[Significance] Digital agriculture is unequivocally the core driving force for modern agricultural transformation, fundamentally aiming to achieve full-process digital mapping and intelligent management of production through the deep integration of advanced information technologies such as the Internet of Things, big data, artificial intelligence (AI), and remote sensing, with earth observation (EO) technology serving as the essential data engine providing indispensable spatial information support for this systemic shift. However, the current landscape of digital agriculture development remains unbalanced, exhibiting a tendency to be "heavy on transactions and light on production", where the core production links suffer from low digitalization penetration rates; furthermore, the profound knowledge embedded within the vast corpus of EO data has yet to be fully extracted and interpreted, leading to a situation where many established algorithms demonstrate insufficient robustness and universality when confronted with the complexity and diversity of global cropping systems, thereby limiting their practical efficacy. Crucially, an over-reliance on technology to optimize production efficiency alone, without ecological guidance, can induce secondary environmental risks, such as exacerbating regional groundwater depletion or contributing to a decline in biodiversity through agricultural landscape simplification, thus necessitating an approach that promotes the deep coupling of EO technology with agronomic principles and local ecological practices to construct a resilient smart agricultural system that achieves a holistic balance between productivity, resource efficiency, and ecological integrity. [Progress] The current research frontiers of EO-driven digital agriculture primarily converge on three critical domains: intelligent crop condition monitoring, digital twin farming systems, and the enhancement of agricultural system resilience. Intelligent monitoring utilizes the fusion of high-resolution remote sensing imagery and machine learning frameworks to enable large-scale, comprehensive crop mapping and the fine-grained identification of crop types at the field scale, with next-generation yield prediction models integrating advanced deep learning techniques to significantly improve accuracy, while remote sensing is also effectively employed for agricultural disaster monitoring. The digital twin farming system represents an advanced stage of precision agriculture, centered on digitally modeling all agricultural production elements to construct a highly consistent virtual replica of the physical environment, operating through a real-time closed-loop mechanism of perception, simulation and analysis, and decision-making support to guide optimal interventions; successful applications include intelligent water resource scheduling in Chinese irrigation districts and the use of AI vision algorithms to manage complex biological processes like crab farming, although the field must overcome the issue of "pseudo-twins" that focuses on mere visualization rather than driving concrete operational decisions. The focus on agricultural system resilience is supported by digital agriculture providing crucial spatial data on global crop yields, cultivated land distribution, and practices like terracing. To illustrate the practical efficacy of these technologies, this paper analyzes two representative application cases. First, the CropWatch system represents a paradigm shift in agricultural monitoring by constructing a "Cloud-Edge" collaborative ecosystem. It integrates machine learning with a "Pre-training, Prompting, and Fine-tuning" large language model (LLM) framework to automate remote sensing-based crop monitoring, report generation and enhance decision-support intelligence. Through open application programming interfaces (APIs) and multi-scale capabilities, CropWatch provides cross-scale information and decision support from macro-level policy support to micro-level farm management, serving as a global public good that bridges the digital divide in developing nations. Second, in the domain of agricultural water management, the ETWatch technical system demonstrates a robust solution for the precise governance of water resources. By achieving high-resolution evapotranspiration (ET) monitoring from basin to field scales, it enables the accurate assessment of water productivity and the optimization of irrigation schedules. Crucially, this technology is successfully embedded into institutional mechanisms, such as water rights allocation and tiered pricing based on actual consumption, thereby realizing a transformation from empirical water use to data-driven, precise regulation. [Conclusions and Prospects] In sum, digital (smart) agriculture is rapidly transcending its role as a mere extension of agricultural informatization to become the "new-quality productivity" driving high-quality agricultural development, achieving this by fundamentally restructuring production factors, enhancing resource efficiency, strengthening risk response capabilities, and promoting value chain upgrading, thereby offering critical momentum for constructing a more efficient, greener, and sustainable modern agricultural system. Given China's pronounced global advantages in the digital economy, information technology, remote sensing, and intelligent equipment, the nation is well-positioned to integrate these strengths to construct comprehensive, full-chain smart agricultural solutions whose mature systemic models and business paradigms can ultimately form a "China Card" in the global agricultural revolution, contributing Chinese wisdom and solutions towards the realization of global food security and the zero-hunger goal.
