To improve the seed-filling performance of pneumatic precision seed metering devices for small-seeded crops, a U-shaped seed agitation plate and a combined-type agitation plate were developed. The agitation performance of cylindrical, U-shaped, and combined-type agitation plates was evaluated using the average and maximum velocities of the seed population as quantitative indicators. A three-factor, three-level simulation experiment based on the Discrete Element Method (DEM) was conducted, with agitation plate type, rotational speed, and knob height as the main factors. The optimal parameter combinations were determined as follows: for millet, a combined-type agitation plate with a rotational speed of 24.85 r/min and a knob height of 8.36 mm; for broomcorn millet, 24.17 r/min and 7.96 mm; and for rapeseed, 22.93 r/min and 8.57 mm. Bench experiments were carried out to validate the optimized combined-type agitation plate. The seed-filling rates reached 99.3%, 100%, and 99.7% for millet, broomcorn millet, and rapeseed, respectively. The results indicate that the combined-type agitation plate significantly improves seed mobility and filling performance, providing an effective solution for precision seeding of small-seeded crops.

To enhance the seeding accuracy of multi-seed-per-hole operations for small-seed crops, a combined experimental-CFD optimization method for an air-suction seed metering plate was developed. Single-factor tests established that the suitable suction-hole diameter was 0.4-0.6 times the equivalent seed diameter, and threshold-based negative-pressure tests were used to define the acceptable suction-hole negative-pressure range for millet, broomcorn millet, and rapeseed. CFD pressure-tracing analysis was then performed to quantify the mapping between suction-hole pressure and air-chamber inlet negative pressure. On this basis, a three-factor, three-level optimization test was conducted using inlet negative pressure, suction-hole diameter, and suction-hole thickness as variables, with the qualification rate of suction-hole pressure as the response. The optimized parameter combinations were 2.51 kPa, 0.88 mm, and 0.31 mm for millet; 2.97 kPa, 1.12 mm, and 0.35 mm for broomcorn millet; and 2.35 kPa, 0.93 mm, and 0.32 mm for rapeseed. Bench validation showed seed-filling rates of 93%, 97%, and 96%, respectively. The results demonstrate that the proposed method can effectively reduce empirical trial workload and provide a practical basis for the design of pneumatic precision seed metering plates for small-seed crops.

To address the challenges of labor-intensive and inefficient manual harvesting of Lycium barbarum L. grown on a double-layer trellis, this study proposes a human–robot collaborative system. The system consists of a hand-held high-frequency rocker-oscillator harvester and an electric receiving cart. Mechanical property measurements of fruits and branches revealed detachment forces of 1.2 to 1.8 N for mature fruits and 2.5 to 3.0 N for immature fruits, with an average bending elastic modulus of 548.9 MPa for fruit-bearing branches, providing a basis for selective harvesting. A Box–Behnken design was employed to optimize vibration frequency, harvesting duration, and inter-rod spacing, and response surface analysis identified the optimal parameters as 18 Hz, 4.5 s, and 26 mm. Next, a central composite design optimized the receiving cloth tilt angle and radius to maximize collection efficiency, defined as the ratio of fruits collected by the cart to the total number of detached fruits. Field validation demonstrated a mature fruit harvesting rate of 96.2%, a mis-harvesting rate of 4.3%, a damage rate of 3.7%, and a collection efficiency of 96.1%, with relative errors below 0.5% compared to model predictions. These results confirm the system’s effectiveness and provide technical support for standardized, low-damage mechanized fruit harvesting of Lycium barbarum L..

The power take-off (PTO) shaft of a tractor is subjected to complex and variable random loads during field operations, and the accuracy of the resulting load spectrum directly affects the reliability of fatigue life prediction. To address the limitations of traditional parametric extrapolation methods, particularly their inadequate fitting performance, a non-parametric extrapolation method based on DBSCAN clustering is proposed. Field test validation demonstrates that the proposed method effectively captures the multi-modal distribution characteristics of the load. The extrapolated results show good agreement with the measured data in terms of cycle count distribution and pseudo-damage indicators. Compared with the fixed-bandwidth method, the proposed approach increases the coefficient of determination by 3.401% and reduces the mean squared error (MSE) by 39.154%, while yielding pseudo-damage values closer to 1. These findings provide a novel technical approach for the construction of load spectra under complex operating conditions of agricultural machinery.

