Fall armyworm (Spodoptera frugiperda) is one of the most serious pests in maize fields. It is often required to early and accurately detect its infestation for timely and effective pest prevention using unmanned aerial vehicle (UAV) imagery. However, reliable detection has been confined to the challenges: 1) The small and subtle feeding marks caused by the larvae, leading difficult to identify at high altitudes. 2) Consistent recognition has been limited to significant variations in object scale at different flight heights. 3) The accurate detection has also been limited to the low contrast between damaged leaf tissue and surrounding healthy foliage, especially under the different lighting and environmental conditions in fields. Collectively, advanced computer vision is necessary to robustly identify early signs of infestation at diverse scales under complex backgrounds. In this study, a robust deep learning model was developed to reliably identify the subtle infestation traces in multi-scale UAV images. A detection architecture, termed coordinated-BiFPN-P2-YOLO (CBP-YOLO), was also proposed using YOLOv8. Real-enhanced super-resolution generative adversarial network (Real-ESRGAN) was applied as a preprocessing step to reduce image degradation from low ground sampling distance. High-fidelity textures of leaf damage were reconstructed from original low-resolution inputs. The backbone of YOLOv8 was enhanced with the Coordinated attention (CA) mechanism. Spatial and channel-wise features were captured to improve the localization and discrimination of minute lesions. Furthermore, the neck component was upgraded with a Bi-directional feature pyramid network (BiFPN) for the highly efficient top-down and bottom-up cross-scale feature fusion. Information loss was minimized for consistent representation during hierarchical propagation at different scales. In addition, a detection head was added to specifically strengthen sensitivity to small targets, particularly at a 160×160 spatial resolution with 64-channel output. The improved model was trained and then evaluated on the custom UAV dataset, which was collected from maize fields naturally infested by fall armyworm under diverse lighting conditions and flight heights. Extensive experiments demonstrated that the CBP-YOLO achieved a peak performance on the imagery with a ground sampling distance (GSD) of 0.38 cm per pixel. Real-ESRGAN significantly alleviated texture blurring and edge ambiguity in low-resolution images, leading to better delineation of feeding scars. Ablation studies were conducted to evaluate the effectiveness of the improved model. There was an outstanding performance on the UAV multi-scale blade dataset. Specifically, there was an average precision (AP@0.5) of 76.5%, which increased by 3.4 percentage points, compared with the baseline model. The robustness and practical applicability of the improved model were obtained in the blades of varying scales during aerial inspection. A comparison showed that the CBP-YOLO outperformed state-of-the-art detectors—including YOLOv9 medium, YOLOv10 medium, YOLOv11 medium, Faster region-based convolutional neural network, and RetinaNet—by margins of 10.1, 7.2, 5.1, 9.3, and 17.9 percentage points in AP@0.5, respectively. Notably, the high precision was also maintained under varying illumination and partial occlusion, indicating strong generalization in agricultural environments. The improved CBP-YOLO framework effectively detected subtle, multi-scale fall armyworm infestation signals during UAV monitoring. Superior accuracy and robustness of the improved model were achieved to synergistically combine super-resolution enhancement, attention-aware feature extraction, fine-grained detection heads, and bidirectional multi-scale fusion. These findings can also provide a practical and scalable solution for early pest outbreak detection, thereby enabling timely intervention to reduce the crop losses in large-scale maize production.