[Significance] Under climate change, the frequency and intensity of extreme weather events have increased markedly, posing persistent threats to global food security. Agricultural meteorological disasters, including droughts, floods, heat stress, frost damage, and mechanically induced events such as lodging and hail, are increasingly characterized by rapid onset, strong spatial heterogeneity, and compound interactions. Conventional management strategies relying mainly on post-event assessment are insufficient for timely warning and precision intervention. The development of high spatiotemporal resolution remote sensing and integrated observation systems combining satellite, unmanned aerial vehicle (UAV), and ground-based sensing has substantially advanced agricultural disaster monitoring. These technologies enable field-scale characterization of spatial variability and detection of short-duration disaster processes at hourly to daily timescales. This review synthesizes recent progress in sky-air-ground integrated remote sensing for agricultural meteorological disaster management and establishes a unified framework linking monitoring, early warning, and decision-making, with emphasis on hydrological stress, thermal stress, and structural damage. [Progress] At the observation level, a multi-tier sensing architecture has emerged. Satellite remote sensing provides broad coverage and regular revisit cycles, forming the backbone of regional monitoring. Optical sensors support retrieval of crop structural and biochemical parameters, thermal infrared data enable canopy temperature and evapotranspiration estimation, and synthetic aperture radar (SAR) offers all-weather capability for soil moisture and flood detection. Solar-induced chlorophyll fluorescence (SIF) provides direct information on crop photosynthetic function and enables early identification of physiological stress. UAV platforms complement satellites through flexible deployment and centimeter-scale resolution, allowing detailed mapping of canopy temperature and three-dimensional crop structure using multispectral, thermal, and light detection and ranging (LiDAR) sensors. Ground-based meteorological stations and sensor networks provide continuous measurements for calibration and validation, although scaling point observations to spatially continuous products remains challenging. Consequently, multi-sensor integration is evolving from data stacking toward physically complementary constraint frameworks. Methodologically, two dominant approaches of physically based inversion and data-driven recognition are used. Radiative transfer models, surface energy balance methods, and SAR scattering models offer strong physical interpretability but depend on prior information and data quality. Machine learning and deep learning methods effectively capture nonlinear relationships and complex spatial patterns for disaster identification, yet remain limited by interpretability and cross-regional generalization. At the early-warning stage, crop growth models, hydrological models, and spatiotemporal prediction networks are applied to simulate disaster evolution. Hybrid models embedding physical constraints into data-driven frameworks have become a key research direction to enhance predictive robustness. Decision-support systems have expanded from threshold-based rule engines toward optimization algorithms and multi-objective frameworks, enabling warning information to be translated into actionable irrigation scheduling, protective measures, and emergency responses. Regarding specific hazards, drought monitoring has shifted from vegetation indices toward coupling root-zone soil moisture with crop physiological responses, with SIF-based indicators showing strong potential for early stress detection. Flood studies rely primarily on SAR-based inundation mapping and extend toward quantitative damage assessment. Heat and frost stress research emphasizes growth-stage-dependent dynamic thresholds. Lodging monitoring integrates structural parameters derived from optical, LiDAR, and SAR data, while hail-related studies focus on rapid post-event damage mapping. Compound and cascading disasters have become an important research frontier. [Conclusions and Prospects] High spatiotemporal resolution remote sensing has greatly enhanced the observability and early-warning potential of agricultural meteorological disasters. Nevertheless, key challenges remain, including heterogeneous data integration, scale inconsistency, uncertainty propagation, and insufficient coupling among monitoring, warning, and decision-making components. Future progress requires a systems-engineering perspective. Physically guided machine learning can bridge mechanistic understanding and data adaptability, while agricultural disaster digital twins provide a framework for dynamic interaction among observation, simulation, and decision optimization. In parallel, multi-factor time-series risk modeling and multi-agent learning are needed to better represent compound disaster processes and support intelligent, adaptive, and precision-oriented agricultural disaster management systems.