In addressing the high labor costs and low operational efficiency of greenhouse tomato harvesting and separate packaging workflows, this study develops an integrated tomato harvesting robotic system embedded with RGB-D machine vision, 5-degree-of-freedom manipulator and vertical heat-sealing net bag packaging mechanism. The YOLOv8s lightweight detection model trained on self-built multi-light greenhouse tomato dataset (1260 annotated images covering unobstructed, semi-occluded and heavily occluded fruits) is adopted to identify ripe tomatoes with a recognition accuracy of 95.2%, and ImageJ software is introduced to conduct secondary maturity screening via RGB chromatographic analysis. A* global path planning combined with TEB local trajectory optimization realizes autonomous obstacle avoidance navigation of the wheeled mobile platform, while RRT-Connect bidirectional random tree algorithm is applied for obstacle-free grasping trajectory planning inside dense tomato canopies. A total of 120 valid cyclic tests are carried out in simulated greenhouse environment to verify the full-chain automation including fruit detection, in-situ picking and instant bagging. Experimental results show that the average single-fruit processing cycle is 12.1 s, with a picking success rate of 90.8% and bagging success rate of 98.3%. Compared with skilled manual picking and packaging, the overall working efficiency is improved by approximately 30%. This system firstly realizes continuous integrated harvesting and commercial packaging operation for greenhouse tomatoes, providing a feasible technical solution for full-process intelligent protected agriculture.

This paper presents a theoretical framework for estimating the gripping force of a three-degree-of-freedom robotic arm mounted on a remotely operated underwater vehicle, intended for sampling operations in intensive aquaculture systems. Using the static relationship based on the Jacobian transpose, the proposed method reconstructs the force and moment vectors acting at the end-effector from the estimated joint torques. The gripping force is determined by projecting the Cartesian force onto the normal directions of a two-finger gripper. Transmission efficiency and joint friction are incorporated into the model through the definition of an effective torque. The influence of hydrodynamic forces is treated as an external disturbance during quasi-static manipulation. The internal consistency of the model is evaluated through a numerical validation based on representative design parameters for compact underwater manipulators. The analysis indicates that a gripping force of approximately 150–200 N satisfies the friction-based stability condition for typical sampling loads of approximately 100 N. Furthermore, the resulting joint torques remain within the realistic operating limits of actuators commonly used in ROV-mounted manipulators. The findings confirm the dimensional consistency and physical plausibility of the proposed estimation framework, which may be applied to assisted underwater force estimation in aquaculture applications.

To promote the mechanization of the tobacco industry, a self-propelled tobacco stem harvester was designed in this study. To improve harvesting efficiency under varying terrain and planting conditions, the machine integrates key functional components, including a chain-clamp conveyor and a loosening shovel with optimized structural parameters. The performance of these core components was analysed using 3D modelling and finite element simulations conducted in ANSYS. Field experiment optimization indicated that the optimal operating parameters were a clamping chain speed of 0.7 m/s, a forward travel speed of 0.4 m/s, and a clamping chain gap of 12 mm. Experimental results showed that the harvester achieved a leakage rate of 3% and a breakage rate of 7%. Overall, the machine demonstrated reliable performance and satisfied practical operational requirements. This study provides an effective technical solution for the mechanization of tobacco stem harvesting, contributing to reduced production costs and the advancement of agricultural modernization.

To address the issue of uneven fracture during the processing of Astragalus slices, this study first establishes a discrete element Bonding model for Astragalus roots, filling the gap in the calibration of its bonding parameters. Through uniaxial compression tests and multiple optimization experiments, the optimal parameters were determined, with simulation errors below 1.6% and validation errors for the fracture rate = 12.7%, providing theoretical support for the development of processing equipment for Astragalus slices.