Water scarcity has long constrained agricultural sustainability in the Huang-Huai-Hai Plain, a vital grain production base in China. Regional water resources can also be regulated to improve water use efficiency in sustainable agriculture. It is often required to precisely assess agricultural water use efficiency. Crop production water footprint can be expected to measure the sustainability and efficiency of water resource utilization during the entire crop growth cycle. This study selected winter wheat as the research subject. Assimilated variables were utilized as remotely sensed leaf area index (LAI) and soil moisture (SM). A quantitative assessment was also developed for winter wheat water footprint, according to dual-variable assimilation of crop models and remote sensing data. Spatial dependency and clustering of winter wheat water footprint were then determined using spatial autocorrelation analysis. Furthermore, winter wheat yield–total water footprint quadrant classification, blue and green water resource dependency, and groundwater extraction proportion were integrated to clarify regional water source dependence and formulate differentiated water footprint management strategies. The results indicated that: 1) Data assimilation significantly improved the accuracy of the WOFOST model to simulate the winter wheat yield. There was strong consistency between the simulation and the statistical yield after data assimilation, with an R² increased to 0.98 and an RMSE reduced to 67.68 kg/hm². The accuracy significantly also improved after simulation, compared with an R² of 0.42 and an RMSE of 566.78 kg/hm²; 2) The average green, blue, and total water footprint of winter wheat were 0.35, 0.30, and 0.65 m³/kg, respectively, after data assimilation. The green and the total water footprint exhibited a spatial distribution pattern higher in the south and lower in the north, while the blue water footprint showed a pattern higher in the north and lower in the south; 3) Spatial autocorrelation of winter wheat green and blue water footprint was stronger than that of the total water footprint. The blue, green, and total water footprint of winter wheat exhibited significant spatial clustering, primarily characterized by high-high and low-low clustering; 4) The northern region should prioritize stable production, water saving regulation, and reduction of groundwater extraction, whereas the southern region should focus on improving precipitation use efficiency. This finding can provide scientific support and decision-making basis for the refined and differentiated water resource strategies in typical water-scarce agricultural regions, such as the Huang-Huai-Hai Plain. A solid theoretical foundation and technical framework can help allocate agricultural water resources at the regional scale.

The meat duck industry in China has contributed over 82% of the total slaughter volume worldwide. Therefore, an optimal air temperature is often required for the comfort and survival of meat ducks during breeding. However, the air temperature is susceptible to multiple factors, such as relative humidity and illumination intensity. It is a high demand to timely and accurately predict air temperature for the high-density healthy breeding. However, conventional temperature prediction has been limited to low accuracy, robustness, and generalization. In this study, a hybrid 1DCNN-BiLSTM-DQN model was proposed to integrate with a one-dimensional convolutional neural network (1DCNN), a bidirectional long short-term memory network (BiLSTM), and a deep Q-network (DQN). Duck-house temperature was accurately predicted after model construction. The temperature time-series signal was also decomposed into high- and low-frequency components via the discrete Fourier transform (DFT). Given that the high-frequency component represented short-term fluctuations, the 1DCNN was used to extract local features from the high-frequency component; whereas the low-frequency component represented long-term fluctuations, the BiLSTM was used to extract long-sequence dependency features from the low-frequency component. Subsequently, the two sets of features were fused using a concatenation model. And finally, the temperature prediction value was obtained after the mapping of a fully connected layer. Furthermore, the DQN algorithm was introduced to construct an agent for iterative optimization of the hyperparameters. An adaptive mechanism was optimized with an 8-dimensional state space and a 12-dimensional action space, enabling dynamic optimization of key hyperparameters, such as the learning rate, number of hidden layers, dropout rate, and network architecture. Thereby, the prediction robustness of the model was improved under scenarios of seasonal transitions and extreme weather. The indoor and outdoor temperature data of net-raised duck houses were collected in Gaoyou City, Yangzhou City, Jiangsu Province, from March 22, 2025, to March 22, 2026. The results demonstrated that the 1DCNN-BiLSTM-DQN model achieved a coefficient of determination (R²) of 0.993 with an optimal input step size of 48. The MAE and RMSE were superior to the conventional models, such as the Temporal Convolutional Network and Transformer. Specifically, the 1DCNN-BiLSTM-DQN model exhibited the following improvements under different weather conditions: On cloudy days, the MAE and RMSE decreased from 0.29 °C to 0.18 °C, and from 0.36 °C to 0.23 °C, whereas the R² increased from 0.77 to 0.91; On sunny days, the MAE and RMSE decreased from 0.48 °C to 0.23 °C, and from 0.59 °C to 0.31 °C, whereas the R² increased from 0.79 to 0.94; On rainy days, the MAE and RMSE decreased from 0.54 °C to 0.33 °C, and from 0.70 °C to 0.46 °C, whereas the R² increased from 0.72 to 0.89. The combined 1DCNN-BiLSTM prediction model achieved better prediction performance compared with the conventional models, such as BiLSTM, 1DCNN, TCN, and Transformer. In summary, the 1DCNN-BiLSTM-DQN model can be expected to predict the temperature in duck houses. The findings can also provide data support for early environmental regulation, thereby reducing the risk of environmental stress in meat ducks.

