[Objective] Maize is one of the most important staple crops in the world and serves as a cornerstone of food security and agricultural sustainability. Accurate and timely prediction of maize yield is essential for optimizing agricultural management practices, supporting market regulation, and guiding policy decisions related to food supply and climate adaptation. In recent years, data-driven yield prediction methods based on machine learning and deep learning have achieved notable improvements in predictive accuracy. However, most existing approaches primarily rely on statistical correlations among variables and often treat influencing factors as independent predictors, without explicitly addressing the complex causal mechanisms and time-lagged interactions that govern crop growth processes. This limitation may lead to reduced model interpretability and compromised robustness under changing environmental conditions. To address these challenges, a novel maize yield prediction framework that integrates causal inference with a hybrid deep learning model was proposed, aiming to improve both predictive performance and mechanistic understanding. [Methods] Multi-source heterogeneous datasets collected across the maize growing season were utilized, including remote sensing-derived vegetation indices, meteorological variables (such as temperature and precipitation), soil profile moisture measurements at multiple depths, and crop observation data corresponding to key phenological stages. First, the Peter-Clark and momentary conditional independence (PCMCI) causal discovery algorithm was applied to systematically identify causal relationships between maize yield and its potential driving factors. The PCMCI method enables the detection of both contemporaneous and time-lagged causal links while effectively controlling for confounding effects in high-dimensional time series data. Through this process, the causal structure of yield formation was explicitly characterized, and key variables with statistically significant causal impacts were selected as inputs for the prediction model. Subsequently, a hybrid moving average, convolutional neural network-long short-term memory (MA-CNN-LSTM) model was constructed to capture the complex spatiotemporal patterns in the causally screened input variables. Specifically, a moving average module was employed as a preprocessing step to suppress high-frequency noise and enhance signal stability. A CNN was then used to extract latent correlation features among multiple variables, reflecting their joint influence on yield formation. Finally, an LSTM network was adopted to model temporal dependencies and cumulative effects across the growing season, enabling effective representation of dynamic yield responses. [Results and Discussions] The causal analysis revealed that soil moisture at depths of 10 cm and 50 cm exerted a significant positive influence on maize yield (P < 0.01), with deeper soil moisture showing a stronger and more persistent time-lagged effect. This finding highlighted the critical role of subsurface water availability in sustaining crop growth during later developmental stages. In addition, vegetation indiced such as the modified chlorophyll absorption ratio index and the normalized difference vegetation index exhibited significant short-term causal relationships with yield during the mid-growth stage of maize, indicating their sensitivity to canopy structure and photosynthetic activity during this period. Comparative experiments conducted against traditional statistical models and conventional machine learning approaches demonstrated that the proposed PCMCI-MA-CNN-LSTM framework consistently achieved superior predictive performance. On the test dataset, the coefficient of determination (R²) reached 0.955, while the mean absolute error (MAE) and root mean square error (RMSE) were reduced to 1.201 kg/mu and 1.474 kg/mu (1 hm²=15 mu). These results indicated that incorporating causal variable selection effectively enhances model accuracy and stability by reducing redundant and spurious correlations. [Conclusions] The results confirm that incorporating causal analysis into yield modeling provides a robust basis for identifying key driving variables and effectively enhances the accuracy and interpretability of maize yield prediction. The proposed framework offers a promising approach for precision agriculture and decision support in crop yield forecasting, particularly under complex and dynamic agro-environmental conditions.

