustness across nine different grape varieties. These findings suggested that the method developed in this study had significant application potential in grape detection and counting tasks. It could provide strong technical support for the intelligent development of precision agriculture and the grape cultivation industry, highlighting its promising prospects in enhancing agricultural practices.

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    LightTassel-YOLO: A Real-Time Detection Method for Maize Tassels Based on UAV Remote Sensing
    CAO Yuying, LIU Yinchuan, GAO Xinyue, JIA Yinjiang, DONG Shoutian
    Smart Agriculture    2025, 7 (6): 96-110.   DOI: 10.12133/j.smartag.SA202505021
    Abstract365)   HTML29)    PDF(pc) (4491KB)(39)       Save

    [Objective] The accurate identification of maize tassels is critical for the production of hybrid seed. Existing object detection models in complex farmland scenarios face limitations such as restricted data diversity, insufficient feature extraction, high computational load, and low detection efficiency. To address these challenges, a real-time field maize tassel detection model, LightTassel-YOLO (You Only Look Once) based on an improved YOLOv11n is proposed. The model is designed to quickly and accurately identify maize tassels, enabling efficient operation of detasseling unmanned aerial vehicles (UAVs) and reducing the impact of manual intervention. [Methods] Data was continuously collected during the tasseling stage of maize from 2023 to 2024 using UAVs, establishing a large-scale, high-quality maize tassel dataset that covered different maize tasseling stages, multiple varieties, varying altitudes, and diverse meteorological conditions. First, EfficientViT (Efficient vision transformer) was applied as the backbone network to enhance the ability to perceive information across multi-scale features. Second, the C2PSA-CPCA (Convolutional block with parallel spatial attention with channel prior convolutional attention) module was designed to dynamically assign attention weights to the channel and spatial dimensions of feature maps, effectively enhancing the network's capability to extract target features while reducing computational complexity. Finally, the C3k2-SCConv module was constructed to facilitate representative feature learning and achieve low-cost spatial feature reconstruction, thereby improving the model's detection accuracy. [Results and Discussions] The results demonstrated that LightTassel-YOLO provided a reliable method for maize tassel detection. The final model achieved an accuracy of 92.6%, a recall of 89.1%, and an AP@0.5 of 94.7%, representing improvements of 2.5, 3.8 and 4.0 percentage points over the baseline model YOLOv11n, respectively. The model had only 3.23 M parameters and a computational cost of 6.7 GFLOPs. In addition, LightTassel-YOLO was compared with mainstream object detection algorithms such as Faster R-CNN, SSD, and multiple versions of the YOLO series. The results demonstrated that the proposed method outperformed these algorithms in overall performance and exhibits excellent adaptability in typical field scenarios. [Conclusions] The proposed method provides an effective theoretical framework for precise maize tassel monitoring and holds significant potential for advancing intelligent field management practices.

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    Low-Altitude Technology Empowering Smart Agriculture: Technical System, Application Scenarios, and Challenge Recommendations
    LAN Yubin, WANG Chaofeng, SUN Heguang, CHEN Shengde, WANG Guobin, DENG Xiaoling, WANG Yuanjie
    Smart Agriculture    2025, 7 (6): 18-34.   DOI: 10.12133/j.smartag.SA202506025
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    [Significance] Agricultural low altitude agricultural technology, with unmanned aerial vehicles (UAVs) as its primary platform, integrates 5G communication, artificial intelligence, and the Internet of Things to support data acquisition, analysis, and decision making throughout agricultural production. These advances are driving a transition from traditional experience based management toward a model in which data serve as the primary basis for decisions. As low altitude technology continues to advance in communication capacity, payload performance, and onboard processing, agricultural operations are undergoing profound changes. UAVs once used mainly for crop protection spraying have gradually evolved into multifunctional platforms capable of data collection, crop growth monitoring, image interpretation, precise input application, and operational assistance. Supported by a three dimensional integrated framework, which includes vertical integration, horizontal expansion, and spatio-temporal coordination, low altitude systems are reshaping field management structures, operational modes, and decision making processes. This transformation is accelerating the digitalization, networking, and intelligent upgrading of agriculture. The aim is to provide theoretical guidance and technical pathways for the broader application of low altitude technology in agriculture and to support the exploration of sustainable development models and industrial layouts for the low altitude agricultural economy. [Progress] The core contribution of low altitude technology to smart agriculture lies in establishing a complete sensing, decision, execution, and feedback cycle and implementing a four level structure comprising infrastructure, core technologies, application support, and scenario deployment. The infrastructure layer relies on 4G/5G networks, RTK high precision positioning, and ground based sensor systems. Multi source data are acquired through UAV mounted multi-spectral and thermal sensors working in coordination with ground monitoring devices to capture information on crop conditions and field environments. The core technology layer utilizes edge computing, cloud platforms, and analytical models to support growth assessment, pest and disease warnings, and other forms of analysis. At the application layer, UAVs operate in collaboration with ground equipment to implement precise crop protection, seeding, and irrigation, while also extending to field monitoring and agricultural logistics. This paper focuses on agricultural low altitude agricultural technology, summarizes its mechanisms and systematically reviews the associated technical system from the perspectives of operational equipment, low altitude remote sensing and recognition, data processing and analysis, and precision operation and supervision. It further examines key functions enabling agricultural intelligence. Drawing on recent research and representative cases, the paper discusses practical applications in depth. In smart orchards, for example, the South China Agricultural University Smart Patrol system combined with the Lichi Jun model can deliver early pest and disease warnings two to three weeks before outbreak and support yield estimation. In ecological unmanned farms, integrated sky, air, and ground monitoring enables autonomous operation across plowing, planting, management, and harvesting. In production operations, agricultural UAVs have accumulated over 7.5 billion mu (500 million hectare) times of service area globally, covering nearly one third of China's cultivated land area, saving approximately 210 million tons of water, and reducing carbon emissions by approximately 25.72 million tons. In logistics scenarios, transport assisted by UAVs in mountainous orchards improves efficiency more than tenfold while keeping damage rates below three percent. [Conclusions and Prospects] Sensors remain fundamental tools for capturing agricultural information and reflecting crop growth conditions. Developing highly generalizable technical modules helps lower application barriers and improve operational efficiency, while fusing multi scale data partially compensates for the limitations of single source information. Despite rapid progress, the low altitude agricultural economy still faces challenges including technological maturity, application cost, standardization, industrial integration, and workforce development. Based on an analysis of these challenges, this paper proposes building a three dimensional integrated technology framework featuring vertical integration, horizontal expansion, and spatio-temporal coordination; promoting the improvement and unification of technical standards; constructing an integrated industry ecosystem spanning research, manufacturing, application, and service; and strengthening policy support, industry norms, and talent training systems. These measures are expected to accelerate the emergence of new drivers of growth in the low altitude agricultural economy.

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    Corn Borer Pests Infestations Detection Method Using Low-Altitude Close-Range UAV Imagery
    ZHAO Jun, NIE Zhigang, LI Guang, LIU Jiayu
    Smart Agriculture    2025, 7 (6): 111-123.   DOI: 10.12133/j.smartag.SA202505006
    Abstract346)   HTML23)    PDF(pc) (3745KB)(29)       Save