[Objective] Lodging is a major agronomic constraint that adversely affects both yield and quality in field crops, with flax (Linum usitatissimum L.) being especially vulnerable due to its slender stems and susceptibility to wind and rainfall. Precise delineation of lodged areas from field imagery remains a significant challenge owing to the complex and heterogeneous morphology of lodging patterns, irregular and blurred boundaries, and substantial background interference from upright plants, weeds, and soil textures. These factors necessitate the development of a segmentation framework that combines high precision and strong boundary adherence with computational efficiency, enabling deployment on resource-constrained agricultural monitoring platforms. In response to this need, a lightweight accurate lodging segmentation approach based on improved YOLOv11n-seg architecture was proposed to enhance fine-grained feature sensitivity, multi-scale representation capability, and boundary precision, while markedly reducing parameter count, giga floating-point operations (GFLOPs), and model size. [Methods] The proposed architecture integrated targeted modifications across the backbone, neck, and output stages. In the backbone, standard C3k2 modules were replaced with C3k2_SDW blocks, which combined a StarBlock structure with depthwise separable convolutions to reduce redundancy and computation without sacrificing spatial and contextual representational capacity. To counteract potential reductions in channel discrimination resulting from light-weighting, a multi-scale efficient channel attention (MS-ECA) mechanism was embedded within selected backbone layers, yielding C3k2_SDW_MS-ECA modules. These modules incorporated parallel convolution branches with varying kernel sizes to capture channel-wise dependencies across multiple receptive fields, thereby adaptively recalibrating lodging-related features with minimal computational overhead. In the neck, a bidirectional feature pyramid network (BiFPN) was introduced to facilitate efficient bidirectional information exchange between scales. By assigning normalized, trainable fusion weights, the BiFPN adaptively balanced contributions from low- and high-level feature maps, while a multi-stage semantic fusion strategy further enriched the integration of spatial details and contextual semantics, thereby improving the detection of small and fragmented lodged patches. At the output stage, a boundary refinement procedure was applied to the predicted masks, improving contour sharpness, enhancing boundary compactness, and mitigating false detections in complex visual environments.The experimental dataset comprised unmanned aerial vehicle (UAV) RGB imagery at a resolution of 4 032×2 268 pixels, acquired from flax fields in Dingxi, Gansu province. Lodged regions were manually annotated with polygonal masks. To increase robustness against variability in illumination, background complexity, and lodging morphology, data augmentation techniques, including random rotation, brightness and contrast adjustment, and blurring were employed, expanding the dataset to 3 852 images. The dataset was divided into training, validation, and testing subsets in a 75%, 15% and 10% split. Model training was conducted with 640×640 pixel inputs for 300 epochs using stochastic gradient descent (initial learning rate 0.01, momentum 0.937, weight decay 0.000 5) in PyTorch 2.0.0. Evaluation involved comparison with YOLACT, YOLOv7-seg, YOLOv8n-seg, and the original YOLOv11n-seg using precision (P), recall (R), mAP@0.5, mAP@0.5:0.95, parameter count, GFLOPs, and model size. [Results and Discussions] Ablation experiments demonstrated the incremental contributions of each architectural component. Substituting C3k2 with C3k2_SDW reduced parameters from 2.83 M to 2.14 M and computation from 10.2 to 8.1 GFLOPs, with slight performance improvements. Incorporating BiFPN further lowered complexity to 1.68 M parameters and 7.7 GFLOPs, accompanied by notable gains in detection metrics. The addition of MS-ECA attention achieved the highest performance, delivering P of 92.6%, R of 92.0%, and mAP@0.5 of 95.2%, corresponding to improvements of 3.7 percentage points in Precision and 2.1 percentage points in mAP@0.5 over the YOLOv11n-seg baseline, without increasing model size. Qualitative Grad-CAM visualizations revealed more precise focus on lodging regions and reduced false activations in upright stems and non-lodged soil areas. Generalization capability was further validated on the public WE3DS agricultural segmentation dataset, where the proposed model achieved average improvements of 4.3, 1.9, and 2.6 percentage points in precision, recall, and mAP@0.5, respectively, compared to the baseline. [Conclusions] The improved YOLOv11n-seg architecture achieves a superior balance between accuracy and efficiency for flax lodging segmentation by combining the C3k2_SDW_MS-ECA backbone, BiFPN with multi-stage semantic fusion in the neck, and output boundary refinement. This combination of high accuracy, lightweight design, and robust boundary delineation renders the model highly applicable to real-time, in-field deployment for intelligent lodging monitoring and precision agriculture. The results further suggest that the approach is transferable to broader agricultural segmentation tasks, providing a practical and scalable solution for modern smart farming applications.
