Table of Content

30 May 2025, Volume 7 Issue 3

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Overview Article

Smart Supply Chains for Agricultural Products: Key Technologies, Research Progress and Future Direction |

HAN Jiawei, YANG Xinting

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.

Agricultural Big Data Governance: Key Technologies, Applications Analysis and Future Directions |

GUO Wei, WU Huarui, ZHU Huaji, WANG Feifei

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.

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

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.

Advances, Problems and Challenges of Precise Estrus Perception and Intelligent Identification Technology for Cows |

ZHANG Zhiyong, CAO Shanshan, KONG Fantao, LIU Jifang, SUN Wei

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.

Electrochemical Sensors for Plant Active Small Molecule Detection: A review |

ZHANG Le, LI Aixue, CHEN Liping

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.

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

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.

Information Processing and Decision Making

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

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.

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

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.

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

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.

Dynamic Prediction Model of Crop Canopy Temperature Based on VMD-LSTM |

WANG Yuxi, HUANG Lyuwen, DUAN Xiaolin

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.

High-Precision Fish Pose Estimation Method Based on Improved HRNet |

PENG Qiujun, LI Weiran, LIU Yeqiang, LI Zhenbo

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.

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

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.

The Lightweight Bee Pollination Recognition Model Based On YOLOv10n-CHL |

CHANG Jian, WANG Bingbing, YIN Long, LI Yanqing, LI Zhaoxin, LI Zhuang

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.

Multi Environmental Factor Optimization Strategies for Venlo-type Greenhouses Based on CFD |

NIE Pengcheng, CHEN Yufei, HUANG Lu, LI Xuehan

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.

Intelligent Equipment and Systems

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

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.