[Objective] The precise quantification of rice seeds within individual cavities of seedling trays constitutes a critical operational parameter for optimizing seeding efficiency and fine-tuning the performance of air-vibration precision seeders. Achieving high accuracy in this task directly impacts resource utilization, seedling uniformity, and ultimately crop yield. However, the operational environment presents significant challenges, including complex backgrounds, seed overlap, variations in lighting and seed orientation, and the inherent difficulty of distinguishing individual seeds within dense clusters. These factors often lead to suboptimal performance in existing automated detection systems, manifesting as low detection accuracy and an inability to achieve robust, precise instance segmentation of individual rice seeds. To address these persistent limitations and advance the state-of-the-art in precision seeding monitoring, an integrated framework for rice seed instance segmentation was proposed. The core innovation lies in the synergistic combination of a cross-modal grounding generation (CGG) network with a pretrained model, which is designed to leverage complementary information from visual and textual domains. [Methods] The proposed methodology fundamentally aimed to bridge the gap between visual perception and semantic understanding within the specific context of rice seed detection. The CGG-pretrained model framework achieved this through deep joint alignment of visual features extracted from seedling tray images and textual features derived from contextual knowledge. This cross-modal grounding enabled collaborative learning, where the visual processing stream (handling object localization and pixel-level segmentation) was continuously informed and refined by the semantic understanding stream (interpreting context and relationships). Specifically, the visual backbone network processes input imagery to generate feature maps, while the pretrained language model component, which utilized contextual embeddings, generated semantically rich textual representations. The CGG module acted as the fusion engine, establishing explicit correspondences between specific regions in the image (potential seeds or clusters) and relevant semantic concepts or descriptors provided by the pretrained model. This bidirectional interaction significantly enhanced the model's ability to disambiguate overlapping seeds, resolved occlusions, and accurately delineated individual seed boundaries under challenging conditions. Key technical innovations validated through rigorous ablation studies include: (1) The strategic use of the bootstrapping language-image pre-training (BLIP) model for generating high-quality pseudo-labels from unlabeled or weakly labeled image data, facilitating more effective semi-supervised learning and reducing annotation burden, and (2) the application of bidirectional encoder representations from transformers (BERT)-based word embed to capture deep semantic relationships and contextual nuances within textual descriptors related to seeds and seeding environments. [Results and Discussions] The ablation experiments demonstrated a pronounced synergistic effect when the core improvements were combined, resulting in a segmentation accuracy improvement exceeding 3 percentage points compared to the baseline model that lacking the integration. Comprehensive experimental evaluation demonstrated the superior performance of the proposed CGG model against established benchmarks. Under the standard intersection over union (IoU) threshold of 0.5, the model achieved a mean average precision (mAP) of 90.7% for bounding box detection (denoted as mAP50^bb for detection) and an outstanding 91.4% mAP for instance segmentation (denoted as mAP50^seg for segmentation). These results represented a statistically significant improvement over leading contemporary models, including region-based convolutional neural network (Mask R-CNN) and Mask2Former, which highlighted the efficacy of the cross-modal grounding approach in accurately localizing and segmenting individual rice seeds. Further validation within realistic seeding trial scenarios, which involved direct comparison with meticulous manual annotations, confirmed the model's practical robustness. The CGG model attained the highest accuracy in two critical operational metrics: (1) Precision in segmenting individual seed instances (single-seed segmentation accuracy), and (2) accuracy in determining the exact seed count per cavity, and it achieved an average accuracy of 88% for per-cavity quantification. Moreover, the model exhibited superior performance in minimizing estimation errors for cavity seed counts, as evidenced by its significantly lower error metrics: a root mean square error (RMSE) of 16.8 seeds, a mean absolute error (MAE) of 13.7 seeds, and a mean absolute percentage error (MAPE) of 2.46%. These error values were markedly lower than those recorded by the comparison models, which underscored the CGG model's enhanced reliability in practical counting tasks. The discussion contextualized these results and attributed the performance gains to the model's ability to leverage semantic context to resolve ambiguities inherent in visual-only approaches, particularly in dense and overlapping seed scenarios common in precision seeding trays. [Conclusions] The developed CGG-pretrained model integration presents a significant advancement in automated monitoring for precision rice seeding. The model successfully addresses the core challenges of low detection accuracy and imprecise instance segmentation for seeds in complex environments. Its high accuracy in both individual seed segmentation and per-cavity seed count quantification, coupled with low error rates, demonstrates strong potential for practical deployment. Importantly, the model enables real-time detection of rice seeds during the image analysis stage, this functionality provides a quantifiable, data-driven basis for making immediate operational decisions, most notably enabling the targeted precision reseeding of empty or under-seeded cavities identified during the seeding process. By ensuring optimal seed placement and density from the outset, the technology contributes directly to improved resource efficiency (reducing seed waste), enhanced seedling uniformity, and potentially higher crop yields. Future work will focus on further optimizing inference speed for higher-throughput seeding lines and exploring generalization to other crop types and seeding mechanisms.