    [Objective] The detection of corn borer infestations is essential for improving corn yield and quality, as corn borer pests pose a significant threat to global corn production. In traditional agricultural practices, identifying corn borer infestations relies on manual field inspections or trapping tools, which are labor-intensive, time-consuming, and difficult to implement over large areas. These methods are further limited by their susceptibility to human error and inability to meet the demands of modern precision agriculture. To address these challenges, a method for detecting corn borer infestations using low-altitude, close-range imagery captured by unmanned aerial vehicles (UAVs) was investigated. By focusing on detecting boreholes rather than insect bodies, this approach overcomes the difficulties of detecting corn borers, which are nocturnal and often concealed within plant tissues, thereby enhancing the applicability of field-based detection and aligning with practical field conditions. [Methods] Based on the YOLOv11 (You Only Look Once v11) object detection algorithm, a model named YOLO-ESN was introduced, for corn borer infestation detection. The YOLO-ESN model was optimized through multiple modifications. In the Backbone, an enhanced lightweight attention (ELA) mechanism was incorporated to increase sensitivity and improve the extraction of small visual features, such as boreholes, by modeling spatial dependencies in horizontal and vertical directions using one-dimensional convolutions. In the Neck, a C3k2-Spatial and channel reconstruction convolution (C3k2-SCConv) module was introduced to reduce the number of model parameters while improving feature fusion efficiency through spatial and channel reconstruction, suppressing redundant information. In the Head, a small-object detection branch, termed the P2 detection head, was added, enabling YOLO-ESN to directly utilize shallow, high-resolution features from early network layers to enhance the detection of fine-grained targets like boreholes. Additionally, a combined loss function of normalized Wasserstein distance (NWD) and efficient intersection over union (EIoU) was employed to optimize bounding box regression accuracy, addressing gradient vanishing issues for small targets and improving target localization stability and robustness. A decision tree algorithm was applied to classify infestation severity levels based on borehole detection results, and heatmaps were generated to visualize the spatial distribution of corn borer infestations across the field. [Results and Discussions] Multiple experiments were conducted using a constructed dataset of corn borer infestation images. The results demonstrated that YOLO-ESN achieved an mAP@50 of 88.6% and an mAP@50:95 of 40.5%, representing an improvement of 7.6 and 4.9 percentage points, respectively, compared to the original YOLOv11 model. The total number of parameters in YOLO-ESN was reduced by 11.52%, contributing to a lighter model suitable for UAV deployment. Ablation studies evaluated individual contributions: incorporating the ELA mechanism alone improved mAP@50 by 0.3 percentage points, and the parameters are reduced by 10.57%; replacing the C3k2 module with C3k2-SCConv reduced parameters by 2.5% while increasing mAP@50 by 0.9 percentage points; adding the P2 detection head enhanced mAP@50 and mAP@50:95 by 4.1 and 1.2 percentage points, respectively; and introducing the NWD+EIoU loss function improved mAP@50 and mAP@50:95 by 1.9 and 1.2 percentage points, respectively. Comparative experiments demonstrate that YOLO-ESN outperforms a range of mainstream object detection models, including Faster R-CNN, SSD, YOLOv8, YOLOv11, and YOLOv12. YOLO-ESN achieves an mAP@50 and an mAP@50:95, surpassing Faster R-CNN by 14.9 and 9.7 percentage points, respectively, and SSD by 17.8 and 11.4 percentage points, respectively. With a compact parameter size of 8.37 M, YOLO-ESN delivers excellent detection accuracy and generalization, striking a strong balance between performance and efficiency. Although its inference speed (32.48 frame/s) was slightly slower than YOLOv12 (75.44 frame/s), it offered a superior trade-off between accuracy and efficiency. These results validated YOLO-ESN as a lightweight, high-performing solution for small object detection tasks, such as dense small targets in remote sensing images. The decision tree algorithm classified infestation severity with high accuracy, achieving F1-Scores of 0.906, 0.803, and 0.842 for mild, moderate, and severe infestations, respectively. Heatmaps generated from borehole detection results enabled spatial visualization of infestation severity, providing a scientific basis for quantitative monitoring and targeted pesticide application in field infestations. [Conclusions] The proposed YOLO-ESN model has more advantages in overall detection accuracy and running speed. While improving the lightweight degree and deployment efficiency of the model, it also shows better recognition ability in small target detection, and can accurately locate the wormhole area on the corn leaf, effectively improving the bounding box regression accuracy and feature extraction efficiency. Compared with the traditional insect recognition method, the use of wormholes as detection objects is more in line with the actual field situation, effectively avoiding the problems of insect occlusion and strong concealment, and improving the availability of field image data and algorithm robustness. The heat map generated by the model detection results can also effectively display the distribution changes of insect pests in farmland, providing a scientific basis for precision pesticide spraying and farmland management. Overall, this study provides an effective solution for the intelligent detection of corn borer pests, has strong versatility and promotion prospects, and can provide strong technical support for precision agriculture and smart farmland management.

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    Forecasting Method for China's Soybean Demand Based on Improved Temporal Fusion Transformers
    LIU Jiajia, QIN Xiaojing, LI Qianchuan, XU Shiwei, ZHAO Jichun, WANG Yigang, XIONG Lu, LIANG Xiaohe
    Smart Agriculture    2025, 7 (4): 187-199.   DOI: 10.12133/j.smartag.SA202505017
    Abstract301)   HTML18)    PDF(pc) (2217KB)(50)       Save

    [Objective] Accurate prediction of soybean demand is of profound practical significance for safeguarding national food security, optimizing industrial decision-making, and responding to fluctuations in international trade. Traditional soybean demand forecasting methods are plagued by inadequacies such as limited capacity to excavate data dimensionality and multivariate interactive features, insufficient ability to capture nonlinear relationships under the coupling of multi-dimensional dynamic factors, and challenges in model interpretability and domain adaptability. These limitations render them incapable of effectively supporting accurate prediction and interpretable analysis of China's soybean demand. When the temporal fusion transformers (TFT) model is applied to forecast China's soybean demand, it exhibits certain constraints in aspects like feature interaction layers and attention weight allocation. Consequently, there is an urgent need to explore a forecasting method based on the improved TFT model to enhance the accuracy and interpretability of soybean demand prediction. [Methods] Drawing on relevant studies, this research applied the deep learning-based TFT model to China's soybean demand forecasting and proposed the MA-TFT (improved TFT model based on MDFI and AAWO) model, which was enhanced through multi-layer dynamic feature interaction (MDFI) and adaptive attention weight optimization (AAWO). Firstly, a dataset for analyzing China's soybean demand, covering eight dimensions: consumption, production, trade, inventory, market, economy, policy, and international factors, was collated. This dataset, encompassing 4 652 relevant indicators spanning from 1980 to 2024, was subjected to data cleaning, transformation, augmentation, and feature engineering. The training, validation, and test sets for the model were constructed using the rolling window method. Secondly, based on the architecture of the TFT model for China's soybean demand forecasting, a multi-layer dynamic feature interaction module and an adaptive attention weight optimization strategy were designed. Additionally, the model's loss function, training strategy, and Bayesian hyperparameter tuning method were formulated, and the model performance evaluation metrics were determined. Subsequently, experiments were designed to compare the prediction performance of the MA-TFT model with that of the autoregressive integrated moving average model (ARIMA), the long short-term memory (LSTM) model, and the original TFT model. Ablation experiments on the MDFI and AAWO modules were conducted separately. The SHapley Additive exPlanations (SHAP) tool was employed for interpretability analysis to identify key feature variables influencing China's soybean demand and their interaction relationships. Error analysis was performed between the predicted and actual values of China's historical soybean demand, and a comparative analysis of the predicted soybean demand in China from 2025 to 2034 was carried out. [Results and Discussions] The mean squared error (MSE) and mean absolute percentage error (MAPE) of the MA-TFT model were 0.036 and 5.89%, respectively, with a coefficient of determination R2 of 0.91, all of which outperformed those of the comparative models, namely ARIMA (1,1,1), LSTM, and TFT. Compared with the benchmark TFT model, the root mean square error (RMSE) and MAPE of the MA-TFT model decreased cumulatively by 21.84% and 3.44%, respectively. These results indicated that the MA-TFT model, as an improved version of TFT, could capture complex relationships between features and enhance prediction performance and accuracy. Interpretability analysis using the SHAP tool revealed that the MA-TFT model exhibited high stability in explaining key feature variables affecting China's soybean demand. It was projected that China's soybean demand would reach 117.99 million tons, 110.33 million tons, and 113.78 million tons in 2025, 2030, and 2034, respectively. [Conclusions] The MA-TFT model, developed by improving the TFT model, provides an innovative solution to address the practical issues of insufficient accuracy and poor interpretability in existing soybean demand forecasting methods. It also offers valuable references for method optimization and application in time series forecasting of other bulk agricultural products.

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    Intelligent Inspection Path Planning Algorithm for Large-Scale Cattle Farms
    CHEN Ruotong, LIU Jifang, ZHANG Zhiyong, MA Nan, WEI Peigang, WANG Yi
    Smart Agriculture    DOI: 10.12133/j.smartag.SA202504004
    Online available: 12 June 2025

    Intelligent Q&A Method for Crop Pests and Diseases Using LLM Augmented by Adaptive Hybrid Retrieval
    YANG Jun, YANG Wanxia, YANG Sen, HE Liang, ZHANG Di
    Smart Agriculture    DOI: 10.12133/j.smartag.SA202506026
    Online available: 09 October 2025

    ReG-RAG: A Large Language Model-based Question Answering Framework with Query Rewriting and Knowledge Graph Enhancement
    LI Xiaoyu, ZHANG Jiayi, ZHANG Haitao, NIE Xiaoyi
    Smart Agriculture    DOI: 10.12133/j.smartag.SA202507011
    Online available: 16 October 2025

    Robust UAV-Based Method for Peanut Plant Height Estimation Using Bare-Soil Invariant Constraints
    SONG Mingxuan, BAI Bo, YANG Juntao, ZHANG Yutao, LI Sa, LI Zhenhai, WAN Shubo, LI Guowei
    Smart Agriculture    2025, 7 (6): 124-135.   DOI: 10.12133/j.smartag.SA202509029
    Abstract205)   HTML2)    PDF(pc) (3004KB)(11)       Save