[Objective] Soil quality is crucial for food security, ecosystem health, and sustainable development, but faces degradation due to intensive land use. Accurate soil quality assessment is therefore essential for informed land management and ecological protection. Machine learning has enhanced digital soil mapping (DSM) by improving modeling accuracy through multi-source data integration. Within DSM, soil sampling design is a foundational step that directly influences prediction accuracy, cost, and efficiency. An ideal scheme must balance mapping precision with economic and operational feasibility. This study focuses on soil organic matter (SOM), a core indicator of soil quality affecting fertility, carbon sequestration, and environmental regulation. Precisely mapping its spatial variability is vital for sustainable soil management. To address the need for efficient sampling, the aim of this research is to develop an optimal sampling design method for regional-scale SOM mapping, reduce sampling redundancy and cost while improving spatial prediction accuracy. [Methods] A sampling optimization framework was proposed that integrated intelligent optimization algorithms with a hybrid spatial interpolation model. The framework was built upon the hippopotamus optimization algorithm (HO) and incorporated the random forest residual kriging (RFRK) method to construct an optimal sampling strategy for the spatial prediction of SOM. At the initialization stage, a population of candidate solutions, referred to as "hippopotamuses", was randomly generated, with each individual representing a potential sampling layout. The HO was employed to select subsets of sampling points from the training sample pool, with each subset forming a candidate solution. Collectively, these solutions constituted the initial hippopotamus population. The study area was located in Lanxi city, Zhejiang province, where a total of 1 080 field-measured soil samples were collected. These samples were partitioned into a training set (n=756), a validation set (n=108), and a test set (n=216) at a ratio of 7:1:2. Environmental covariates, including terrain attributes, vegetation indices, and climate factors, were extracted from multi-source remote sensing datasets. Using these covariates, the HO optimized sampling schemes across varying densities and spatial configurations. The resulting designs were then evaluated using the RFRK model to assess their SOM prediction performance. This process enabled the identification of the optimal sampling density and spatial layout that balanced accuracy and cost-efficiency. [Results and Discussions] When the HO-RFRK framework was applied, the prediction accuracy of SOM improved significantly as sampling density increased from 0.5 to 2.3 points/km2 (136-629 points). The root mean square error (RMSE) on the test set decreased from 6.04 to 5.11 g/kg, representing a reduction of approximately 15.4%. The lowest prediction errors were observed at a sampling density of 2.3 points/km2, with the RMSE and mean absolute error (MAE) reaching their minimum values of 5.11 and 3.79 g/kg, respectively, beyond which further increases yielded only marginal gains, indicating diminishing returns. To assess the effectiveness of HO, its performance was compared with three established methods: conditioned Latin hypercube sampling (cLHS), genetic algorithm (GA), and particle swarm optimization (PSO). At lower densities (0.5－1.3 points/km2), all methods showed limited predictive power. However, at 1.4 points/km2 (383 points), the HO method was the first to exceed predefined accuracy thresholds (coefficient of determination, R2>0.40; Lin's concordance correlation coefficient, LCCC>0.55), achieving R2=0.41 and LCCC=0.57, outperforming cLHS (R²=0.38, LCCC=0.53), GA (R2=0.39, LCCC=0.52), and PSO (R2=0.38, LCCC=0.51). Across the range of 1.4－2.3 points/km2, HO consistently delivered superior results. At 2.3 points/km2, the HO-RFRK combination achieved R2=0.49 and LCCC=0.63, surpassing cLHS, GA, and PSO in both metrics. [Conclusions] Based on the cultivated land of Lanxi city as a test case, a novel sampling optimization strategy was proposed based on the HO. First, the strategy successfully identified an optimal sampling density that maximizes prediction accuracy, as well as a lower, cost-effective density that maintains robust predictive performance with substantially reduced survey costs, defining a practical density range that balances precision and economic feasibility. Second, the RFRK model consistently demonstrated superior prediction accuracy compared to the standard random forest (RF) model across all tested sampling schemes, validating the effectiveness of the integrated HO-RFRK approach. In summary, this optimized strategy achieves high mapping accuracy with greater sampling efficiency, offering a scientifically grounded and practical methodology for reducing long-term soil monitoring costs. It provides a valuable reference for optimizing soil surveys in Lanxi city and other regions with similar environmental settings.