Strong agricultural towns have promoted the circulation of resources among towns in modern rural China. Local resources can be integrated to develop comparatively advantaged leading industries in sustainable agriculture. This study aims to clarify the spatial distribution and influencing factors of strong agricultural towns with various categories at the national level from 2018 to 2024. Nine categories were classified according to the leading industries. A combination of spatial analysis, including the average nearest neighbor index, kernel density estimation, and geographical detector, was adopted to explore the spatial pattern and driving factors. The results show that: (1) A total of 1 709 strong agricultural towns were approved in China, which were distributed in the third topographic step along water sources. The overall uneven spatial distribution exhibited a “northeast–southwest” pattern. Kernel density analysis revealed that the towns specializing in different product categories exhibited the distribution patterns of "category-region matching and core-led radiation". Among them, the largest number of grain and oil industry-strong towns reached 389, while edible fungus industry-strong towns were the smallest, with only 60. Overall, three major high-density clusters were formed in the border areas of Hebei-Shandong-Henan, Jiangsu-Zhejiang, and Sichuan-Chongqing. (2) At the provincial level, the conventional major agricultural provinces—Shandong, Sichuan, and Henan—shared a large number of such towns, indicating the sound quantity and spatial layout. According to the average nearest neighbor index and distribution density, 17 provinces shared the high dense distribution. Specifically, the dense agglomeration was found in the six regions (Shandong, Henan, Guangdong, Jiangsu, Hubei, and Chongqing); Beijing, Tianjin, and Shanghai were the dense uniformity; Three autonomous regions (Xinjiang, Inner Mongolia, and Tibet) presented the scattered agglomeration. (3) The geographical detector showed that the agricultural production scale and regional economic development level served as the significant single factors to explain the distribution of strong industry towns. Among them, the total output value of agriculture, forestry, animal husbandry, and fishery also presented the strongest explanatory effect, particularly for the agricultural development level and optimal industrial structure. In contrast, regional and policy factors shared the weak independent explanatory effects, with the stronger explanatory power after interactions with the other factors. In conclusion, the strong agricultural towns can be expected to position product categories, according to local resources, differentiated development, and comparative advantages. Planning and layout can promote the clustered development of factor efficiency in strong agricultural towns, leading to their differentiated, intensive, and high-quality development. The findings can also provide data support to construct strong industry towns.

Ecological ditch–pond systems are important measures for controlling agricultural non-point source pollution, yet their practical application is constrained by unstable purification performance and large land occupancy. The effective application of such systems in irrigation districts depends not only on the design and operation management of individual units, but also significantly on their spatial layout (including system area and unit connection pattern). Existing studies often fail to adequately capture the multi-level dynamic responses of water volume and water quality in such systems, which hinders their support for spatial layout optimization. Against this backdrop, this study proposes a system dynamics-based simulation method for optimizing the spatial layout of ditch-pond systems in irrigation districts. A field-ditch-pond system model was developed using the system dynamics simulation tool Vensim, integrating water balance, pollutant removal processes, and hydraulic connections among ditches and ponds. The water depth in the paddy model was determined by inflows, outflows, and water consumption during each time step, while the total nitrogen and total phosphorus concentrations were simulated by considering fertilization, first-order pollutant decay, and inputs from rainfall and irrigation. For the ditch–pond unit model, water volume changes were governed by rainfall, evapotranspiration, seepage, upstream inflow, and drainage discharge. Pollutant concentrations in the ditch–pond unit model were calculated using two modes: static storage-based reduction and dynamic drainage-based reduction. The paddy and ditch–pond unit models were linked through system dynamics into an integrated field-ditch-pond system model, which was calibrated and validated using field monitoring data. A case study was conducted in a typical double-cropping paddy high-standard farmland demonstration area in southern China. The model verification results show that the developed model can effectively simulate the dynamic variations of water volume and pollutant concentrations in the system. The case analysis results show that: 1) With increasing ditch-pond to paddy area ratio, nitrogen and phosphorus removal rates rise, but the rate of increase gradually slows down, suggesting an optimal range of 5%-9%; 2) During the late rice season or under larger area ratios, concentrating wetlands in a single drainage path results in a significantly lower removal rate compared to other layouts; 3) For practical implementation, it is recommended to first determine an appropriate area ratio based on target pollutant reduction goals. Subsequently, wetlands may be placed either at the main drainage outlet or distributed in parallel across different drainage pathways, depending on site-specific conditions. The findings provide a methodological reference for modeling multi-level wetland systems and offer a scientific basis for ecological control of agricultural non-point source pollution. Future work may further refine the simulation of water cycling and pollutant transformation processes within field-ditch-pond systems to enhance model accuracy and applicability.