    Objective Peanuts plant height is a key structural trait for assessing crop growth and nitrogen response. Accurate and efficient height acquisition is essential for monitoring canopy vigor, supporting genotype selection, and enabling precision management. However, conventional ground control point (GCP)-based methods require substantial field deployment and are highly sensitive to local misregistration between multi-temporal digital surface models (DSMs) and digital elevation models (DEMs). In low-stature, prostrate peanut canopies on uneven terrain, such residual elevation errors propagate directly into the canopy height model, severely reducing estimation accuracy. To overcome these limitations, a robust unmanned aerial vehicle (UAV)-based method is developed for peanut plant height estimation using bare-soil invariant constraints. The workflow incorporates crop-mask-assisted fine registration to optimize DSM-DEM alignment and eliminates the need for dense GCP distribution. Methods Field experiments were conducted at a peanut experimental station in Wangbian community, Ningyang county, Tai'an city, Shandong Province, China, using multiple UAV platforms (DJI Mavic 3 Multispectral and DJI MATRICE 350 RTK equipped with a DJI Zenmuse P1 camera), two growth stages (42 d and 49 d after sowing, DAS), and two nitrogen fertilization levels (high nitrogen and low nitrogen). To validate the peanut plant height estimates, representative plants in each plot were selected before and after each UAV image acquisition, and manual measurements from the ground surface to the canopy apex were recorded as the reference plant height. High-resolution digital orthomosaic (DOM) images were then generated from the UAV data, and peanut canopy regions were extracted using the excess green (ExG) index. To mitigate threshold instability caused by variable illumination and soil background conditions, a fixed empirical threshold was combined with an adaptive strategy that integrated Otsu's between-class variance method and the median absolute deviation (MAD), thereby ensuring robust canopy segmentation across growth stages and nitrogen treatments. After canopy extraction, the peanut canopy mask derived from the DOM was used to remove corresponding pixels from the DSM on a per-pixel basis. The DSMs with and without canopy points were then separately used for 3D reconstruction, yielding a canopy point cloud and a bare-soil point cloud. This bare-soil point cloud and a bare-soil DSM acquired before crop emergence (used as the DEM reference) were jointly input into the iterative closest point (ICP) algorithm to solve for a three-dimensional rigid transformation matrix. The resulting matrix was used to jointly optimize translations and rotations along the X, Y, and Z directions. It was applied uniformly to the DSM containing peanut canopy points, thereby achieving fine-scale alignment between the DSM and DEM at the block level. Following registration, the DEM was used as the ground reference, and the canopy height model was constructed by differencing the DSM and DEM pixel by pixel. The 95th percentile (P95) of canopy height within each plot, derived from the canopy height histogram, was used as the representative plant height to reduce the influence of local noise on the statistics. Results and Discussions The results showed that varying the ExG threshold among 0.05, 0.10 and 0.15 had only a limited effect on overall plant height estimation accuracy, with the best performance observed at 0.10. At this threshold, the Mavic 3 platform achieved an R2 of 0.864 7 and a root-mean-square error (RMSE) of 2.57 cm. In contrast, the P1 platform achieved an R2 of 0.918 6 and an RMSE of 2.05 cm, indicating that the proposed threshold selection strategy provided a good balance between accuracy and robustness. Error analysis across different canopy-height percentiles showed that, as the percentile increased from P90 to P99, R2 and RMSE exhibited a typical concave pattern, first improving and then degrading. Among these percentiles, P95 yielded the highest R2 and the lowest RMSE, representing the best trade-off between noise suppression and canopy-top information retention; therefore, P95 was adopted as the representative plant height for this method. Under the P95-based definition of plant height, the traditional GCP method produced R2 values of only 0.592 3-0.669 9 and RMSE values of 4.60~4.94 cm, and the "GCP+ICP" workflow, in which canopy points were not removed prior to ICP registration, was most strongly affected by noise in the point clouds, with R2 dropping below 0.3 in some cases. In contrast, the proposed method maintained R2 values of 0.864 7~0.918 6 and RMSE values of 2.05~2.57 cm across both platforms, markedly improving the agreement between estimated and measured plant height relative to the traditional GCP-based approach. Further platform-specific analysis showed that, owing to its higher spatial resolution, the P1 platform reconstructed a more complete canopy-top structure and yielded better plant height estimates than the Mavic 3 platform at each growth stage. Nevertheless, when combined with the proposed plant height extraction workflow, the Mavic 3 platform still achieved reliable performance (R2 > 0.817 7) in regions with different nitrogen contents, confirming the method's multi-platform applicability. From the perspective of canopy cover and nitrogen level, as the crop progressed from 42 to 49 DAS, the peanut canopy gradually approached full closure, the proportion of high-value pixels in the canopy height model increased, the canopy-top point cloud in the DSM became more continuous, and plant height estimation accuracy improved accordingly. Under high nitrogen treatment, the canopy was denser and structurally more complete than under low nitrogen treatment, resulting in slightly higher R2 and slightly lower RMSE on both platforms; however, these differences remained within a controllable range, demonstrating that the bare-soil-based registration workflow was robust to fertility differences and that the proposed method was stable and transferable across growth stages and fertility conditions. Conclusions Overall, the proposed method for estimating peanut plant height substantially alleviates the constraints imposed by the misregistration of residual DSM and DEM on plant height inversion for low-stature, prostrate crops. It achieves centimetre-level accuracy for plant height retrieval across platforms and nitrogen treatments. By significantly reducing the dependence on densely distributed GCPs and offering a simple, reproducible, and low-cost processing pipeline, the method provides a scalable technical route for monitoring peanut nitrogen responses, deriving high-throughput agronomic structural traits, and measuring plant height in other low-stature, prostrate crops.

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    Smart Agriculture    2025, 7 (2): 0-0.  
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    Remote Sensing Approaches for Cropland Abandonment Perception in Southern Hilly and Mountainous Areas of China: A Review
    LONG Yuqiao, SUN Jing, WEN Yanru, WANG Chuya, DONG Xiuchun, HUANG Ping, WU Wenbin, CHEN Jin, DING Mingzhong
    Smart Agriculture    2025, 7 (6): 58-74.   DOI: 10.12133/j.smartag.SA202505022
    Abstract186)   HTML14)    PDF(pc) (4023KB)(19)       Save

    [Significance] Cropland abandonment in hilly and mountainous regions is a pronounced manifestation of land‐use marginalization, with profound implications for both regional food security and ecosystem service provision. In southern China, this issue is particularly acute due to a confluence of factors including early and rapid urbanization, rugged topographic relief, complex multi‐cropping systems, and substantial rural‐to‐urban labor migration, which have driven widespread abandonment of steep, fragmented terraces. This trend presents a profound dual dilemma: On one hand, the cessation of cultivation diminishes local grain production capacity, amplifies pressure on existing cropland, and threatens national food supplies; On the other hand, the secondary succession of spontaneous vegetation on these deserted parcels offers significant carbon sequestration potential and contributes to biodiversity recovery. Yet, accurately mapping these spatio-temporal patterns is severely hampered by persistent cloud cover and the landscape's complexity. This leaves decision-makers without the timely, high‐resolution maps needed to track abandonment dynamics, uncover their socioeconomic and environmental drivers, and craft land-use policies that holistically balance agricultural output, carbon storage, and landscape resilience. [Progress] Drawing from literature published since 2014, this paper systematically reviews remote sensing‐based methods for cropland abandonment, revealing a clear developmental trajectory. Methodologically, the approaches have evolved along two parallel paths. First, the monitoring paradigm has shifted from early "state comparison" methods, such as post-classification comparison of discrete multi-temporal images, to modern "process tracking" approaches. These leverage dense time series, utilizing phenology‐aware algorithms such as LandTrendr and BFAST to identify abrupt or gradual breaks in the vegetation trajectory, thus capturing the dynamics of abandonment and distinguishing it from short-term fallows. Second, the identification algorithm has progressed from traditional machine learning classifiers and object-based image analysis (OBIA), which depend on hand‐crafted features, towards sophisticated deep learning frameworks capable of automatically learning complex spatio-temporal signatures. Concurrently, data pre-processing techniques have advanced significantly, with harmonic analysis, Savitzky-Golay filtering, and the integration of Synthetic Aperture Radar (SAR) data now routinely applied to reconstruct continuous, high-quality time series. Furthermore, this review provides a critical synthesis of common methodological issues, focusing on the spatio-temporal representativeness bias in ground validation samples and the multiple sources of uncertainty stemming from cloud cover, mixed pixels, and phenological variability. [Conclusions and Prospects] Despite considerable advances, persistent challenges continue to limit operational monitoring. Looking forward, the field must evolve from descriptive mapping toward a truly predictive and decision‐ready framework. This transformation hinges on five interlinked frontiers. First, it requires forging the seamless integration of diverse data streams: Fusing optical imagery, radar backscatter, and terrain models within cloud computing environments to yield uninterrupted, high‐resolution time series that capture both abrupt and gradual land‐use changes. Second, it necessitates the establishment of an extensive, stratified ground‐truth network; by systematically sampling high-risk, transitional, and reference plots and collecting synchronized measurements, researchers can iteratively recalibrate classification models and improve their resilience across the region's landscape mosaic. Third, on the algorithmic frontier, hybrid approaches that embed expert‐defined phenological rules within deep learning architectures offer a promising path to robustly disentangle permanent abandonment from temporary fallows and to quantify a continuous "abandonment intensity". Fourth, the deployment of fully automated and reproducible processing pipelines on cloud platforms like Google Earth Engine will democratize access to near-real‐time monitoring and enhance reproducibility through open-source workflows. Finally, anchoring detection within dynamic simulation frameworks (e.g., agent‐based models) driven by historical trajectories and key drivers will allow for the projection of future abandonment of "hotspots". Layering these projections with multi‐criteria risk assessments will yield spatially explicit risk maps to guide precision interventions—such as targeted recultivation subsidies or ecological restoration efforts—enabling sustainable land stewardship that simultaneously safeguards food security and enhances ecosystem resilience.