[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] 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] 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 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] 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] Pepper cultivation frequently faces challenges from diseases and pests, and early detection is critical for reducing yield losses. However, existing detection models often suffer from limitations such as insufficient feature extraction for subtle lesions, loss of edge information due to complex backgrounds, and high missed detection rates for small lesions. To address these issues, the YOLO-MDFR (You Only Look Once), a lightweight detection algorithm was proposed based on an enhanced YOLOv12s, specifically designed for accurate identification of pepper leaf diseases and pests in complex natural environments. [Methods] The dataset was established in the primary pepper cultivation zone of Gangu county, Tianshui city, Gansu province. The cultivated variety was the locally dominant Capsicum annuum L. var. conoides (Mill.). Data collection was conducted from March 15 to May 20, 2024. The collected samples included four categories of pepper leaves: healthy leaves, leaves damaged by thrips, leaves infected with tobacco mosaic virus exhibiting yellowing symptoms, and leaves affected by bacterial leaf spot. First, the original YOLOv12s backbone was replaced with an improved MobileNetV4 architecture to enhance lightweight performance while preserving feature extraction capability. Specifically, the original 5×5 standard convolutions in the bottleneck layers of MobileNetV4 were substituted with two sequential 3×3 depthwise separable convolutions. This design was based on the principle that two 3×3 convolutions achieve an equivalent receptive field (matching the 5×5 coverage) while reducing parameter count—depthwise separable convolutions further decompose spatial and channel convolution, minimizing redundant computations. Second, a novel dimensional frequency reciprocal attention mixing transformer (D-F-Ramit) module was introduced to enhance sensitivity to lesion boundaries and fine-grained textures. The module first converted feature maps from the spatial domain to the frequency domain using discrete cosine transform (DCT), capturing high-frequency components often lost in spatial-only attention. It then integrated three parallel branches: channel attention, spatial attention, and frequency-domain attention. Finally, a residual aggregation gate-controlled convolution (RAGConv) module was developed for the neck network. This module included a residual aggregation path to collect multi-layer feature information and a gate control unit that dynamically weighted feature components based on their relevance. The residual structure provided a direct gradient propagation path, alleviating gradient vanishing during backpropagation and ensuring efficient information transfer during feature fusion. A systematic experimental framework was established to comprehensively evaluate model performance: (1) Ablation studies were conducted using a controlled variable approach to verify the individual contributions of the improved MobileNetV4, D-F-Ramit, and RAGConv modules; (2) Lesion scale sensitivity analysis assessed detection performance across different lesion sizes, with emphasis on small-spot recognition; (3) Resolution impact analysis evaluated five common input resolutions (320×320–736×736) to explore the trade-offs among accuracy, speed, and computational efficiency; and (4) Embedded deployment validation involved model quantization and implementation on the Rockchip RK3588 platform to measure inference speed and power consumption on edge devices. [Results and Discussions] The proposed YOLO-MDFR achieved an mAP@0.5 of 95.6% on this dataset. Compared to YOLOv12s, it improved accuracy by 2.0%, reduced parameters by 61.5%, and lowered computational complexity by 68.5%. Real-time testing showed 43.4 f/s on an NVIDIA RTX 4060 GPU (CUDA 12.2) and 22.8 f/s on a Rockchip RK3588 embedded platform with only 3.5 W power consumption—suitable for battery-powered field devices. Lesion-scale analysis revealed 33.5% accuracy for <16×16 pixel lesions critical for early detection. Confusion matrix evaluation reduced misclassification, bacterial leaf spot/thrips damage misrates fell from 5.8% to 2.1%, and tobacco mosaic virus/healthy leaves from 3.2% to 1.5%, resulting in an overall 2.3% misrate. Experiments across varying input resolutions revealed a clear performance–resolution trade-off. As resolution increased from 320×320 to 736×736, mAP rose from 89.5% to 96.2%, showing diminishing returns beyond 512×512. Concurrently, computational cost grew roughly quadratically, reducing inference speed from 65.2 f/s to 35.1 f/s. [Conclusions] This study presents YOLO-MDFR, a lightweight detection model for identifying pepper leaf diseases and pests under complex natural conditions. By integrating an improved MobileNetV4 backbone, a multi-dimensional frequency reciprocal attention mixing transformer (D-F-Ramit), and a residual aggregation gate-controlled convolution (RAGConv) module, YOLO-MDFR outperforms mainstream detection models in both accuracy and efficiency. Systematic deployment experiments yielded optimized configurations for different application scenarios. Despite its strong performance, the model shows limitations in robustness under extreme lighting, generalization to emerging diseases, and detection of small targets under occlusion. Future work will address these issues through ambient light data fusion, domain adaptation with semi-supervised learning, and binocular vision integration.

[Objective] Leaf diseases significantly affect both the yield and quality of tea throughout the year. To address the issue of inadequate segmentation finesse in the current tea spot segmentation models, a novel diagnosis of the severity of tea spots was proposed in this research, designated as MDC-U-Net3+, to enhance segmentation accuracy on the base framework of U-Net3+. [Methods] Multi-scale feature fusion module (MSFFM) was incorporated into the backbone network of U-Net3+ to obtain feature information across multiple receptive fields of diseased spots, thereby reducing the loss of features within the encoder. Dual multi-scale attention (DMSA) was incorporated into the skip connection process to mitigate the segmentation boundary ambiguity issue. This integration facilitates the comprehensive fusion of fine-grained and coarse-grained semantic information at full scale. Furthermore, the segmented mask image was subjected to conditional random fields (CRF) to enhance the optimization of the segmentation results [Results and Discussions] The improved model MDC-U-Net3+ achieved a mean pixel accuracy (mPA) of 94.92%, accompanied by a mean Intersection over Union (mIoU) ratio of 90.9%. When compared to the mPA and mIoU of U-Net3+, MDC-U-Net3+ model showed improvements of 1.85 and 2.12 percentage points, respectively. These results illustrated a more effective segmentation performance than that achieved by other classical semantic segmentation models. [Conclusions] The methodology presented herein could provide data support for automated disease detection and precise medication, consequently reducing the losses associated with tea diseases.

