[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] The vegetable supply chain is characterized by multiple production entities, diverse product varieties, and complex circulation processes, which often result in low data accuracy, label forgery, data tampering, and difficulties in cross-enterprise collaboration in traditional traceability systems. Furthermore, the rapid development of quantum computing poses significant threats to existing cryptographic foundations by enabling efficient factorization or discrete logarithm attacks. This study aimed to design and implement a vegetable supply chain anti-counterfeiting and traceability system that integrates the Internet of Things (IoT), blockchain technology, and a post-quantum enhanced elliptic curve integrated encryption scheme (PQ-ECIES). The system seeks to enhance the trustworthiness, privacy protection, and collaborative efficiency of supply chain data management, while maintaining practical performance for IoT devices and high-frequency data uploading scenarios. [Methods] The proposed system was constructed on an IoT framework incorporating nine categories of devices. A registration and admission mechanism was developed to establish a trusted mapping between "device–enterprise–data", effectively preventing unauthorized entities from uploading forged data. At the data layer, collected information was divided into public and private categories: Public data were uploaded directly to the blockchain, while private data were encrypted using PQ-ECIES before being stored on-chain. Smart contracts automated processes such as data classification, permission verification, and encrypted data querying, thus reducing human intervention and ensuring compliance. PQ-ECIES was designed by combining elliptic curve cryptography (ECC) and the Kyber algorithm from lattice-based post-quantum cryptography. A dual-key mechanism was employed to generate session keys, where an ECC-derived shared secret was combined with a Kyber-derived shared secret through SHA3-256 hashing, followed by key derivation for encryption and authentication. This design provided resilience against Shor's algorithm and other quantum attacks while maintaining efficiency compatible with IoT devices. The blockchain system was implemented using Hyperledger Fabric 1.4.4, with seven organizational nodes and the Raft consensus mechanism. Performance testing included evaluations of data collection accuracy, on-chain latency, query latency, and encryption performance across RSA, advanced encryption standard (AES), and PQ-ECIES. [Results and Discussions] The IoT-based data collection achieved significantly higher accuracy than manual input, particularly in large-scale sample scenarios such as pesticide residue testing. The average latency for data uploading to the blockchain was 2 879 ms, while data query latency averaged 122 ms, both of which met the practical requirements of vegetable supply chain applications. In cryptographic performance testing, PQ-ECIES achieved encryption and decryption of 128 B plaintext in approximately 10－30 ms, outperforming RSA (50－80 ms) and only slightly slower than AES (<10 ms). This result indicates that PQ-ECIES achieved an optimal trade-off between efficiency and security, offering asymmetric encryption benefits such as key distribution and identity verification, along with strong post-quantum resistance. Simulation under quantum attack models confirmed that traditional ECC and AES could be compromised within hours using Shor's and Grover's algorithms, whereas PQ-ECIES maintained resilience due to the lattice-based hardness assumptions of Kyber. From a system-level perspective, three major contributions were identified. First, trustworthiness was enhanced by binding IoT devices to enterprises through Bluetooth-based verification and blockchain's immutable ledger, ensuring data authenticity at the source. Second, privacy protection was achieved by adopting graded visibility: Consumers accessed only public data such as testing results and logistics status, while regulators could decrypt private information (e.g., production location and batch details) via authorized keys, balancing transparency with confidentiality. Third, collaboration across enterprises was improved through the consortium blockchain structure and Fabric channel mechanisms, which eliminated information silos and enabled selective data sharing in real time, reducing inter-organizational access time from weeks to minutes. Experimental validation confirmed that IoT-based collection significantly improved accuracy, blockchain integration achieved acceptable on-chain and query latency, and PQ-ECIES outperformed RSA while offering post-quantum resistance not available in AES. [Conclusions] This study proposed and implemented a vegetable supply chain traceability system that integrates IoT, blockchain, and PQ-ECIES. By deploying nine categories of IoT devices, establishing trusted device-enterprise mappings, and incorporating blockchain's decentralized and tamper-proof ledger, the system ensured reliable data collection and storage. The integration of PQ-ECIES provided dual cryptographic protection, balancing efficiency with long-term quantum security. Beyond technical performance, the system enhanced trust, privacy, and collaboration across the vegetable supply chain, effectively addressing common issues of data forgery, tampering, and cross-enterprise coordination.Overall, the proposed framework demonstrates high potential for real-world deployment in agricultural supply chains, offering a secure, efficient, and future-proof solution to ensure authenticity, reliability, and transparency in vegetable traceability. The study also provides a reference model for extending post-quantum blockchain-based traceability to other agri-food sectors facing similar challenges.