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    Parcel-Scale Crop Distribution Mapping Based on Stacking Ensemble Learning
    XIE Wenhao, ZHANG Xin, DONG Wen, ZHENG Yizhen, CHENG Bo, TU Wenli, SUN Fengqing
    Smart Agriculture    2025, 7 (6): 196-209.   DOI: 10.12133/j.smartag.SA202509003
    Abstract179)   HTML4)    PDF(pc) (8396KB)(16)       Save

    [Objective] With the widespread availability of high-resolution and multi-source remote sensing data, remote sensing-based crop classification has played an increasingly vital role in agricultural monitoring, yield estimation, and land use management. However, traditional pixel-level classification methods often struggle to achieve stable, high-precision classification under conditions of intra-plot heterogeneity, spectral confusion, and noise interference.Therefore, this study aimed to improve parcel-level crop classification accuracy and spatial consistency by constructing a multi-source feature fusion and ensemble learning framework, which exploits complementary spectral, spatial, temporal, and productivity characteristics to enhance robustness and generalization in multi-crop classification tasks. [Methods] To enhance field-level classification accuracy and spatial consistency, a crop classification method integrating field-scale feature extraction, feature selection, and Stacking ensemble learning was proposed and validated. This approach aimed to fully leverage the complementarity of spectral, spatial, and temporal information through feature engineering and model fusion. The study area was located in Feicheng city, Shandong province. The data included multi-temporal Sentinel-2 optical imagery, Sentinel-1 SAR data, Gaofen remote sensing imagery, and parcel vector samples with crop_type attributes. All imagery underwent radiometric and atmospheric correction, projection registration, and cropping during preprocessing to ensure spatial consistency and temporal correspondence across sensors. The dataset was constructed at the field level, comprising 3 200 fields for model training and independent validation. This study systematically constructed four types of meta-features on the plot scale: raw bands and vegetation indices; spatial meta-features, including texture, morphology, and structural indicators calculated from high-resolution imagery to reflect internal spatial heterogeneity; temporal sequence meta-features, extracting vegetation indices, backscatter, and harmonic/temporal statistics from multi-temporal optical and SAR imagery to characterize crop growth cycles; crop primary productivity features to highlight differences in carbon fixation and biomass accumulation among crops. Subsequently, for the high-dimensional, multi-source feature set, a combined strategy of LightGBM and recursive feature elimination (RFE) was employed for feature importance assessment and selection. This retained a subset of features most critical to classification, enhancing model generalization and computational efficiency. Within the classification framework, a Stacking-based ensemble learning model was constructed. Base learners included random forest (RF), eXtreme gradient boosting (XGB), support vector machine (SVM), gradient boosting (GB), categorical boosting (CatBoost), adaptive boosting (ADA), back propagation (BP), K-nearest neighbors (KNN), and light gradient boosting machine (LightGBM). These base models learn and represent plot features from distinct perspectives, fully exploring nonlinear relationships among spectral, spatial, and temporal characteristics. During the meta-learner selection phase, to compare the impact of different feature fusion strategies on classification performance, XGBClassifier, LightGBMClassifier, MLPClassifier, and LogisticRegression were selected as meta-learners for experimental comparison. By contrasting the classification outcomes of different meta-models under the same base model outputs, their contributions to improving feature fusion accuracy and stability differences were analyzed. During model training, hierarchical cross-validation was employed to mitigate bias caused by class imbalance. Overall accuracy (OA), Kappa coefficient, and F1-Score served as primary evaluation metrics, while recall and precision rates for each crop category underwent systematic analysis. [Results and Discussions] The findings indicated that feature selection significantly impacted classification performance. By integrating LightGBM with feature selection strategies, a subset of 102 optimal features was identified. This subset included gross primary production (GPP), spectral features, vegetation indices, textural features, temporal features, and harmonic features. This approach effectively mitigated feature redundancy and multicollinearity issues, enhancing model stability and generalization capability. Among these, GPP-related features and vegetation indices from key growth stages demonstrated high discriminative power in distinguishing crop categories, fully reflecting the close coupling between remote sensing features and crop phenological information. The Stacking ensemble strategy demonstrated outstanding classification performance. Among various meta-learners, the Stacking model with XGBClassifier as the final learner achieved the highest classification accuracy (OA = 95.66%, Kappa = 0.900 6), showcasing exceptional ensemble generalization capability. It performed particularly well in identifying major crops like maize while maintaining good adaptability for less common crops. The method's advantage extended beyond accuracy gains to its comprehensive integration of complementary spectral, temporal, and spatial feature processing capabilities across base learners. The meta-learner adaptively synthesized multi-model outputs, enhancing classification stability and spatial consistency. Compared to traditional pixel-level classification followed by parcel reclassification, direct feature extraction and classification based on vector parcels effectively avoided edge blending and noise interference inherent in pixel-level methods, significantly improving parcel recognition stability and accuracy. Experimental results demonstrated that parcel-level classification outperformed pixel-level strategies in overall accuracy and Kappa coefficient, with superior spatial consistency and robustness in classification outcomes. [Conclusions] The "optimal feature subset + Stacking ensemble learning + parcel-level classification" method developed in this research demonstrates outstanding accuracy and stability in multi-source remote sensing crop identification, providing an efficient and feasible technical pathway for parcel-level classification in complex agricultural landscapes. Future work will integrate high-resolution time-series data with deep learning models to further enhance the method's cross-regional adaptability and crop monitoring capabilities.

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    Physics-Constrained PROSAIL-cGAN Approach for Spectral Sample Augmentation and LAI Inversion of Winter Wheat
    LU Yihang, DONG Wen, ZHANG Xin, YAN Ruoyi, ZHANG Yujia, TANG Tao
    Smart Agriculture    2025, 7 (6): 149-160.   DOI: 10.12133/j.smartag.SA202508026
    Abstract172)   HTML4)    PDF(pc) (1770KB)(14)       Save