To address the problems of low bale-forming rate and loose compaction during the mechanized recovery of residual plastic film in farmland, a three-stage residual film baling device was developed. Based on the discrete element method (DEM), a flexible thin-shell model of the residual film was established, and dynamic simulation and parameter optimization of the baling process were carried out using Rocky DEM software. Combined with single-factor experiments and response surface methodology, the effects of baling chamber inclination angle, belt type, belt linear speed, and upper belt inclination angle on bale integrity and compaction were analyzed. The results showed that the bale-forming rate of the three-stage device reached 99.13%, and the bale density reached 83.75 kg/m³ under the optimal conditions: baling chamber inclination angle of 30°, upper belt inclination angle of 30°, belt linear speed of 2.3 m/s, and the use of a rough-surface belt. Field validation tests showed that the average density of the residual film bales was 91.4 kg/m³, with a prediction error of only 2.23 kg/m³ compared with the simulation results. The device operated stably and reliably. This study presents a high-efficiency residual film baling device with improved bale-forming rate and compaction performance through structural optimization and parameter tuning, providing technical support for efficient recovery and pollution control of residual plastic film in farmland.

Agricultural power networks accelerate contact wear and reduce the accuracy of conventional loop resistance testing. This study presents an APP-based wireless three-phase synchronous measurement system integrating UC3854-driven multi-stage power factor correction (0–100 A, 82–83.17% efficiency, PF 0.99) and a supercapacitor pulse module (16 V trigger, 230 V charge). The system achieves 0.01 µO resolution and >10,000 MO insulation resistance. Field validation on 110 kV GIS reduced wiring and testing times by 67% (=1% deviation), while 500 kV GIL measurements improved efficiency by 80%. The platform enables high-current stability, strong interference immunity, and digitalized operation.

Traditional uniform fertilization in rice paddy fields is associated with low fertilizer-use efficiency and severe agricultural non-point source pollution. Existing variable-rate fertilization equipment commonly presents limitations such as reliance on single-source prescription information and insufficient control precision. To address these issues, this study uses an impeller-type fertilizer applicator as the working platform and, considering the characteristics of cold-region rice cultivation in Heilongjiang Province, constructs an integrated ternary nitrogen-phosphorus-potassium fertilization prescription map. A mapping model between fertilizer application rate and motor speed is established, and a fuzzy PID controller optimized using an improved whale optimization algorithm (IWOA) is designed. Fertilizer-discharge calibration tests and field trials were conducted. The results showed that the coefficient of determination (R²) between fertilizer discharge rate and motor speed reached 0.9912. The IWOA-fuzzy PID algorithm achieved a step-response overshoot of only 3.3% and a settling time of 1.55 s, while the relative error of field fertilization was controlled within ±5%.

To address the limitations of computational resources and the stringent real-time stability requirements in post-harvest online grading of fresh tea leaves, this study proposes a lightweight object detection method oriented toward efficient deployment. The YOLOv11n network was selected as the baseline model. A StarNet backbone was introduced to enhance nonlinear inter-channel interactions while reducing the complexity of feature extraction. In addition, a DySample dynamic upsampling module was employed to predict content-adaptive sampling locations, thereby improving multi-scale feature reconstruction under lightweight constraints. Furthermore, a wavelet-based pooling structure was designed to perform structure-aware frequency-domain decomposition, preserving critical edge and texture information while reducing redundant computation. The Inner-MPDIoU loss function was also adopted to calculate overlap consistency within a compact core region, thereby improving bounding-box localization stability for fine-grained structures. Based on these integrated improvements, the lightweight YOLOv11-SDWI detection model was developed. Experimental results demonstrated that the proposed model achieved an accuracy of 89.5%, with only 4.9 GFLOPs and 1.89 M parameters. To further verify its engineering applicability, the algorithm was encapsulated into a complete visual software system specifically designed for fresh tea leaf sorting. The developed system provides a functional interface for real-time detection, effectively bridging theoretical algorithm design and practical agricultural applications, and establishing a solid software foundation for future deployment on physical sorting equipment.