[Objective] The Chenopodium quinoa panicle is a critical phenotypic indicator for estimating crop yield and evaluating the growth condition of Chenopodium quinoa plants. Accurate and efficient recognition of Chenopodium quinoa panicles in complex field environments is therefore of great significance for intelligent agriculture, yield prediction, and automatic crop management. However, unmanned aerial vehicle (UAV)-acquired field imagery often exhibits complex characteristics such as diverse panicle morphology, uneven illumination, overlapping occlusion, and background interference, et al., posing substantial challenges for conventional target detection algorithms. To address these issues, a lightweight target detection model, named YOLOv8n-SSND (YOLOv8n with Switchable Atrous Convolution, Slim Neck, and Deformable Attention) is proposed, and specifically optimized for UAV-based Chenopodium quinoa panicle identification to improve the detection accuracy and inference efficiency for Chenopodium quinoa panicles while maintaining low computational cost and real-time performance suitable for embedded UAV deployment. [Methods] The proposed model was constructed based on the YOLOv8n and YOLOv11n frameworks, and incorporated several improvements tailored for small-object agricultural detection tasks. To enhance the ability to capture multi-scale and high-dimensional semantic features, the switchable atrous convolution (SAC) module was embedded into the backbone network. This module dynamically adjusted its receptive field according to spatial context, enabling more precise extraction of local and global texture details of Chenopodium quinoa panicles. In order to reduce redundant parameters and maintain high computational efficiency, a slim-neck lightweight feature fusion layer was designed, which effectively strengthened the integration of shallow spatial information and deep semantic features, allowing the network to maintain high accuracy without increasing model complexity. Additionally, a deformable attention (DA) mechanism was introduced to enable adaptive focus on regions with rich panicle-related features while suppressing irrelevant background noise. This attention mechanism assigned dynamic weights across both spatial and channel dimensions, improving the model's robustness against occlusions, illumination variations, and complex field textures commonly encountered in UAV images. [Results and Discussions] Comprehensive field experiments were conducted using UAV images of Chenopodium quinoa plots collected under different environmental conditions and growth stages. The results demonstrated that the proposed YOLOv8n-SSND model achieved a mean average precision (mAP50) of 94.3%, showing a remarkable improvement over multiple baseline and comparative models. Specifically, compared with YOLOv11n-SSND, YOLOv11n, YOLOv12n, YOLOv7, YOLOv5s, single shot multibox detector (SSD), fast region-based convolutional neural network (Fast R-CNN) and YOLOv8n, the proposed model achieved improvements of 0.7, 0.9, 2.1, 1.4, 2.0, 23.1, 19.6 and 1.8 percentage points respectively (SSD and Fast R-CNN). In terms of computational efficiency, the inference speed reached 166.7 f/s, representing a 26.7% increase over the YOLOv8n baseline, which ensured real-time detection capability for UAV-mounted onboard processors. Moreover, the total operation count was reduced to 6.8 GFLOPs, reflecting a 16.0% reduction compared with the baseline model, thus demonstrating the improved efficiency of the proposed architecture. The experimental comparison also indicated that the integration of SAC enhanced the model's sensitivity to complex spatial patterns, while the DA module effectively improved feature selectivity and prevented overfitting to background textures. The Slim-Neck design contributed significantly to reducing parameter redundancy and facilitated smooth feature propagation across layers. [Conclusions] The YOLOv8n-SSND model effectively achieves a balance among detection accuracy, inference speed, and computational cost, making it well-suited for real-time UAV-based agricultural monitoring. The experimental outcomes confirm that the model not only provides high-precision detection of Chenopodium quinoa panicles but also offers superior inference efficiency with minimal computational resources. These characteristics make it a promising solution for UAV-deployed intelligent agricultural systems, where power and processing capacity are limited. Furthermore, the proposed method provides a technical foundation for large-scale and automated monitoring of Chenopodium quinoa growth, enabling accurate yield estimation, phenotypic analysis, and precision crop management.