    Objective The leaf area index (LAI) is a key biophysical parameter that reflects the canopy structure and photosynthetic capacity of crops. However, the inversion of winter wheat LAI from remote sensing data is often constrained by the limited availability of field measurements, leading to insufficient model generalization. Although radiative transfer model (RTM)-based simulations can expand the sample size, discrepancies between simulated and observed spectra persist due to simplified canopy and soil parameterizations. Conversely, purely data-driven generative models such as generative adversarial networks (GANs) can enhance sample diversity but often produce physically inconsistent samples in the absence of biophysical constraints. To address these issues, a physics-constrained PROSAIL-cGAN (conditional generative adversarial network) spectral sample augmentation method was proposed that integrated the PROSAIL model with cGAN to improve the accuracy and robustness of LAI inversion under small-sample conditions, generate physically realistic spectral-parameter pairs and provide reliable data support for remote sensing-based monitoring of winter wheat growth. Methods The study area was located in Zouping city, Shandong Province, a major winter wheat production region within the Huang-Huai-Hai Plain. A total of 133 field samples were collected during the jointing stage in April 2025 using an LAI-2200C canopy analyzer, with synchronous canopy spectra acquired. A Sentinel-2A Level-2A image from April 15, 2025, served as the remote sensing source, comprising 13 bands resampled to a spatial resolution of 10 m. The dataset was divided into training (70%) and validation (30%) subsets, with LAI values ranging from 1.646 to 7.505. The proposed method combined the PROSAIL radiative transfer model with a conditional GAN framework. First, PROSAIL was employed to simulate canopy reflectance and corresponding biophysical parameters, including chlorophyll content (Cab), carotenoid content (Car), brown pigment content (Cbrown), equivalent water thickness (Cw), dry matter content (Cm), LAI, and leaf inclination distribution (LIDFa). A multi-layer perceptron (MLP) surrogate model was then trained to approximate the forward mapping of PROSAIL, enabling differentiability for integration with deep learning architectures. The cGAN generator received random noise and physical parameters as conditional inputs to produce corresponding canopy reflectance, while the discriminator jointly evaluated authenticity and physical consistency. During adversarial training, physical constraints were incorporated into the generator's loss function to ensure biophysical realism. The generated samples were subsequently filtered based on parameter ranges and discriminator confidence scores. Kernel density overlap between real and generated LAI distributions was used to quantify their statistical consistency. Finally, the enhanced dataset was used to train random forest (RF) and extreme gradient boosting (XGBoost) regression models for the LAI inversion. Model performance was assessed using the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), and compared with three baselines: 1) field-measured modeling, 2) the PROSAIL lookup table (LUT) method, and 3) cGAN-only augmentation. Results and Discussions The surrogate MLP model accurately reproduced PROSAIL-simulated spectra, achieving R2 0.817, RMSE 0.008 5, and MAE 0.005 5, confirming its feasibility as a differentiable physical proxy. The cGAN-based augmentation achieved a LAI distribution overlap of 0.806 with the measured samples, whereas the PROSAIL-cGAN improved the overlap to 0.827, demonstrating enhanced physical realism and sample diversity. Model comparisons revealed substantial differences in performance. The LUT-based inversion yielded only R2 0.353 0 and RMSE 1.284 0, reflecting its limited adaptability to spectral heterogeneity. Direct regression using field data improved accuracy (R2=0.680 1 for XGBoost and 0.648 8 for RF). Incorporating cGAN-generated samples further enhanced model accuracy (R2 0.745 0 for RF and 0.739 0 for XGBoost). The PROSAIL-cGAN-enhanced RF model achieved the best overall performance, with R2 0.848 8, RMSE 0.540 9, and MAE 0.293 7. The sample-size sensitivity analysis demonstrated that as the number of field samples increased from 27 to 106, R2 improved from 0.546 2 to 0.848 8 and RMSE decreased from 1.024 3 to 0.540 9. When the sample size exceeded 79, model performance stabilized, indicating strong robustness. Spatial mapping results showed that LAI values were higher in the central and northern regions (4~7) and lower in the southern mountainous areas (1.5~4), consistent with variations in soil fertility and field management practices. These findings validate the model's applicability for regional-scale monitoring of crop growth. Conclusions This study developed a physics-constrained PROSAIL-cGAN spectral sample augmentation method for winter wheat LAI inversion. By integrating a radiative transfer model, a conditional generative network, and a differentiable surrogate, the method effectively generated physically consistent and diverse spectral-parameter samples under small-sample conditions. The PROSAIL-cGAN-based RF model achieved a relatively high inversion accuracy, outperforming traditional LUT and field-only approaches. The proposed method successfully mitigated small-sample limitations, ensured physical interpretability, and improved model generalization. It provides a robust framework for the remote sensing inversion of crop canopy parameters, supporting precision agriculture and dynamic monitoring of crop growth. Future work will focus on optimizing sample generation strategies, integrating multi-temporal satellite data and additional physiological parameters, and coupling with deep or semi-supervised learning techniques to further enhance scalability and applicability across crops and regions.

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    Method for Estimating Leaf Area Index of Winter Rapeseed Based on Fusion of Vegetation Indices and Texture Features
    LIU Jie, GUO Jiaxin, ZHANG Jiahao, ZHANG Bingchao, XIONG Jie, CAO Jianpeng, WU Shangrong, DENG Yingbin, CHEN Guipeng
    Smart Agriculture    2025, 7 (6): 161-173.   DOI: 10.12133/j.smartag.SA202507018
    Abstract166)   HTML2)    PDF(pc) (1434KB)(15)       Save

    [Objective] Leaf area index (LAI) is a vital agronomic parameter that reflects the structure of crop canopies, photosynthetic capacity, and population growth status. It holds significant importance for the precision cultivation and management of winter oilseed rape. Traditional methods for measuring LAI, such as destructive sampling or the use of costly instruments, are often constrained by low efficiency, high costs, and limited adaptability. In contrast, unmanned aerial vehicle (UAV) remote sensing technology offers a novel approach to rapid and non-destructive LAI monitoring due to its advantages in high resolution and flexibility. However, reliance solely on spectral vegetation indices (VIs) frequently results in saturation phenomena at elevated LAI levels, complicating accurate representation of complex canopy structures. Consequently, this study aims to investigate the integration of vegetation indices with texture features (TFs) using machine learning techniques to enhance the estimation accuracy of LAI throughout the entire growth cycle of winter oilseed rape. [Methods] The research was conducted at an experimental site in Gao'an city, Jiangxi province, where 81 plots were established with varying sowing dates, densities, and fertilization treatments. These plots encompassed four critical growth stages: seedling, bud elongation, flowering, and pod development. A DJI Phantom 4 RTK multispectral UAV was employed to acquire image data, while ground-truth LAI data were concurrently collected using an LAI-2200C plant canopy analyzer, resulting in a total of 324 valid samples. From the multispectral imagery obtained, eight vegetation indices, namely normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), normalized difference red edge (NDRE), enhanced vegetation index (EVI), green normalized difference vegetation index (GNDVI), simple ratio (SR), difference vegetation index (DVI), and canopy infrared reflectance estimate (CIRE), were derived alongside reflectance values from five spectral bands: blue, green, red, red-edge, and near-infrared. Additionally, 40 texture features were extracted based on the Gray-Level Co-occurrence Matrix. To select the ten most representative features with minimal redundancy among these variables, the minimum redundancy maximum relevance (mRMR) algorithm was utilized. Subsequently, three machine learning algorithms, multiple linear regression (MLR), extreme gradient boosting (XGBoost), and support vector machine regression (SVR), were applied to develop models for estimating LAI. To assess model generalizability and mitigate overfitting risks during evaluation processes, a 3-fold GroupKFold cross-validation approach was implemented to ensure that samples originating from the same plot remained intact between training and testing sets. The performance of each model was rigorously evaluated using several metrics, including the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). [Results and Discussions] The results indicated that the LAI of winter oilseed rape exhibited a dynamic trend throughout its growth cycle, characterized by being "low at the seedling stage, high at the bud elongation stage, decreasing at the flowering stage, and dropping again at the pod stage". The LAI values ranged from 0.90 to 6.39, with a uniform sample distribution observed within each developmental phase. Most vegetation indices and texture features demonstrated a highly significant correlation with LAI (P < 0.001), with the SR and near-infrared entropy (NIR-entropy) exhibiting the strongest correlation (r = 0.81). In terms of feature selection, vegetation indices such as SR, CIRE, and NDRE along with texture features like NIR-entropy and G-variance maintained high selection frequencies among the top ten mRMR-selected features. This indicated their stable contribution to model construction. Regarding model performance, the fused vegetation and texture features (VTFs) model outperformed all other models evaluated; specifically, the VTFs-SVR model achieved superior estimation accuracy across the entire growth cycle (R2=0.90, RMSE=0.38, MAE=0.27). When compared to models utilizing only vegetation indices or solely texture features, the fused model demonstrated particularly enhanced performance during high-coverage stages such as bud elongation, effectively addressing issues related to spectral saturation. Residual analysis further confirmed that the VTFs model exhibited a more concentrated residual distribution, indicating significantly greater prediction stability than single-feature models. [Conclusions] The fusion of vegetation indices and texture features extracted from UAV-based multispectral imagery, combined with machine learning modeling, particularly the SVR algorithm, enabled high-accuracy, non-destructive estimation of LAI throughout the entire growth cycle of winter oilseed rape. Texture features effectively supplemented canopy structural information, showing strong complementarity, especially during high-LAI stages where spectral data is prone to saturation. Through mRMR feature selection and group-based cross-validation, the model demonstrated good generalizability and practical application potential. This method could provide reliable technical support for monitoring winter oilseed rape growth status and precision agriculture management. Future research would further incorporate canopy structural parameters or multi-temporal features to enhance the model's estimation capability during stages dominated by non-leaf organs.