This study aimed to evaluate the performance of a vacuum seed-metering unit (VSMU) developed for sowing vegetable seeds in trays under different vacuum pressures. Laboratory experiments were conducted using three seed types with markedly different physical and geometric properties: pepper, Armenian cucumber, and okra. Metering performance was evaluated in terms of the accuracy, miss, and multiple indices. The results showed that seed physical and geometric properties affected the optimal vacuum pressure, which was 1.52, 7.5, and 8.5 kPa for pepper, Armenian cucumber, and okra seeds, respectively. The corresponding accuracy indices were 93.54%, 91.25%, and 96.04%. Seeds with a relatively large mass, such as okra seeds, or low sphericity, such as Armenian cucumber seeds, required higher vacuum pressures than the flat, approximately circular pepper seeds. The developed VSMU therefore demonstrated its ability to handle seeds with diverse physical and geometric characteristics and achieve accurate seed metering.

Effective cooling is crucial for maintaining suitable thermal environments in greenhouses during hot summers. This study developed a coupled numerical model to investigate the cooling performance of a wet curtain system in a multi-span plastic greenhouse in Weifang, China. Relative deviations were less than 5.88% for wet curtain outlet temperature and below 7% for average greenhouse temperature. Results indicated that the wet curtain reduced the average greenhouse temperature by 2.33 ~ 4.20 ?, with the maximum reduction of 4.20 ? occurring at 12:00. The cooling capacity increased with rising solar radiation in the morning and then diminished in the afternoon. The cooled air entered as a floor-level jet, accumulated near the crop zone, and subsequently rose due to buoyancy, forming two large recirculation vortices that enhanced vertical mixing and improved temperature uniformity. Consequently, the average indoor air velocity increased by 8.38% ~ 22.40% compared to ventilation without the wet curtain. Parametric analysis revealed that the average greenhouse temperature decreased nonlinearly with increasing inlet velocity, and the marginal cooling benefit dropped significantly when the inlet velocity exceeded 0.75 m/s. An optimal inlet velocity range of 0.75 ~ 1.0 m/s is recommended. The model accurately captured the thermal performance and airflow patterns of the greenhouse with wet curtain cooling.

Efficient and flexible agricultural image annotation is crucial for intelligent crop monitoring in smart agriculture, yet conventional detection models are limited by fixed class labels and require extensive manual annotations. This study presents a zero-shot annotation framework that integrates OWLv2, Googles second-generation open-vocabulary vision model, with large language models (e.g., GPT-3.5, DeepSeek V1) to enable multilingual, natural language-driven fruit recognition in smart agriculture. A user-friendly interface was developed to support individual or batch image annotation with adjustable sensitivity to meet diverse field requirements. Experimental evaluations demonstrated the frameworks strong generalizability and semantic understanding capabilities, allowing recognition of unseen fruit categories and attributes such as ripeness or color. The system significantly reduces annotation time and labor costs, while enhancing accessibility through natural language interaction. To ensure a robust evaluation of generalizability, a cross-domain protocol was employed using a novel dataset from 2025. Results showed that OWLv2 achieved an F1-score of 0.80 and an mAP of 0.8301, significantly outperforming the pre-trained YOLO11 (F1: 0.74, mAP: 0.60) in zero-shot scenarios. OWLv2 exhibited superior flexibility and required no task-specific dataset retraining, although its computational demands remain higher than lightweight models like YOLO11. Notably, while the LLM (DeepSeek) introduced a total one-time API latency of 598.3 ms (called only once for processing multiple images). the actual core computational latency of OWLv2 was only 257.7 ms per image. Despite a total processing time of 891.9 ms (including visualization output), the framework demonstrates superior recall (0.9080) and semantic flexibility without retraining. These results verify the enormous application potential of OWLv2 and similar zero-shot models in agriculture, providing scalable solutions for automated annotation, real-time monitoring, and large-scale data collection.

Addressing the issues of severe resource wastage, low automation levels, and insufficient control precision in traditional orchard water and fertiliser management, this paper proposes a multi-dimensional, collaborative integrated water and fertiliser management system for orchards. This system combines IoT sensing technology, wireless communication technology, cloud platform computing, and intelligent control algorithms. Through a layered architecture design, this system innovatively enhances the perception layer, transmission layer, data layer, and application layer. It integrates diverse communication protocols such as ZigBee, LoRa and NB-IoT, combines sensors for irrigation, fertilisation, and other aspects, and merges intelligent algorithms including PID closed-loop control and fuzzy neural networks. This enables precise water-fertiliser ratio formulation, remote real-time monitoring, and adaptive regulation. Field validation at a demonstration orchard in Guangdong demonstrated water savings of 30%–65%, fertiliser reductions of 40%–50%, and crop yield increases of 20%–135%. Research findings indicate that the deep integration of IoT and cloud platforms significantly enhances water and fertiliser utilisation efficiency in orchards while reducing labour costs. This provides technical support for standardised orchard cultivation, aligning with the demands for precision and intelligent development in modern agriculture.