Robotic arms are often required for high picking efficiency, path planning, and path smoothness for safflower harvesting in unstructured environments. This study aims to introduce a picking area clustering and a Target redirecting rapidly-exploring random tree (TR-RRT*) path planning. Firstly, a roller end-effector was designed with an effective picking area of 5 cm × 20 cm, according to the spatial distribution of safflower seed balls under natural conditions. A picking point clustering was also developed to divide the working range of the robotic arm into multiple sub-areas. Each cluster was designed to cover 1-3 picking points. The end-effector was used to harvest 1-3 safflowers in a single operation. A goal redirecting, a goal-direct and deflection expansion, and an artificial potential field (APF) tangential escape force strategy were integrated into the bidirectional RRT* framework to improve the path search efficiency and obstacle avoidance. Among them, the goal redirecting strategy continuously updated the target points during expansion to rapidly connect paths within opportunity windows. The goal-directed and deflection expansion allowed both search trees to extend from the nodes closest to the goal, thus maximizing progress toward the target while deflecting to avoid obstacles. The tangential escape force strategy introduced a tangential component into the conventional artificial potential field, enabling smooth sliding along obstacle boundaries when approaching them. As such, the module effectively avoided the path oscillation and target unreachability in the conventional APF. In path optimization, a combination of a greedy jump-point strategy, interpolated curvature optimization, and B-spline curve fitting was applied to smoothly adjust the curvature of path nodes and then generate high-order continuous trajectories. An obstacle avoidance reconstruction was also introduced to prevent the trajectory penetration through obstacles for the continuous, collision-free, and smooth motion of the robotic arm. A comparison was made on the TR-RRT* and seven algorithms in the dense, small-obstacle environments. The results demonstrated that the superior performance was achieved in the complex obstacle environments, where the path lengths (3892.05mm) were shortened by 9.16% and 8.22%, compared with the IBI-P-RRT*(4284.44 mm) and BI-RRT*(4240.45 mm), respectively; While the average planning time (0.30 s) was only 7.94% of that of RRT*(3.78 s) and 69.77% of BI-RRT*(0.43 s), respectively. Additionally, the steering angles of BI-RRT*(37.51°) and BI-APF-RRT*(39.61°) were 1.22 and 1.29 times larger than those of the improved algorithm (30.63°), indicating significant improvement of the path smoothness. Statistical quantitative experiments were conducted for the algorithm in different obstacle environments. The optimal performance was achieved in both path length and time consumption under various environments. Moreover, the TR-RRT* algorithm successfully planned paths in narrow passage obstacle environments. The tendency of conventional APF was to avoid the local oscillations during path planning in such scenarios. Physical harvesting tests further validated the effectiveness of the clustering and TR-RRT* algorithm. The robotic arm took an average of 3.64 s to move from the initial position to the first target point, with an average transfer time of 3.12 s between tasks. The average positional error relative to the robotic arm's workspace was less than 0.9%, indicating the stable and efficient performance of safflower harvesting.

High-load disturbances during rotary tillage can cause significant wheel slip on the electric-drive mobile platform in the distributed horticulture facility. It is often required to control the speed-slip rate for the longitudinal stability of the distributed horticultural facility. In this study, a cascaded controller of vehicle speed–slip rate was proposed using integral robust vehicle speed and sliding-mode slip rate control. Its effectiveness was validated using simulations and vehicle experiments. Firstly, a dynamic model was established for the coupled system between the distributed electric-drive horticultural platform and the rotary tiller. Tire-soil interaction and the resistance of rotary tillage were also considered to explicitly incorporate the wheel rotational dynamics and external disturbance torques. Soil adhesion also led to variable tillage resistance. Moreover, a coupled modeling framework was constructed to describe the nonlinear relationship between longitudinal tire force and slip ratio. The traction generation was accurately characterized under deformable soil conditions. The slip regulation and speed stabilization were coordinated under high-load environments. Subsequently, an outer-loop vehicle speed controller was designed to incorporate integral robust control. The steady-state errors were eliminated from the operational disturbances for the high-speed stability. The integral term was used to compensate for the persistent disturbance-induced bias. While the robust component was enhanced, the controller’s tolerance to parametric uncertainties and unmodeled dynamics. Integral action was combined with robustness enhancement. The outer-loop controller maintained accurate speed tracking, even when sudden load fluctuations occurred. Furthermore, an inner-loop slip rate controller was developed using sliding-mode control. The optimal slip rate was obtained from the inverse tire model to serve as the reference input for the rapid convergence and precise tracking of slip rate. Sliding-mode control was selected for its high robustness against disturbances and modeling uncertainties, thereby enabling the dynamic response and strong anti-interference. The optimal