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    Construction and Evaluation of Lightweight and Interpretable Soybean Remote Sensing Identification Model
    WANG Yinhui, ZHAO Anzhou, LI Dan, ZHU Xiufang, ZHAO Jun, WANG Ziqing
    Smart Agriculture    2025, 7 (6): 136-148.   DOI: 10.12133/j.smartag.SA202508025
    Abstract164)   HTML7)    PDF(pc) (7016KB)(16)       Save

    Objective Soybean stands as one of the most crucial global crops, serving as a vital source of plant-based protein and vegetable oil while playing an indispensable role in sustainable agricultural systems and global food security. Accurate and timely mapping of soybean cultivation areas is essential for agricultural monitoring, policy-making, and precision farming. However, existing remote sensing methods for soybean identification, such as threshold-based approaches, traditional machine learning, and deep learning, often face challenges related to model complexity, computational efficiency, and interpretability. These limitations collectively highlight the pressing need for a methodological solution that maintains classification accuracy while simultaneously offering computational efficiency, operational simplicity, and interpretable results, a balance crucial for effective agricultural monitoring and policy-making. To address these limitations, a lightweight and interpretable soybean mapping framework was proposed based on Sentinel-2 imagery and a binary logistic regression model in this method. Methods Six representative agricultural regions within the primary U.S. soybean production belt were selected to capture the diversity of cultivation practices and environmental conditions across this major production area. The analysis utilized the complete growing season (April-October) Sentinel-2 satellite imagery from 2021 to 2023. The USDA's cropland data layer served as reference data for model training and validation, benefiting from its extensive ground verification and statistical rigor. All Sentinel-2 images undergo rigorous preprocessing, including atmospheric correction, cloud and shadow masking with the scene classification layer, and spatial subsetting to the regions of interest. The Jeffries-Matusita distance was employed as a quantitative metric to objectively identify the optimal temporal window for soybean discrimination. This statistical measure evaluated the separability between soybean and other major crops across the growing season, with calculations performed on 10 d composite periods to ensure data quality and temporal consistency. The analysis revealed that late July to mid-September (Day of Year 210-260) provided maximum spectral separability, corresponding to the soybean's critical reproductive stages (pod setting and filling) when its spectral signature becomes most distinct from other crops, particularly in short-wave infrared regions sensitive to canopy structure and water content. Within this optimally identified window, a binary logistic regression model was implemented that treated soybean identification as a probabilistic classification problem. The model was trained using spectral features from the optimal period through maximum likelihood estimation, creating a computationally efficient framework that required optimization of only a limited number of parameters while maintaining physical interpretability through explicit feature coefficients. Results and Discussions The comprehensive evaluation showed that the integrated approach balanced classification performance and operational practicality optimally. The temporal optimization identified late July to mid-September as the peak discriminative period, which matches soybean's reproductive phenological stages (when its canopy spectral characteristics differ most from other crops). This finding was consistent across three study years and multiple regions, verifying the robustness of the data-driven window selection. The binary logistic regression model, trained on features from this optimal period, performed excellently: In the 2022 model construction region, it achieved 0.90 overall accuracy and 0.79 Kappa coefficient. When applied to independent validation regions in the same year, it maintained strong performance (0.88 overall accuracy, 0.76 Kappa) without region-specific parameter adjustments, demonstrating outstanding spatial transferability. Temporal validation further confirmed the model's robustness: Across the 2021 to 2023 study period, it maintained consistent performance across all regions, with an average accuracy of 0.87 and Kappa of 0.76. This inter-annual stability is notable, despite potential variations in annual weather, management practices, and planting schedules, and highlights the advantage of basing the model on a stable phenological period rather than fixed calendar dates. The model's lightweight architecture offered practical benefits: Compared with complex ensemble or deep learning methods, it only requires optimizing a limited number of parameters. This parsimonious structure enhances computational efficiency, enabling rapid training and deployment over large areas while reducing reliance on extensive labeled datasets—a key advantage in regions lacking sufficient ground truth data. Beyond accuracy and efficiency, the model exhibited exceptional interpretability via its probabilistic framework and transparent feature weighting. Coefficient analysis provided quantifiable insights into feature contributions, revealing that short-wave infrared bands and specific vegetation indices had the highest discriminative power during the optimal temporal window. Conclusions An effective soybean mapping approach that balances accuracy with operational practicality through the strategic combination of temporal optimization and binary logistic regression was proposed. The method offers a viable solution for operational agricultural monitoring, especially in resource-constrained environments. Future work can enhance the robustness of the model across multiple regional conditions through cross-regional validation in different climate zones and cropping systems, or by integrating transfer learning with domain adaptation methods. This will improve its potential for global-scale application. Concurrently, integrating additional data, methodologies, and models to achieve end-to-end feature learning should be considered.

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    Remote Sensing Extraction Method of Rice-Crayfish Fields Based on Dual-Branch and Multi-Scale Attention
    ZHANG Yun, ZHANG Lumin, XU Guangtao, HAO Jiahui
    Smart Agriculture    2025, 7 (6): 185-195.   DOI: 10.12133/j.smartag.SA202507032
    Abstract163)   HTML2)    PDF(pc) (3231KB)(21)       Save

    [Objective] Rice-crayfish co-culture represents a highly efficient ecological agricultural system that simultaneously provides substantial economic returns and ecological benefits. Accurately obtaining spatial distribution information on rice-crayfish fields is of great importance for promoting the optimal allocation of agricultural resources, supporting ecological protection, and facilitating sustainable agricultural development. In regions characterized by complex terrain, fragmented plots, and diverse planting structures, traditional extraction approaches are often influenced by spectral confusion and indistinct field boundaries, making it difficult to achieve high-precision identification of rice-crayfish fields. The aim of this research is to develop a deep learning model that integrates multi-temporal and multi-scale feature information to improve the accuracy of remote sensing identification of rice-crayfish fields. [Methods] Multi-temporal GF-2 satellite imagery was employed as the primary data source, and a deep learning-based extraction method named the dual-branch attention pyramid network (DBAP-Net) was developed. The proposed model was established upon the U-Net framework and designed with a dual-branch encoder architecture to extract temporal features from different phenological stages, thereby fully capturing the spectral variations of rice-crayfish fields between the paddy flooding and rice-growing periods. The convolutional block attention module (CBAM) was embedded in each encoder layer and in the skip connections to adaptively adjust feature weights along both the channel and spatial dimensions, enhancing the network's capacity to emphasize critical spatial structures of rice-crayfish fields while effectively suppressing background noise and redundant information. During the feature fusion stage, an atrous spatial pyramid pooling (ASPP) module was incorporated to aggregate contextual information from multiple receptive fields through multi-scale atrous convolutions, improving the model's capability for multi-scale spatial information representation. [Results and Discussions] A quantitative performance evaluation of each DBAP-Net component was conducted through ablation experiments. The results demonstrated that the introduction of a dual-branch structure improved overall accuracy (OA), F1-Score, intersection over union (IoU), and Matthews correlation coefficient (MCC) by 0.48, 0.57, 0.94, and 0.99 percentage points, respectively. Incorporating the CBAM module further enhanced these metrics by 0.52, 0.85, 1.40, and 1.20 percentage points, while the addition of the ASPP module yielded further increases of 0.59, 1.09, 1.81, and 1.49 percentage points, respectively. The DBAP-Net model achieved the highest comprehensive performance, with an OA of 94.45%, F1-Score of 91.79%, IoU of 84.82%, and MCC of 87.60%. These values represented respective improvements of 1.83, 2.55, 4.25, and 3.98 percentage points compared with the baseline U-Net model. These findings indicated that each enhancement module made a substantial contribution to improving both feature representation and spatial boundary delineation. DBAP-Net was further compared with five representative semantic segmentation networks, U-Net, PSPNet, DeepLabV3+, SegFormer, and TransUNet, to comprehensively evaluate its generalization and segmentation performance. The results demonstrated that DBAP-Net consistently achieved higher overall accuracy, precision, F1-Score, and IoU than all other comparison models. Specifically, compared with U-Net, PSPNet, and DeepLabV3+, the F1-Scores increased by 2.55, 2.98, and 2.75 percentage points, while the IoU values improved by 4.25, 4.96, and 4.57 percentage points, respectively. In comparison with the more recent models, SegFormer and TransUNet, DBAP-Net's F1-Score was higher by 3.09 and 1.67 percentage points, and its IoU was enhanced by 5.12 and 2.81 percentage points. Visualization of the segmentation results further revealed that DBAP-Net produced clear segmentation boundaries with complete fields, significantly reducing misclassification and omission rates. In contrast, other models exhibited varying degrees of boundary blurring and fragmentation. When applied across the entire study area, DBAP-Net demonstrated strong robustness and stability. The model achieved an overall accuracy of 96.00%, with a Kappa coefficient of 0.920. The producer's accuracy and user's accuracy were 95.13% and 97.38%, respectively. Compared with traditional approaches such as the seasonal water-difference method, random forest classification, and temporal index thresholding, DBAP-Net significantly improved both extraction precision and spatial completeness, particularly under conditions of complex terrain and fragmented agricultural landscapes. [Conclusions] DBAP-Net, by integrating multi-temporal spectral and multi-scale spatial information, significantly improves the accuracy and completeness of remote sensing extraction of rice-crayfish fields from high-resolution remote sensing imagery. The model provides a reliable and adaptable technical framework for fine-scale monitoring, precise mapping, and sustainable management of rice-crayfish co-culture systems, offering valuable methodological support for agricultural resource assessment and ecological protection.