[Objective] Under abiotic stress conditions, rice plants become fragile and susceptible to disease infection. Accurate diagnosis and scientific prevention and control strategies for rice diseases are crucial for the prevention and control of rice diseases, even disasters such as blooding and high temperatures. However, under field natural environmental conditions, the identification of rice diseases is a challenging problem. There are various issues such as complex backgrounds, illumination changes, occlusion, which make it extremely difficult to comprehensively obtain disease information, thus significantly increasing the difficulty of disease identification. This study aims to develop an efficient rice disease recognition model by integrating the efficient channel attention (ECA) mechanism with the MobileViT model, enhancing the accuracy of rice disease identification in the field. Additionally, the rice disease knowledge graph was combined to achieve precise diagnosis and generate scientifically grounded control prescriptions for effective disease management. [Methods] A total of 1 304 raw images of rice diseases were collected from different rice disease investigation and long-term monitoring points in Jiangsu province, at different periods of time, using mobile phones and cameras. 167 disease images from the rice leaf disease image samples dataset were used to supplement the dataset. The raw images were accurately classified and preprocessed under the guidance of plant protection experts. A dataset containing 1 471 original images was constructed that includes seven types of rice diseases: bacterial leaf blight, false smut, leaf blast, bakanae disease, heart rot, grain discoloration, and panicle blast. The dataset was partitioned into training, validation, and test sets following a 7:1.5:1.5 ratio. Data augmentation techniques were applied exclusively to the training and validation sets to enhance sample diversity, while the test set remained unaugmented to preserve its independence for unbiased model evaluation. Post-augmentation, the total image count increased to 7 735. A novel rice disease recognition model was established by integrating the efficient channel attention (ECA) module into the MobileViT model. The recognition model architecture was optimized by improving convolutional structures, reconstructing Transformer encoding blocks, replacing activation function using SiLU. To verify the performance of the model, cross validation and ablation experiments were conducted. After establishing a highly accurate recognition model, the recognition model was combined with the rice disease knowledge graph to achieve accurate diagnosis of rice diseases and generate scientific prevention and control strategies. Finally, an intelligent rice disease diagnostic system was developed using the Flask framework and cloud computing technologies. [Results and Discussions] The results of the ablation study revealed that the model, which combined convolutional layer optimization, Transformer block reconstruction, and the integration of the ECA module, got outstanding performance.The overall precision, F₁-Score and recall rate achieved 97.27%, 97.32%, and 97.46%, respectively. In terms of accuracy, the improved model increased to 97.25%, representing an improvement of 2.3% over the original model (94.95%). To further verify the effectiveness of the improved model, various mainstream models such as Swin Transformer, TinyVit, and ConvNeXt were compared with the proposed model.The experimental results showed that the improved model outperformed the suboptimal model (TinyVit) by 0.92, 1.43, 0.95, 1.32 percent points in overall accuracy, F₁-Score and recall rate, respectively. Moreover, the improved model showed significant advantages in terms of floating-point operations, model size, and parameter count, with a parameter count of only 6.02 MB, making it more suitable for deployment on hardware-constrained devices. Analysis of the confusion matrix and heatmap visualizations revealed that the enhanced model achieved recognition accuracy improvements of 0.6, 0.3, 0.3, and 0.6 percentage points for bacterial leaf blight, heart rot, grain discoloration, and panicle blast, respectively. The integrated system, combining this model with the knowledge graph, demonstrated significantly enhanced accuracy in disease identification and diagnosis. Meanwhile, the disease prevention and control strategies were generated to guide rice disease prevention and control. During field deployment, the rice disease diagnosis system achieved an accuracy rate as high as 98%, with an average response time of 181 ms, demonstrating reliable real-time performance and stability. [Conclusions] By integrating ECA module and reconstructing Transformer encoding blocks, the MobileViT model achieved noticeable improvements in precision, recall and F₁ score, while effectively reducing computational costs, leading to more efficient recognition capabilities of rice diseases in complex field environments. The application of the rice disease intelligent diagnosis system revealed that the system could achieve accurate rice disease diagnosis results, and generate disease prevention and control strategies for guide rice disease prevention and control. This method could effectively improve the prevention and control efficiency of rice diseases, providing technical support for improving the quality, efficiency, digitization and intelligence of rice production.