[Objective] In the agricultural domain, user demand texts serve as essential primary sources for agricultural extension, production management, and policy services. However, these texts typically contain highly specialized terminology, exhibit non-standard, colloquial, and diverse linguistic expressions, present fragmented semantics, and rely heavily on contextual reasoning. Such characteristics make them difficult to parse accurately using traditional rule-based approaches or shallow machine learning models. Consequently, these limitations often lead to biased demand classification and incomplete extraction of key factors, thereby constraining the quality of data available for intelligent agricultural decision-making. To address these challenges, the aim of this research is to develop a robust, domain-adapted, and highly interpretable structured analysis method for agricultural user demands. [Methods] Agri-NeedAgent, an agricultural user demand analysis framework, was proposed based on a "three-stage training + multi-agent collaboration" paradigm. First, during the domain knowledge pretraining stage, 80 000 agriculture-related texts, including crop cultivation manuals, pest and disease control guides, agricultural policy documents, and farmer consultation records, were used to construct domain-specific semantic understanding, thereby enhancing the model's capability to interpret agricultural terminology, dialectal expressions, contextual logic, and implicit semantics. Second, in the instruction fine-tuning stage, 6 320 annotated samples in an "instruction-input-output" format were employed to establish an explicit mapping from raw demand texts to structured outputs. Third, in the agricultural knowledge low-rank adaptation stage, Low-rank Adaptation (LoRA) was applied to perform lightweight parameter tuning on task-specific agents, enabling targeted adaptation for demand classification and key-factor extraction tasks. Built upon the above training process, a multi-agent collaborative framework was constructed, in which the manager agent was responsible for task scheduling and quality control, while task agents were designed to perform demand classification, key-factor extraction, and explanation generation, respectively. Through this division of labor and collaborative mechanism, the framework achieved efficient and structured analysis of agricultural user demands. [Results and Discussions] Experimental results demonstrate that the proposed Agri-NeedAgent achieved a demand classification accuracy of 84.6%, a key-factor extraction F₁-Score of 85.2%, a structured interface compliance rate of 94.2%, and an interpretability score of 90.2.These results showed clear improvements over traditional deep learning models such as Bidirectional Encoder Representations from Transformers (BERT) as well as general-purpose large language models (LLMs) without domain adaptation. The findings confirmed the critical role of domain knowledge injection, explicit task alignment, and multi-agent specialization in enhancing semantic understanding and structured analysis of agricultural texts. Ablation experiments further validated the effectiveness of each component. Removing domain pretraining or LoRA fine-tuning resulted in substantial performance degradation in classification and key-factor extraction, indicating the necessity of domain adaptation and task-specific optimization for handling non-standard agricultural expressions. Moreover, eliminating the manager agent or the Reasoning and Acting (ReAct) mechanism significantly reduced structured interface compliance and interpretability, highlighting the importance of task coordination, intermediate verification, and multi-step reasoning for ensuring logical consistency and output completeness. Additionally, removing the external knowledge base reduced the interpretability score from 90.2 to 77.6, underscoring its essential role in providing theoretical grounding, reasoning support, and professional explanations. Although the multi-agent collaboration introduced an additional inference overhead of approximately 140 ms, the overall per-sample inference time remained within 225 ms, meeting the real-time requirements of agricultural consultation scenarios. [Conclusions] Supported by a "three-stage training + multi-agent collaboration" framework, LLMs can effectively address challenges posed by non-standard expressions, semantic fragmentation, and multi-factor reasoning in agricultural user demand texts. The proposed method demonstrated significant improvements in demand classification, key-factor extraction, structured output compliance, and interpretability, providing high-quality and traceable structured data for intelligent agricultural decision-making. After domain adaptation and task-specific tuning, the model not only gains enhanced capability for deep semantic analysis of agricultural user demands but also ensures the completeness and interpretability of outputs through multi-agent coordination. Although the current workflow still requires optimization in terms of data preparation, staged training, and knowledge-base updating, future work will focus on expanding region-specific and emerging-technology-related demand data, developing a dynamically updated agricultural knowledge system, improving multi-agent coordination efficiency, and exploring cross-lingual agricultural demand analysis to further promote the application and deployment of agricultural large models across broader scenarios.