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    Analysis of the Spatiotemporal Evolution and Driving Forces of Rural Settlements in Relation to Terrain Differences
    LIU Miao, ZHANG Jiayi, LI Zhenhai, CHEN Jing
    Smart Agriculture    2025, 7 (6): 210-224.   DOI: 10.12133/j.smartag.SA202509027
    Abstract160)   HTML1)    PDF(pc) (4559KB)(8)       Save

    [Objective] With the in-depth implementation of the rural revitalization strategy and the rapid progress of China's urbanization, the urban-rural spatial structure has undergone significant restructuring. Rural settlements, as the fundamental spatial units of rural areas, have witnessed remarkable changes in scale, layout, and function. Accurately identifying their spatiotemporal evolution and clarifying the driving forces is essential for comprehending urban-rural transformation, optimizing territorial spatial planning, and promoting coordinated development. However, current research still has limitations. Many studies primarily concentrate on describing spatial patterns, overlooking the underlying processes and regional disparities. Comparative analyses between different topographic regions, such as plains and hilly areas, are inadequate, resulting in an incomplete understanding of the impacts of terrain. Moreover, investigations into multi-scale and multi-factor driving mechanisms are relatively weak. Therefore, the aim of this research is to systematically uncover the spatiotemporal evolution of rural settlements under various topographic conditions and to quantitatively identify the key drivers shaping these patterns. [Methods] The primary research regions were Laoling City in Shandong Province, representing typical plain terrain, and Yi'an District in Anhui Province, characterized by hilly landforms. This selection fully takes into account how variations in topographic conditions influence the long - term evolution of rural settlement patterns. Based on the remote - sensing mapping results of rural settlements in 2002, 2012, and 2022, a systematic analysis of the spatial distribution characteristics and temporal differentiation of settlement patterns was conducted. Using geographic information system (GIS) spatial analysis as the analytical foundation and integrating methods such as landscape pattern indices, centroid migration analysis, and spatial pattern change detection, the spatiotemporal evolution trajectories of rural settlements were revealed under contrasting geomorphic settings from the perspectives of overall spatial configuration, internal structural features, and dynamic change processes. In addition, the geographical detector model was employed to quantitatively assess ten potential driving factors, including natural environmental conditions, socio - economic development indicators, transportation accessibility, and location - related attributes. [Results and Discussions] On the county scale, in the plain area, the largest patch index increased from 0.88 to 2.46, while the average nearest neighbor ratio (NNR) decreased from 0.99 to 0.90. This indicates that the settlement size expanded, the structure became more centralized, and the degree of clustering continuously strengthened. In contrast, in the hilly area, the patch density (PD) decreased from 9.16 to 2.77, and the NNR increased from 0.50 to 0.69. This suggests that the number of settlements declined and their spatial structure evolved from highly clustered to relatively dispersed. At the village scale, there were significant differences in the evolution trends between the two regions. In the plain area, changes in rural settlement areas were relatively balanced, with similar proportions of villages experiencing expansion and contraction. Settlements mainly exhibited a block - like distribution, extending along roads. In contrast, in the hilly area, the expansion of rural settlements was more pronounced, with over 70% of villages showing an increase in area. Settlements primarily displayed a linear distribution pattern, extending along rivers and valleys. Over the 20 - year period, the driving mechanisms of rural settlement evolution in the plain area shifted from being dominated by natural and economic factors to being dominated by land resource and safety factors. The driving power (q value) of distance to cultivated land and distance to the urban center increased by 0.51 and 0.39, respectively, becoming the main growth factors. In the hilly area, settlement evolution became increasingly constrained by topography, water resources, and geological safety. The driving power of distance to rivers increased from 0.22 to 0.45, and the dominant driving interaction shifted from "cultivated land-scenic spot" to "geological hazard point–scenic spot", reflecting a more complex driving mechanism. [Conclusions] Different topographic regions exhibit distinct spatial pattern characteristics and evolutionary driving mechanisms at both the county and village scales. Rural settlements in plain areas tend to demonstrate higher degrees of clustering, more regular morphologies, and relatively stable evolutionary processes. In contrast, settlements in hilly areas are more scattered and fragmented due to topographic constraints and resource limitations, and their evolutionary processes are more intricate. This study not only deepens the understanding of rural settlement evolution but also offers scientific support for the localized development of smart agriculture and the reconstruction of rural spatial systems.

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    Progress in Soil Moisture Retrieval under Crop Canopy Cover Based on Multi-polarization SAR Data
    SUN Rong, GAO Han, JIANG Yujie, LI Qiaochu, WU Haoyu, WU Shangrong, YU Shan, XU Lei, YU Liangliang, ZHANG Jie, BAO Yuhai
    Smart Agriculture    2025, 7 (6): 75-95.   DOI: 10.12133/j.smartag.SA202509009
    Abstract152)   HTML7)    PDF(pc) (2235KB)(17)       Save

    [Significance] Soil moisture is a critical parameter in surface water cycling and agricultural productivity, playing an essential role in crop growth monitoring, yield estimation, and field management. Synthetic aperture radar (SAR), with its all-weather capabilities and multi-polarization advantages, is highly sensitive to the structural, orientational, and moisture characteristics of crops and soil, making it a key remote sensing tool for soil moisture monitoring. However, under crop cover, surface scattering signals are confounded by vegetation scattering, and the spatial heterogeneity of crop and soil properties further complicates the scattering process. These factors make it challenging to directly apply traditional methods for agricultural soil moisture retrieval. The separation of scattering contributions from the crop canopy and underlying soil remains a significant research challenge. To address this, the present paper systematically reviews the state-of-the-art advancements in soil moisture retrieval under crop cover across three dimensions: data resources, scattering theory, and retrieval applications. [Progress] This review offers a comprehensive assessment of multi - polarization SAR - based agricultural soil moisture retrieval technology from the viewpoints of data, theory, and application, emphasizing future optimization. In terms of data resources, the paper presents a comprehensive summary of spaceborne multi - polarization SAR data. It compares key imaging parameters (e.g., frequency band, polarization mode, spatial resolution, and incidence angle) and analyzes their impacts on agricultural soil moisture retrieval. Research shows that, under single - source data conditions, long - wavelength bands, small incidence angles, and co - polarization modes are less prone to canopy scattering interference. Under multi - modal data conditions, integrating multi - band, multi - angle, and multi - polarization SAR data can more effectively distinguish between vegetation and surface scattering contributions. Regarding theoretical and technical progress, the paper tracks the development of scattering models, reviews existing soil and vegetation scattering models, and contrasts the applicability of physical, empirical, and semi - empirical models. It also emphasizes the advantages of coupled modeling approaches. Moreover, the paper examines various solution methods for scattering models, focusing on local and global optimization algorithms. In the application context, this paper evaluates the performance of multi - polarization SAR in soil moisture retrieval across different crop and soil conditions, using wheat, corn, rapeseed, and soybean as typical crops. It discusses the influence of different crop types (e.g., differences in leaf and stem structure) and phenological stages on retrieval accuracy. The paper compares the applicability of soil scattering models and retrieval methods under various soil surface roughness and soil texture conditions (e.g., sandy and loamy soils) and examines their retrieval accuracy under different soil scenarios. Additionally, it reviews the improvements in retrieval performance through multi - source data fusion, including optical - SAR combinations and active - passive remote sensing fusion. It also synthesizes the main challenges and future directions for multi - source data fusion strategies, especially with regard to scale effects. [Conclusions and Prospects] Based on the reviewed advancements, the paper identifies key technical challenges, including discrepancies in monitoring range and scale among spaceborne, airborne, and ground-based data, difficulties in adapting scattering models to crop morphology, and the lack of standardized validation protocols for retrieval results. Looking ahead, the paper envisions the potential for future technological progress driven by multi-modal big data and artificial intelligence. This review highlights critical insights, addresses key bottlenecks, and drives the development of intelligent, adaptive, high-resolution, and high-precision soil moisture retrieval systems in multi-polarization SAR soil moisture retrieval.

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    Vegetable IoT Blockchain Anti Counterfeiting Traceability System Based on PQ-ECIES
    QI Peiyang, SUN Chuanheng, TAN Changwei, WANG Jun, XING Bin
    Smart Agriculture    DOI: 10.12133/j.smartag.SA202507019
    Online available: 09 October 2025

    A Low-rank Adaptation Method for Fine-tuning Plant Disease Recognition Models
    HUANG Jinqing, YE Jin, HU Huilin, YANG Jihui, LAN Wei, ZHANG Yanqing
    Smart Agriculture    DOI: 10.12133/j.smartag.SA202504003
    Online available: 28 November 2025

    Cross-validation Study on the Authenticity of Agricultural Insurance Underwriting Based on Multi-Source Satellite Remote Sensing Data: Taking Multi-Season Rice in M ​​County, S Province as A Case
    CHEN Ailian, ZHANG Rusheng, LI Ran, ZHAO Sijian, ZHU Yuxia, LAI Jibao, SUN Wei, ZHANG Jing
    Smart Agriculture    2025, 7 (6): 225-236.   DOI: 10.12133/j.smartag.SA202507034
    Abstract142)   HTML5)    PDF(pc) (1911KB)(19)       Save