[Objective] Soil organic matter (SOM) is a fundamental indicator for evaluating soil fertility and soil quality. In mountainous counties characterized by complex terrain and pronounced environmental heterogeneity, SOM exhibits strong spatial variability even over short distances, which often results in limited prediction accuracy for conventional digital soil mapping (DSM) models. With the nationwide implementation of the Third National Soil Census, the demand for high-resolution and high-accuracy SOM mapping at the county scale has become increasingly urgent. Against this backdrop, Yiyuan county in Shandong province was selected as the study area to assess the applicability of the geographically weighted random forest (GWRF) model in SOM mapping within complex terrain regions. Furthermore, it sought to systematically compare the predictive performance of GWRF with several commonly used models, thereby providing technical support for soil resource surveys, census result compilation, and county-level land management. [Methods] The dataset consisting of 1 565 measured topsoil SOM samples was utilized, along with nineteen environmental variables representing five categories: topography, climate, vegetation, soil properties, and land use. Through correlation analysis and collinearity diagnostics, twelve key variables were retained for model construction. The GWRF model, which integrates localized spatial modeling with nonlinear machine-learning capability, was developed to generate high-resolution SOM predictions across the study area. An adaptive bandwidth strategy was employed, and the optimal bandwidth of 500 was determined. Grid search combined with cross-validation was used to identify the optimal mtry value of 4 for the random forest component. In addition to GWRF, four reference models were constructed for comparison: ordinary kriging (OK), multiple linear regression (MLR), geographically weighted regression (GWR), and random forest (RF). Model performance was evaluated using two commonly adopted accuracy metrics: the coefficient of determination (R2) and root-mean-square error (RMSE). [Results and Discussions] Overall, SOM levels in Yiyuan county were relatively low, with a mean value of 15.62 g/kg. The spatial variation was moderate and exhibited a clear pattern: SOM values were higher in the central area and lower in the northeastern and southwestern areas. Considerable differences were observed in prediction accuracy among the five models. The GWRF model achieved the best overall performance, with an R2 of 0.48 and an RMSE of 5.12 g/kg. This accuracy clearly surpassed that of RF (R2=0.41) and GWR (R2=0.35), and its advantage over MLR and OK was even more pronounced. A paired-sample t-test further confirmed that the accuracy improvements of GWRF over the other four models were statistically significant, supporting the robustness and reliability of the model's enhanced performance. According to the mapping results, the OK model produced an excessively smooth surface, making it difficult to reveal local details. While the MLR and GWR models could characterize certain environmental effects, they exhibited significant biases such as underestimation of high values and overestimation of low values. In contrast, the GWRF model performed prominently in capturing both global trends and local subtle variations. The analysis of variable importance showed that soil type, annual evapotranspiration, slope, and sand content were the most influential factors governing SOM distribution in the study area. Moreover, their spatially varying importance revealed notable heterogeneity. [Conclusions] This study demonstrated that the GWRF model possesses significant advantages in county-scale SOM digital mapping within mountainous areas. Its prediction accuracy markedly exceeded that of RF and conventional linear models, owing to its ability to simultaneously capture nonlinear environmental relationships and localized spatial variations. The enhanced mapping precision and improved representation of spatial details highlight the strong potential of GWRF for applications requiring high-accuracy soil information. GWRF is well-suited for SOM prediction under complex terrain conditions and can serve as an effective technical tool for county-level soil property estimation. Future research may incorporate human-activity-related variables, employ localized variable-selection strategies within the GWRF framework to further refine model performance, and explore the application potential of more advanced deep learning models in soil property mapping.
[Objective] The first flowering date of apples is a key phenological stage in the annual growth cycle of fruit trees. Its occurrence timing is directly associated with pollination efficiency, fruit set rate, and subsequent fruit development, and it also serves as an important basis for orchard management practices, including flower and fruit thinning, pest and disease control, as well as early risk warning and emergency management for low-temperature frost events during the flowering period. Existing studies still have room for improvement in the fine-scale extraction of temperature time-series information and in the representation of model adaptability across different spatial locations. Therefore, the purpose of this research is to develop a prediction method for the first flowering date of apples that can effectively characterize time-varying temperature patterns and achieve regional adaptability, thereby providing more reliable technical support for refined orchard management and disaster prevention. [Methods] A deep learning-based forecasting framework for predicting the first flowering date of apples was developed based on observation sites in Luochuan county, Shaanxi province. First, daily near-surface air temperature (NSAT) data from 2019 to 2021 were collected for the period from apple harvest to the subsequent flowering season in the study area, including daily maximum, mean, and minimum temperatures. In addition, elevation, latitude, and longitude were introduced as static geographic factors, forming a combined input