Driven by global agricultural mechanization and green low-carbon transformation, hybrid tractors have become a key focus in agricultural machinery competition due to fuel economy and operational adaptability. Using literature review and comparative analysis, this paper summarizes global and domestic technological development and characteristics of typical models. It analyzes three core power topological structures, compares energy configurations and management strategies, clarifies applicable scenarios and pros and cons of different routes, and points out bottlenecks in power coupling, energy storage and control, as well as industrial dilemmas like high costs and poor supporting facilities.

During mechanized harvesting operations, including excavation, root–soil separation, collection, and transportation, cassava roots are susceptible to multiple mechanical loads such as shear, compression, and impact, which easily induce mechanical damage and seriously deteriorate the processing quality and storage performance of cassava roots. To reveal the mechanical variation characteristics and damage thresholds of cassava roots under primary mechanical damage modes during harvesting and transportation, this study took harvest-stage cassava roots as the research object and conducted systematic mechanical property tests. Three typical mechanical tests, including compression, shear, and bending tests, were performed to explore the mechanical response rules of cassava roots under typical loads, obtain key mechanical parameters and damage thresholds, and clarify the failure characteristics and mechanical mechanisms under different loading modes. The research results clarify the internal relationship between mechanical properties and mechanical damage of cassava roots at the harvesting stage. This study provides reliable basic data and theoretical support for the parameter optimization of key working components, operational parameter matching of specialized cassava harvesters, and the optimal selection of tool edge angles for cassava slicing machinery, and possesses practical engineering significance for reducing mechanical harvesting damage and improving the post-harvest commodity rate and processing performance of cassava roots.

To explore the operational performance of a ridge roller under two different working modes, namely, linear arrangement (LA) and interleaved arrangement (IA) of adjacent ridging shovels, firstly, theoretical analysis was conducted to conclude that the operational resistance of IA is greater than that of LA. Secondly, a discrete element simulation method was employed to establish a discrete element simulation model. The extracted data verified the conclusions of the theoretical analysis. Qualitative analysis was conducted based on the formed ridge profile, indicating that LA has a better ridge forming effect than IA. Finally, field experiments were conducted, with the operating speed of the ridge roller (1.5 km/h, 3 km/h, 4.5 km/h) and different installation distances between adjacent ridging shovels (0, 240 mm, 480 mm) as experimental factors. The average operational resistance, the distance between adjacent ridge tops, and the height difference at the bottom of the ridge furrow were used as indicators for a full-factor experiment. It was concluded that under the same conditions, LA has the best ridge forming effect but requires the greatest resistance, while IA can effectively reduce resistance but has a poorer ridge forming effect. This study provides a theoretical basis and guidance for tillage and soil preparation operations.

To overcome YOLOv11s limitations in complex field environments, this paper proposed SDD-YOLOv11n, a lightweight real-time detector for soybean diseases. The model reconstructed the backbone using GhostConv to minimize redundancy and integrates a C3k2_Star module to enhance small lesion detection against background noise. Additionally, a Detect Efficient (DE) head further compressed the architecture. Experimental results verified the models efficiency, achieving a parameter count of 1.88 M and a weight size of 3.9 MB—reductions of 27.3% and 25% compared to YOLOv11n, respectively. Furthermore, the model maintained high detection performance with a Mean Average Precision (mAP50) of 75.6% and an F1-score of 69.3%, demonstrating its effectiveness in balancing architectural efficiency and accuracy in complex field environments.