    Objective Rice, wheat, and corn, account for over 50% of government-subsidized premiums. Therefore, ensuring the authenticity of insurance underwriting data of three major staple crops is crucial for safeguarding fiscal funds and promoting the high-quality development of agricultural insurance. Currently, verifying the authenticity of underwriting data relies on remote sensing technology to achieve high-precision, high-efficiency, and low-cost crop identification. However, in the multi-season rice-growing areas of southern China, remote sensing identification still suffers from insufficient accuracy and delayed timeliness. This study, targeting the actual business needs of agricultural insurance, explores a "fast, accurate, and low-cost" identification method for multi-season rice in the hilly areas of Southern China. Cross-validation of underwriting data authenticity is conducted based on case studies, providing technical support for fiscal fund security and the high-quality development of agricultural insurance. Methods The high-resolution remote sensing data of China was integrated with internationally available data. First, a deep learning algorithm and high-resolution imagery were used to rapidly extract cultivated land parcels as classification units. Combined with field sampling and samples derived from high resolution imagery, rice classification and identification were performed on the Google Earth Engine (GEE) platform using Sentinel-1 radar data and Sentinel-2 multispectral data. Three methods, random forest, support vector machine, and classification and regression tree, were compared, and the optimal model result was selected for cross-validation of rice insurance data. Validation metrics included four categories: area difference (AD), the difference between remote sensing and insured area, cover ratio (CR), crop insurance coverage, overlapping ratio (OR), overlap of policy parcels, and crop proportion (CP), crop proportion within policy parcels. Results and Discussions Based on the cultivated land units extracted using deep learning, a classification feature set was constructed by integrating Sentinel-1 radar polarimetric signatures from March to October with Sentinel-2 multi-spectral reflectance and NDVI from July and August. The random forest model achieved 0.93 identification accuracy, meeting the accuracy and cost requirements for verifying mid- and late-season rice insurance data. However, due to the lack of multi-spectral data at key time phases required for early rice identification, its identification timeliness is poor and is only suitable for post-warning and deterrence the following year. Cross-validation results show that the county's overall insured area is basically the same as the remote sensing identification area, but there are significant differences in township scale: Among the 33 townships, 14 towns have AD more than 10 000 hm2, indicating false insurance. As for CR, 10 townships have a crop insurance coverage rate of more than 1, 1 township has a CR less than 0.4, and another 2 townships have not carried out insurance. As for policy parcels, the 31 townships with insurance records, 10 did not provide plot data; among the 21 townships that provided data, 3 townships had an overlap rate of more than 40% for more than 50% of the policy plots, and 14 townships had an overlap rate of more than 40% for more than 20% of the policy plots. These results suggest that some areas may have problems such as false insurance, duplicate insurance, or non-standard operations, and regulatory authorities need to intervene and verify in a timely manner. Conclusions A technical system suitable is developed for rapid remote sensing identification and cross-validation with insurance data. Its feasibility in practical regulatory applications has been verified, providing effective methodological support for authenticity verification and precise regulation of agricultural insurance underwriting.

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    Multi-Machine Collaborative Operation Scheduling and Planning Method Based on Improved Genetic Algorithm
    ZHU Tianwen, WANG Xu, ZHANG Bo, DU Xintong, WU Chundu
    Smart Agriculture    DOI: 10.12133/j.smartag.SA202508010
    Online available: 04 November 2025

    Online Detection System for Freshness of Fruits and Vegetables Based on Temporal Multi-source Information Fusion
    HUANG Xianguo, ZHU Qibing, HUANG Min
    Smart Agriculture    DOI: 10.12133/j.smartag.SA202505037
    Online available: 21 October 2025

    A Greenhouse Temperature and Humidity Prediction Method Based on Adaptive Kalman Filter and GWO-LSTM-attention
    CAI Yuqin, LIU Daming, XU Qin, LI Boyang, LIU Bojie
    Smart Agriculture    DOI: 10.12133/j.smartag.SA202506033
    Online available: 13 October 2025

    Smart Agriculture    2025, 7 (3): 0-1.  
    Abstract123)            Save
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    Multi-scale Tea Leaf Disease Detection Based on Improved YOLOv11n
    XIAO Ruihong, TAN Lixin, WANG Rifeng, SONG Min, HU Chengxi
    Smart Agriculture    DOI: 10.12133/j.smartag.SA202509014
    Online available: 13 November 2025

    Research on Tractor Trajectory Tracking Control Based on A Dual-Motor Steer-by-Wire System
    LI Lei, PAN Liang, DONG Jiwei, CAO Zhonghua, LUO Xingfa, ZHAN Xiaomei, LI Yali, SUN Zhiqiang
    Smart Agriculture    DOI: 10.12133/j.smartag.SA202507027
    Online available: 21 October 2025

    Remote Sensing Monitoring Method of Cropping Index in Typical Open-Field Vegetable Production Areas
    ZHANG Yunxiang, WU Xuequn, HE Yonglin, MA Junwei
    Smart Agriculture    2025, 7 (6): 174-184.   DOI: 10.12133/j.smartag.SA202508024
    Abstract108)   HTML2)    PDF(pc) (32880KB)(25)       Save

    Objective Most existing studies on cropping index extraction have primarily focused on cereal crops, whereas investigations targeting vegetable-based cropping systems remain relatively limited. Current national-scale cropping index products are largely designed for major cereal-producing regions, with model parameters calibrated according to the phenological characteristics and growth cycles of cereal crops. Such parameterization neglects the distinctive multi-season rotation patterns of open-field vegetable cultivation, leading to reduced accuracy in capturing the actual cropping dynamics of vegetable-growing areas. Consequently, these limitations hinder a comprehensive understanding of cropland utilization characteristics in regions characterized by intensive vegetable production. This study aims to improve the extraction method of the cropping index for vegetable cultivation systems and to reveal the characteristics of cropland use in typical regions with intensive vegetable multiple cropping. Methods Sentinel-2 MSI surface reflectance (SR) data released by the European space agency (ESA) was employed. Greedy algorithm was used to identify the optimal grid and orbit combination (47QRG, 61) covering Tonghai county, Yunnan Province. Based on this configuration, a total of 362 images acquired from 2020 to 2024 were compiled to construct a 5-day, 10 m spatial resolution time series. The normalized difference vegetation index (NDVI) time series was smoothed and reconstructed using the Whittaker smoothing (WS) method. The cropland extent was defined using the cropland mask from the GLC_FCS10 land cover dataset. Active croplands and greenhouse-covered areas were further identified using the vegetation-soil-pigment indices and synthetic-aperture radar (SAR) time-series images (VSPS) and the advanced plastic greenhouse index (APGI). Non-active croplands and greenhouse areas were excluded to refine the open-field cropland boundaries. Subsequently, the second-order difference method was applied to detect NDVI peaks in the reconstructed time series, with rule-based constraints used to eliminate false peaks. The number of valid peaks per pixel was then used to calculate the annual cropping index of Tonghai county from 2020 to 2024, and its spatial distribution and spatiotemporal variations were analyzed. Results and Discussions Compared with conventional 10-day median or maximum value compositing approaches, the time-series reconstruction based on specific grid and orbit combinations provides a more accurate representation of crop growth dynamics and peak patterns. Validation using 338 ground samples of cropping index obtained from field surveys in 2024 demonstrated an overall accuracy of 89.94%, a Kappa coefficient of 0.84, mean absolute error (MAE) of 0.11, and root mean square error (RMSE) of 0.36, indicating satisfactory reliability of the extracted results. From 2020 to 2024, the average cropping indices of croplands in Tonghai county were 221.45%, 217.80%, 275.37%, 232.41%, and 237.50%, respectively, reflecting a generally high level of land-use intensity. In 2020, 2021, 2023, and 2024, double cropping systems dominated, with triple cropping being secondary, whereas in 2022, triple cropping became predominant. Multi-season cropping (≥3 seasons) was mainly concentrated along the urban zones adjacent to Qilu lake, where abundant water resources provide favorable conditions for open-field vegetable cultivation. Interannual variations in the cropping index were largely driven by the alternation between double- and triple-cropping systems. Specifically, from 2020 to 2021, the cropping index decreased by 3.67%; cropland areas with decreased, unchanged, and increased indices accounted for 31.08%, 40.23%, and 28.69% of the total cropland area, respectively, with 10.45% of croplands shifting from triple to double cropping. From 2021 to 2022, the index increased substantially by 57.57%; decreased, unchanged, and increased areas accounted for 13.79%, 33.02%, and 53.18%, respectively, with 17.60% of croplands converting from double to triple cropping. Between 2022 and 2023, the index decreased by 42.96%, with corresponding area proportions of 47.96%, 35.06%, and 16.99%, and 16.62% of croplands shifting from triple to double cropping. From 2023 to 2024, the index slightly increased by 5.09%, with 27.49%, 39.19%, and 33.32% of croplands showing decreases, stability, and increases, respectively; 11.21% of croplands converted from double to triple cropping. Overall, the interannual variations were mainly influenced by the mutual transitions between double- and triple-cropping systems. Conclusions The results provide valuable theoretical and technical references for cropland resource management, optimization of regional vegetable production, and the promotion of sustainable agricultural development in Tonghai county.

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