composed of dynamic temperature sequences and static spatial attributes. Second, in terms of the model design, a bidirectional long short-term memory network (Bi-LSTM) was employed as the temporal encoder to learn bidirectional dependencies within the temperature time series. On this basis, a customized multi-head attention (MHA) mechanism was integrated, consisting of a local dependency head, a global trend head, and a cumulative feature head, which were designed to represent short-term pre-flowering temperature fluctuations, overall temperature trends, and cumulative temperature effects, respectively. This configuration enhanced the extraction of time-varying information across multiple temporal scales. The attention outputs were then fused with the static geographic factors, and the predicted first flowering date was generated through a regression layer, enabling regionally adaptive prediction. To ensure comparability of results, LSTM and Bi-LSTM models were simultaneously constructed as baseline models using identical data preprocessing and training procedures.Third, Bayesian optimization was applied for automatic hyperparameter tuning, during which key parameters, including learning rate, number of network layers, number of hidden units, regularization terms, and optimizers, were systematically searched, and the optimal configuration was selected based on validation performance. Finally, a cross-year validation strategy was adopted to evaluate model generalization ability: Data from 2019 to 2021 were used as the modeling dataset (training and validation), while the observed first flowering date in 2022 served as an independent test dataset. The predictive performance of all models was evaluated using three widely recognized metrics: root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R). [Results and Discussions] The proposed model achieved an RMSE of 1.34 d, a MAE of 1.13 d, and the R of 0.84 on the test dataset, with most prediction errors concentrated within a range of 0－2 d. Validation results indicated that the proposed approach was capable of providing stable predictions approximately 15－20 d in advance within the study area. Further comparative analysis demonstrated that the Bi-LSTM architecture more effectively exploited both forward and backward dependencies in the pre-flowering temperature time series, thereby offering a more stable temporal representation for regression-based prediction of the first flowering date. Building upon this structure, the introduction of three attention heads: the local dependency head, the global trend head, and the cumulative feature head, enabled the model to more explicitly distinguish and utilize short-term fluctuations, stage-wise trends, and cumulative temperature effects. This targeted extraction of multi-scale time-varying information contributed to reduced prediction errors and improved overall prediction accuracy. Ablation experiments involving static geographic factors further verified the necessity of the spatial adaptability component. When the elevation was removed, the RMSE increased from 1.34 d to 1.45 d. Removing latitude and longitude led to a larger increase in RMSE to 2.54 d, and when both elevation and geographic coordinates were excluded, the RMSE further rose to 2.69 d accompanied by a decrease in correlation. These results indicated that geographic factors provided effective spatial constraints, which supported the learning of location-specific phenological responses across different sampling sites. In addition, spatial prediction maps revealed that the first flowering date in the study area exhibited a gradient distribution with respect to elevation to a certain extent. This spatial pattern was consistent with the modeling rationale of incorporating geographic factors into a unified prediction framework. [Conclusions] This study proposes a deep learning-based prediction method for the first flowering date of apples that integrates multi-dimensional temperature features, a multi-head attention mechanism, and geographic factors. The proposed method achieves relatively high prediction accuracy in cross-year forecasting and enables spatially adaptive prediction of the first flowering date of apples. These findings provide a new data-driven technical pathway for refined prediction of apple flowering phenology and offer important technical support for orchard flowering management, frost damage prevention, and agricultural production decision-making.
[Objective] Detecting dense and small aquaculture net cages in complex backgrounds is difficult, the purpose of this study is to build a specialized dataset and design a targeted detection model that enhances recognition accuracy and robustness for practical aquaculture management. [Methods] A dataset of aquaculture net cages was constructed using high-resolution remote sensing imagery collected from seven representative farming regions (Australia, Canada, Chile, Croatia, Greece, China, and the Faroe Islands), and Cage-YOLO, a deep learning model based on YOLOv5, was proposed for detecting dense and small aquaculture net cages. First, an adaptive dense perception algorithm was introduced, which automatically selects and generates feature maps that reflect the high-density distribution of small aquaculture net cages. Second, an enhanced module based on spatial pyramid pooling fast was integrated to effectively reduce background noise interference and improve global feature extraction capabilities. Finally, a mixed attention block was incorporated to further enhance the model's perception of dense and small objects. [Results and Discussions] Experimental results showed that the proposed Cage-YOLO achieved improvements over the original YOLOv5 in terms of precision, recall, and mean average precision by 5.6, 21.8, and 17.4 percentage points, respectively. The model size was maintained at 16.9 MB, demonstrating both strong performance and deployment advantages. [Conclusions] This study provides a new approach for dense and small object detection and offers technical support for the intelligent management of marine cage aquaculture.