slip rate corresponded to the traction peak region of the tire–soil interaction curve. Traction efficiency was maximized to prevent excessive slip. The outer and inner loops were coordinated for the longitudinal stability of the platform under high disturbance. A control strategy was then integrated for anti-slip driving and speed regulation. Specifically, the inner loop was used to rapidly suppress the deviations of the slip ratio, while the outer loop was for the global speed regulation using a cascaded structure. A hierarchical architecture of traction control was constructed, suitable for the distributed electric-drive systems. Simulation results indicate that the cascaded controller achieved an average speed error of 0.10 km/h under sudden muddy conditions, which was reduced by 16.6% and 67.7%, compared with the switching and speed control, respectively. The speed recovery time was 0.11 s, which was reduced by 64.5% and 68.6%, respectively. There was an average speed error of 0.07 km/h under variable tillage depths, which was reduced by 40.0% and 52.0%, compared with switching control and speed control, respectively. Experimental results indicate that an average speed error of 0.44 km/h was found under acceleration, which was reduced by 4.3% and 8.3%, compared with the switching and speed control, respectively. The average slip ratio was 0.15, which was reduced by 11.7% and 16.6%, respectively. There was an average speed error of 0.20 km/h under deep tillage, which was reduced by 20.0% and 37.5%, respectively. The average slip ratio was 0.13, which was reduced by 23.5% and 31.6%, respectively. Therefore, the cascaded controller can be expected to effectively suppress the slip ratio during rotary tillage, thereby enhancing the speed control performance and operational stability.

Asparagus harvesting can be confined to the efficacy of robotic vision in recent years. Asparagus spears are characterized by a slender morphology in their natural growth state. These tender stems are highly prone to mutual occlusion and overlapping when growing densely in field conditions. Furthermore, the stout mother stems can simultaneously present as the complex background interference. Collectively, it is often required for the high accuracy of the multi-target segmentation and recognition using machine vision. In this study, the lightweight instance segmentation model (YOLO11n-seg) was adopted as a baseline, in order to improve the precise positioning and harvesting performance of the robotic end-effector. Consequently, an optimized model named YOLO11n-SAL was also proposed to specifically tailor the slender, occluded targets with high fidelity. Two modules were introduced to enhance the feature extraction and attention mechanisms in the architectural framework. Firstly, the multi-scale edge enhancement Module (MEEM) was conceptually designed and integrated in order to mitigate the challenge wherein the edge features of the slender asparagus targets were inherently weak and easily lost during convolutional operations. Multi-scale decomposition was performed on the convolutional feature maps. The MEEM effectively extracted and intensified the edge and contour information before feature fusion. The sensitivity to the target boundaries was significantly elevated for the high segmentation precision, thereby enhancing the perceptual capability of the targets with the slender morphological structures. Secondly, the separated and enhancement attention module (SEAM) was introduced to rectify the feature confusion and data incompleteness caused by inter-target occlusion. Attention separation over both channel and spatial dimensions was also utilized to adaptively perceive the local and global features of the occluded asparagus at the varying scales. These features were selectively enhanced and effectively fused to better position the visible subjects of the partially masked targets, while suppressing the background noise and distractor information. The robust performance of the detection and recognition was maintained even within the complex and cluttered environments. A series of experiments was conducted to verify the effectiveness of the improved model. Quantitative evaluation results indicate that the improved YOLO11n-SAL model achieved significant gains over all key performance indicators, compared with the baseline model. In the detection task of the target bounding box, the superior performance was achieved with a detection precision of 94.2%, a recall rate of 83.1%, a mean average precision at IoU threshold 0.5 (mAP_0.5) of 91.2%, and a mean average precision at IoU threshold 0.5-0.95(mAP_0.5-0.95) of 76.2%. In the more granular instance mask segmentation, the model also performed impressively. The segmentation precision, recall, mAP_0.5 and mAP_0.5-0.95 reached 93.4%, 77.9%, 90.7%, and 62.7%, respectively. Furthermore, the heatmap analysis demonstrated that the YOLO11n-SAL model was markedly improved to perceive the asparagus edge features over different scenarios, with the superior multi-target segmentation and recognition under occluded conditions. The high accuracy of the segmentation and recognition was achieved to reduce the interference in the complex multi-scenario environments, compared with the baseline. Finally, a series of asparagus recognition, positioning, harvesting, and grasping trials were carried out using depth cameras and mechanical arms, in order to validate the cognition and position performance in the actual deployment scenarios. The empirical results showed that a positioning success rate of not less than 90% was accompanied by effective harvesting and grasping performance. These findings can provide reliable technical support for the advancement of robotic harvesting in precision agriculture.