[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] There are several critical challenges in automated safflower harvesting, particularly the inefficiencies in path planning, suboptimal route quality, and limited decision-making capability under dynamic and complex environments. To solve these issues, the problem was formulated as a three-dimensional traveling salesman problem and an enhanced reinforcement learning model named actor-critic reinforcement learning pointer network (AC-RL-PtrNet) was proposed, specifically designed for deployment on intelligent safflower picking robots in agricultural settings. [Methods] First, to address the inherent limitations of conventional attention mechanisms in dynamic environments with complex spatial structures, an enhanced attention module was proposed based on the dynamic exponential moving average framework. By combining multi-head attention, spatial distance encoding, and adaptive exponential smoothing, the improved design allowed the model to better capture long-range dependencies and spatial context among safflowers. Meanwhile, to minimize computational cost while preserving inference quality, a structured pruning approach was adopted, which selectively removed redundant connections in the long short-term memory gates and fully connected layers. In parallel, the critic network was redesigned to improve learning stability and accuracy. This was achieved through the inclusion of batch normalization, residual feature aggregation, and a multi-layer value estimation head, all of which contributed to a tighter actor-critic synergy during policy training. [Results and Discussions] To quantitatively assess the impact of each component, ablation experiments were conducted across various configurations. The results confirmed that each module contributed distinct benefits, while their combination yielded the highest improvements in both planning precision and inference efficiency. This coordinated actor-critic design effectively enhanced both trajectory quality and decision stability, which were critical in sequential robotic picking tasks. Experimental results also demonstrated that, compared with traditional swarm intelligence algorithms particle swarm optimization (PSO), ant colony optimization (ACO), and non-dominated sorting genetic algorithm, the proposed AC-RL-PtrNet model achieved a planning time improvement ranging from -2.63% to 61.87% on the 25-target dataset and from 22.93% to 59.1% on the 31-target dataset. Meanwhile, the optimized paths were significantly shortened across different planning instances, indicating robust generalization capability under varied problem scales. Furthermore, field experiments provided concrete validation of the model's practical applicability. When deployed on a mobile picking robot in real safflower fields, the AC-RL-PtrNet achieved a 9.56% reduction in path length and 5.43% time saved for a 25-target picking task, and a 20.17% path reduction and 29.70% time saving for a 31-target scenario involving a different safflower variety. Overall, these results all indicated that the proposed method exhibited significant advantages in enhancing path planning efficiency and optimizing path quality. [Conclusions] This study offers a practical solution for achieving efficient and robust automatic picking by safflower picking robots and provides new insights into solving 3D combinatorial optimization problems.