[Objective] Accurate and timely crop mapping is fundamental for agricultural management, yield forecasting, and food security assessment. However, in mountainous and hilly regions characterized by frequent cloud cover and highly fragmented farmland, crop classification methods relying solely on optical remote sensing data are severely constrained. Persistent cloud contamination introduces data gaps and temporal inconsistencies in optical image time series, significantly degrading classification accuracy and robustness. To address these limitations, a robust and adaptive deep learning framework is developed capable of effectively integrating multi-modal remote sensing data. The primary objective is to enhance crop classification accuracy and stability under complex conditions where optical observations are scarce or unreliable, thereby supporting reliable agricultural monitoring in cloudy and fragmented landscapes. [Methods] A novel deep neural network architecture named 3D convolutional neural network based on attention mechanism (Attention-3DCNN) was proposed, designed to jointly exploit multi-temporal optical and synthetic aperture radar (SAR) observations. The model integrated Sentinel-2 multispectral time-series imagery with weather-insensitive Sentinel-1 SAR data through a dedicated cross-modal fusion strategy driven by a triple-attention mechanism. The network adopted a dual-branch feature extraction architecture. For the Sentinel-2 data, a hybrid module combining three-dimensional and two-dimensional convolutional neural networks (3D-CNN and 2D-CNN) was employed to capture discriminative spatiotemporal features and crop phenological dynamics across the growing season. This design enabled effective modeling of the spectral-temporal interactions inherent in crop development. For the Sentinel-1 SAR data, depthwise separable convolutions were utilized to efficiently extract spatial and textural features related to crop structure and surface scattering characteristics while reducing computational complexity. Features extracted from both modalities were subsequently integrated using a custom-designed attention-based fusion module. This module consisted of three complementary attention mechanisms: channel attention, temporal attention, and spatial attention. Residual connections were incorporated throughout the network to facilitate stable training and effective gradient propagation. The proposed model was evaluated on two datasets to assess both its performance and generalizability. The first was the publicly available panoptic agricultural satellite time series (PASTIS) benchmark dataset from France, which contained dense time-series observations and multiple crop classes. The second was a real-world dataset constructed for Yishui county, Shandong province, China, which was characterized by high cloud frequency (approximately 33%), highly fragmented farmland (average parcel size < 0.5 hm2), and a relatively simple crop rotation system. Comparative experiments were conducted against several state-of-the-art models, including 3D-ConvSTAR, UNet++, Self-Attention 3D, CNN-LSTM dual-stream network, and TGF-Net. Ablation studies were also performed to quantify the contribution of each attention component. [Results and Discussions] Experimental results demonstrated that Attention-3DCNN consistently outperformed all baseline methods on both datasets. On the PASTIS benchmark, the model achieved an overall accuracy (OA) of 97.5%, confirming its strong classification capability under favorable observation conditions. On the more challenging Yishui county dataset, Attention-3DCNN attained an OA of 93%, outperforming the other comparison models. Ablation experiments confirmed the effectiveness of the proposed triple-attention mechanism, as removing any attention component resulted in a clear reduction in classification performance. Under heavy cloud coverage, Attention-3DCNN exhibited the smallest accuracy degradation, with an OA drop of only 3.6 percentage points, indicating its ability to adaptively rely on SAR information when optical data quality deteriorated. In regions with highly fragmented farmland, the proposed model also maintained the highest accuracy and the smallest performance decline (2.8 percentage points), benefiting from the spatial attention mechanism. Moreover, attention visualization provided meaningful interpretability. Temporal attention peaks aligned with key crop phenological stages, while channel attention highlighted spectrally and physically informative optical bands and SAR polarizations, which was consistent with established agronomic and remote sensing knowledge. [Conclusions] This study presents the Attention-3DCNN model for accurate and robust crop classification in regions affected by persistent cloud cover and fragmented agricultural landscapes. By fusing Sentinel-2 optical and Sentinel-1 SAR time-series data through a channel-temporal-spatial triple-attention mechanism, the proposed framework enables adaptive integration of complementary multi-modal information. The model achieves outstanding performance on both benchmark and real-world datasets, demonstrates strong robustness under adverse conditions, and offers enhanced interpretability. Overall, the proposed approach provides a reliable and practical solution for crop mapping in complex agricultural environments.