Fruit-tree canopies are typically characterized by complex branching architecture and dense foliage, leading to severe self-occlusion, uneven light distribution, and low light-use efficiency. However, experience-driven decisions cannot fully meet the large-scale pruning of the standard canopy shapes and optimal regulation of tree vigor in the orchard. In this study, an intelligent pruning model was proposed to optimize the canopy structure reconstruction and light-efficiency evaluation using Neural Radiance Fields (NeRF). A reproducible, quantitative, and visualization-friendly workflow was also provided for canopy analysis and pruning under a real orchard. Qingcuili plum (Prunus salicina cv. ‘Qingcuili’) was selected as the target species. Multi-view videos were captured around each tree from multiple angles for sufficient coverage of the canopy under field lighting and background. A NeRF reconstruction was used to learn volumetric radiance and density fields from the video frames. A high-fidelity 3D representation of the tree was generated from the reconstructed structure. Branch topology and geometric descriptors (e.g., branch order, orientation, length, and spatial distribution) were extracted for decision-making. The Monte Carlo Ray Tracing (MCRT) module was integrated to simulate ray–canopy interactions. Both direct and diffuse radiation pathways were estimated to quantify light conditions in the 3D canopy space. Two indicators were computed: light interception ratio (LIR) to characterize the proportion of incident light intercepted by the canopy, and energy interception ratio (EIR) to reflect the effective energy under the simulated radiation field. A pruning recommendation model was constructed to fuse: (i) branch-classification and geometric features, and (ii) light-efficiency weights derived from the MCRT outputs. Candidate-branch suggestions were given for the improved canopy illumination with structural feasibility. In addition, a virtual interactive pruning system was developed to support human-in-the-loop validation, intuitive visualization of light distribution, and pruning effects before field implementation. The framework was validated on a dataset of 102 Qingcuili plum trees with diverse canopies and growth. The NeRF-based reconstruction was achieved with high accuracy, with an average reconstruction error of 4.3%, indicating reliable recovery of canopy structure under complex occlusion. The pruning recommendation performance reached 93.2% accuracy, compared with expert labelling. Pruning recommendations were consistent with practical knowledge. The optimal pruning strategy was applied to substantially improve the canopy light conditions. LIR and EIR increased by approximately 15.2% and 18.9%, respectively, indicating enhanced light interception and more effective energy capture. From a deployment perspective, the end-to-end system response time remained stable in 2.3 min using cloud GPU acceleration (NVIDIA V100), indicating favorable real-time applicability for decision support and interactive analysis. A canopy structure–light-efficiency optimization was integrated with NeRF 3D reconstruction, MCRT light simulation, and feature–weight fusion for intelligent pruning. The 3D canopy reconstruction and quantitative pruning improved the light-use efficiency and the precision of tree vigor regulation in a real orchard. The finding can provide a feasible technical pathway toward digital twins and smart pruning for fruit-tree production.