[Objective] In the eel (Monopterus albus) farming system used in feed distribution research of mechanical arm, the challenges included slow path planning speeds, excessive trajectory redundancy, and suboptimal obstacle avoidance success rates within confined operational spaces. To mitigate these issues, an improved path planning algorithm, based on the bidirectional rapidly-exploring random tree star (BI-RRT*) algorithm was proposed. The primary aim was to significantly enhance the motion efficiency and task success rate of robotic arms operating in complex, constrained environments. [Methods] The proposed improved BI-RRT* algorithm integrated an adaptive goal-biased strategy with an enhanced artificial potential field (APF) method. The algorithm's framework comprises three core components: a high-quality sampling strategy, an efficient search strategy, and a path optimization algorithm. For the high-quality sampling strategy, an adaptive goal-biased approach was introduced to overcome the limitations of inefficient random sampling and slow convergence rates characteristic of traditional BI-RRT algorithms in complex environments. This strategy dynamically adjusted the generation of sampling points, moving beyond purely random selection. Instead, it prioritized sampling regions in the vicinity of the target, guided by the target direction and a predefined bias probability. This mechanism substantially augmented the growth propensity of the search tree towards the target area, effectively reducing the stochasticity of random sampling and consequently accelerating the path search process. To enhance search efficiency and prevent the algorithm from converging to local optima, an improved APF was incorporated into the node expansion process. The APF was refined to achieve superior integration with the BI-RRT framework. During each new node expansion, in addition to considering the inherent random exploration characteristics of BI-RRT, a directional attractive field was superimposed. This attractive field not only originated from the ultimate target point but also factored in the current growth orientation of the search tree and localized environmental information. Specifically, a composite attractive function was devised, which synergized the attractive force exerted by the target point on the current node with the attraction from potential "guide points". Concurrently, the computation of the repulsive field was optimized to more precisely delineate the geometry and proximity of obstacles, thereby circumventing common issues such as "oscillation" and "deadlock" prevalent in traditional APF. Through this methodology, the algorithm was able to more effectively steer the search tree to circumvent obstacles and rapidly converge towards the target region, significantly bolstering the directness of the search and successfully preventing the algorithm from becoming ensnared in suboptimal local solutions. For the path optimization algorithm, following the generation of an initial feasible path, a greedy optimization strategy was employed for path pruning and smoothing. This was executed to yield an optimal path characterized by reduced length, enhanced smoothness, and improved conformity with the kinematic properties of the robotic arm. Path pruning was initially applied to eliminate redundant nodes; if a collision-free direct connection existed between two non-adjacent nodes, intermediate nodes were excised, thereby substantially abbreviating the path length. Subsequently, path smoothing techniques, such as B-spline curves or cubic spline interpolation, were introduced to enable the robotic arm to execute movements with greater stability and efficiency during actual operation, mitigating impact and vibration. This two-stage optimization procedure ensured that the final generated path was not merely feasible but also optimal across metrics of length, smoothness, and motion efficiency. [Results and Discussions] To comprehensively validate the performance of the proposed algorithm, a two-stage experimental verification was conducted. Initially, comparative simulations were performed in both two-dimensional (2D) and three-dimensional (3D) environments utilizing the Matlab platform. These simulation scenarios were meticulously engineered to encompass three archetypal environments—simple, complex, and narrow passages—thereby emulating the diverse obstacle configurations potentially encountered in industrialized eel aquaculture. The results demonstrated that, concerning both path planning speed and quality, the improved BI-RRT* algorithm significantly surpassed RRT, APF-RRT*, and traditional BI-RRT* algorithms across all tested environments, substantiatingthe theoretical superiority and inherent robustness of the improved BI-RRT* algorithm proposed in this study across varying complex environments. To further ascertain the engineering applicability and practical potential of the algorithm, an eel feeding robotic arm simulation system was meticulously constructed based on the robot operating system and MoveIt frameworks. This system precisely emulated the kinematics, dynamics, and obstacle distribution pertinent to an industrialized eel aquaculture environment. During simulated continuous feeding tasks, the improved BI-RRT* algorithm consistently exhibited impressive and outstanding performance. Its average running time was merely 2.1 s, representing a substantial 41.6% reduction compared to the traditional BI-RRT*. The average length of the planned path was recorded at only 1 680 mm, with an average of 180 nodes, indicating a significant reduction in path redundancy. Furthermore, the algorithm achieved an impressive obstacle avoidance success rate of 96% in complex confined spaces. These empirical findings not only validated the algorithm's effectiveness but also underscored its immense potential for practical engineering applications. [Conclusions] The experimental results conclusively demonstrated that the improved BI-RRT* algorithm significantly enhanced the path planning efficiency and trajectory quality of robotic arms operating within confined spaces. It also exhibited exceptionally high reliability in obstacle avoidance, thereby effectively addressing the automated feeding requirements of industrialized eel aquaculture. The algorithmic framework possessed considerable generality, offering valuable theoretical insights and technical precedents for resolving analogous robotic arm path planning challenges in other agricultural automation contexts.

[Objective] Large language models (LLMs) have demonstrated strong capabilities in natural language understanding, knowledge integration, and complex reasoning, offering new opportunities for intelligent decision-making in agriculture. However, their direct application in agricultural production and facility environment control remains challenging due to strong physical constraints and high operational risks. The lack of real-world interaction and executable decision grounding limits the practical effectiveness of conventional LLMs in such scenarios. To address these challenges, a tool-augmented LLM-based agricultural intelligent agent system, termed AgriAgent, was proposed, and a digital-twin-based evaluation platform for agricultural decision-making was developed. By integrating a high-fidelity digital twin environment with an end-to-end agent architecture, the decision-making performance of agricultural intelligent agents with different parameter scales was systematically evaluated across multiple crops and climate scenarios. [Methods] A high-fidelity agricultural digital twin evaluation platform was constructed using the decision support system for agrotechnology transfer (DSSAT) v4.8 crop growth model as the core simulation engine to model crop growth under diverse environmental conditions and management strategies. Meteorological driving data were obtained from the Seoul Historical Weather Data dataset. Through data cleaning, missing-value imputation, unit normalization, and time-series reconstruction, the raw meteorological data were transformed into standardized inputs compatible with DSSAT. Three climate scenarios representing different environmental complexities were designed, including a regular scenario, a perturbed scenario, and an extreme scenario. The regular scenario employed historical observations, the perturbed scenario introduced stochastic disturbances to simulate short-term climate variability, and the extreme scenario incorporated multi-factor coupled stresses such as high temperatures and excessive precipitation during sensitive growth stages. In total, 90 annual climate driving sequences were generated. Fixed soil profile parameters calibrated by domain experts were applied across all simulations to minimize confounding effects. Within this digital twin environment, a tool-augmented agricultural intelligent agent, AgriAgent, was implemented using a modular architecture consisting of a sensor module, memory module, retriever, large language model, and tool executor, forming a closed-loop decision-making framework. In each decision cycle, the agent perceived environmental and crop state information, including soil moisture and nutrient status, meteorological conditions, crop growth stages, and stress indicators. State summaries and historical decisions were stored in memory, while agronomic knowledge was retrieved through a retrieval-augmented generation mechanism. Based on integrated information, the LLM generated structured environmental control commands in JSON format, which were validated and constrained by the tool executor before updating the DSSAT environment. The system supported irrigation, supplementary lighting, ventilation, heating, fertilization, and CO₂ enrichment. Five representative crops: maize, millet, sugar beet, tomato, and cabbage were simulated under the three climate scenarios over complete growing seasons, resulting in 450 crop-scenario combinations. An unmanaged DSSAT simulation served as the baseline. AgriAgent models with three parameter scales (1.5B, 3B, and 7B), built on the Qwen2.5 series, were evaluated. Crop economic yield expressed as dry matter at physiological maturity was adopted as the evaluation metric. [Results and Discussions] The results showed that AgriAgent consistently outperformed the baseline across all crops and climate scenarios, with model scale exerting a significant influence on decision-making performance. AgriAgent-7B achieved the best overall performance under regular, perturbed, and extreme scenarios, demonstrating strong generalization ability and environmental adaptability. By dynamically adjusting water, nutrient, light, and thermal management strategies, the agent effectively mitigated environmental stresses even under multi-factor coupled extreme climate conditions. Under extreme scenarios, AgriAgent-7B increased yields by 463.60% for maize, 351.20% for millet, 125.40% for sugar beet, 1 537.46% for tomato, and 1 185.14% for cabbage compared with the baseline. Particularly large gains were observed for high-value crops such as tomato and cabbage, highlighting the advantages of the proposed framework for precision-controlled facility agriculture. In contrast, AgriAgent-1.5B exhibited performance comparable to the baseline, while AgriAgent-3B achieved moderate improvements but remained inferior to the 7B model. These findings indicate a clear scaling effect, suggesting that larger models possess stronger capabilities in multi-source information integration, long-term temporal reasoning, and adaptation to complex environments. [Conclusions] This study developed a digital-twin-based agricultural decision evaluation platform and proposed a tool-augmented, end-to-end agricultural intelligent agent named AgriAgent. Experiments across multiple crops and climate scenarios verified the effectiveness and robustness of the proposed framework for dynamic agricultural decision-making. The results demonstrate that integrating knowledge retrieval, reasoning, and tool execution within a closed-loop LLM-based agent enables stable, reliable, and adaptive environmental control, providing a feasible technical pathway and standardized evaluation paradigm for intelligent agriculture.

[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] 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.