Microplastics can enter agro-food systems via multiple pathways, including agricultural production, environmental transport, post-harvest handling, processing, packaging, and distribution. Major sources can be attributed to the residue, weathering, fragmentation, and secondary breakdown of agricultural plastics, such as mulching films, greenhouse covers, irrigation tapes, pipes, nets, and food-contact materials. Particles can be redistributed through irrigation water, surface runoff, soil dust resuspension, atmospheric deposition, and the agricultural application of compost or sewage sludge. Additional contamination can also cause from abrasion, shedding, and migration of processing equipment, filtration media, packaging materials, and storage interfaces. Together, these pathways can pose a great challenge to the multi-source, multi-stage, and cross-media exposure pattern, leading to difficult-to-source identification, risk interpretation, and food safety. This review aims to focus on microplastic monitoring, assessment, and process control in the agro-food chain. Major source categories and contamination pathways were summarized in crop production, animal production, and processing environments, and then examined the reported occurrence in plant-derived foods, animal-derived foods, and processed products. Available studies showed that microplastics were widely detected in vegetables, fruits, cereals, shellfish, fish, meat products, bottled water, salt, sugar, honey, beer, and liquid milk. But reported concentrations varied substantially over studies, leading to orders of magnitude. Such heterogeneity was strongly influenced by commodity differences and regional context, method factors, including particle-size thresholds, reporting metrics with particle number or mass, pretreatment intensity, recovery correction, blank control, confirmation criteria, and sampling contexts, such as washing, peeling, retail cutting, cooking, and packaging. Direct comparison was limited for the quantitative interpretation, especially when trophic transfer coexisted with process-related contamination. Therefore, current occurrence data were more useful to identify high-concern commodities, high-concern links, and major uncertainty sources, compared with the cross-study ranking under heterogeneous analytical conditions. Analytical challenges were further highlighted under complex agro-food matrices rich in lipids, proteins, polysaccharides, pigments, and inorganic particulates, where microplastics often occurred at trace levels over broad size ranges. Practical bottlenecks were closely related to matrix removal, polymer preservation, particle loss control, and procedural contamination prevention. Pretreatment strategies, including chemical digestion, enzymatic digestion, density separation, and membrane filtration, were compared from the perspective of matrix applicability, polymer compatibility, recovery performance, and quality-control requirements. Chemical digestion provided effective organic matter removal, but caused damage to polymers under strong acidic or oxidative conditions. Enzymatic digestion offered milder treatment and better polymer preservation but remained constrained by cost, duration, and reagent background. Density separation was used for enrichment efficiency, but it was required for the selection of separation media and recovery validation, especially for small particles and high-density polymers. Membrane filtration functioned as a concentration step as a critical interface affecting optical imaging, verification, and background interference. Verification workflows were reviewed over microscopy and fluorescence imaging, vibrational spectroscopy, thermal analysis, and emerging high-throughput platforms. Among them, microscopy and fluorescence staining supported rapid morphological screening, but visual identification alone remained vulnerable to false positives and operator subjectivity. Micro-FTIR and micro-Raman spectroscopy provided for chemical identification central to polymer at the particle level, yet they were constrained by throughput, diffraction limits, fluorescence interference, and spectral-library dependence. Thermal evaluations, such as Py-GC/MS and TED-GC/MS, were mass-based quantification of visually undetectable particles, but their performance in food matrices remained sensitive to marker interference, calibration strategy, and background decomposition products. Emerging techniques, including laser direct infrared imaging, optical photothermal infrared spectroscopy, atomic force microscopy infrared spectroscopy, hyperspectral imaging, surface-enhanced Raman scattering, and portable sensing systems, were expected to improve throughput, particle-size coverage, and field-oriented monitoring, although their broader application still depended on scenario adaptation and quality. Artificial intelligence was reviewed as an auxiliary tool for high-throughput screening, spectral interpretation, and multimodal data integration. Image-based models were used to improve particle localization, counting, and morphological characterization in fluorescence and microscopic datasets. While machine learning and deep-learning were supported spectral denoising, feature extraction, data matching, and multimodal fusion over imaging, FTIR, and Raman data. Their practical value depended on transparent training datasets, reproducible preprocessing pipelines, external validation over matrices and devices, drift monitoring, reject-option strategies, and alignment with conventional analytical indicators, such as recovery, blank correction, repeatability, detection limits, and particle-size-specific performance. As such, artificial intelligence was better supported by standardized monitoring and data interpretation rather than a substitute for chemical verification. Furthermore, a flux accounting perspective was introduced to link endpoint measurements with process control. Input and output fluxes of the individual unit were defined to quantify the removal efficiency and net introduction. This framework was used to identify critical control points and then evaluate mitigation options. Source reduction, process interception, and terminal were selected as complementary controls. Priority measures included the life cycle of agricultural plastics, fragmentation-prone inputs, water purification, control of sludge and compost application, high-friction processing interfaces, and standardized sampling, pretreatment, reporting, and quality-control requirements. Safety thresholds were required for microplastics in agro-food products, covering precautionary and prioritizing comparable data generation, critical control points, and controllable exposure reduction over the full chain. Overall, the microplastics in agro-food systems can be expected to move from isolated endpoints toward integrated monitoring, process evaluation, and process-oriented detection.