To achieve precise prevention and control of rubber roll wear in huskers, this study established a complete experimental platform integrating mechanical transmission, feeding control, and visual acquisition units, and subsequently developed a real-time monitoring system for paddy flow instability based on machine vision. An image acquisition system composed of a CMOS camera and a customized light source was built to construct a dedicated dataset for paddy flow states. Based on the lightweight YOLOv8n detection model, Python programming was adopted in the PyCharm environment with the Ultralytics library integrated, realizing real-time recognition and quantitative analysis of paddy flow states. The results demonstrated that the system realized a real-time detection efficiency of 14 FPS on a local workstation, and the YOLOv8n model achieved a recognition accuracy of 90.8% in terms of mean Average Precision at IoU threshold 0.5 (mAP@0.5) for sparse and overlapping grain states. The system could effectively capture key abnormal states, including inclined paddy grains entering the rolling zone, sparse paddy flow lasting more than 5 seconds, and overlapping paddy flow density exceeding 10 grains/cm². This study transformed the mechanical characteristics of paddy flow instability into pixel-level quantitative indicators and established an integrated visual monitoring paradigm of "perception-analysis-decision", providing effective technical support for the intelligent management and control of huskers.

The efficiency of agricultural operations depends on the length of non-working passes in the headlands. Therefore, the aim is to minimize both turn length and headland width. Various methods are used to construct turn trajectories, allowing them to be compared under specific conditions and enabling the most efficient movement pattern to be selected. This article examines closed fishtail-shaped turns composed of arcs with a given radius and straight-line segments. Analytical relationships were derived to determine the headland width and the length of closed fishtail-shaped turns for different travel directions of a machine-tractor unit equipped with a mounted reversible plough in an irregularly shaped field. It was found that, at a positive angle between the travel direction of the unit and the field boundary, movement of the unit across the field from right to left results in shorter closed fishtail-shaped turns and requires a narrower headland than movement from left to right.

To investigate the effects of moisture content on the compressive mechanical properties of sweet potato roots, this study used three sweet potato varieties (Zishu, Hongyao, and Xiguahong) cultivated in the Tianjin region as experimental materials. Experiments were conducted between October 8, 2025, and January 8, 2026, in a laboratory environment maintained at 20°C ± 2°C and 50% ± 5% relative humidity. The test employed a moisture content measurement method based on storage duration, measuring moisture content every 10 days. Simultaneously, compressive mechanical property tests were performed using an INSTRON 3344 series universal material testing machine under conditions of a 10 mm/min loading speed and a maximum pressure of 1500 N. The maximum rupture force, maximum deformation, and elastic modulus of sweet potatoes at different moisture contents were determined. Data analysis was conducted using Design-Expert 13 software. The results indicated that the variation patterns of moisture content were similar across the different sweet potato varieties. The moisture content decreased rapidly during the initial storage period, showed a brief increase in the middle stage, and then continued to decline at a slower rate in the later stage. Moisture content significantly affected the compressive mechanical properties of sweet potatoes. As the moisture content decreased, the elastic modulus and maximum rupture force increased, while the maximum deformation decreased.

Corn seedling and weed detection during the 2–5-leaf stage is essential for precision weeding, but similar morphology, field background interference, and the accuracy-efficiency trade-off limit the practical use of lightweight detectors. To address these problems, this study proposes Corn-Weed-ENAS, an improved YOLOv8-based detection model with joint structure and loss-function search. A differentiated ENAS search space was constructed for the backbone, neck, detection head, and loss function to automatically identify a compact architecture suitable for corn seedling-weed detection. A field image dataset of 2–5-leaf corn seedlings and weeds were collected in Heilongjiang Province, and comparative and ablation experiments were conducted against mainstream detectors. The proposed model achieved 97.3% mAP@0.5 with 2.5M parameters and 6.7 GFLOPs, showing a favorable balance between detection accuracy and lightweight deployment. To further verify its agricultural engineering applicability, an indoor simulated operation test was conducted on a Jetson Orin Nano Super platform using 20 pots of corn seedlings and weeds and a moving test vehicle at 1.2 m/s. In the virtual spraying signal verification, YOLOv8n produced five missed detections and one false detection, whereas Corn-Weed-ENAS produced three missed detections and no false detection. These results indicate that Corn-Weed-ENAS can provide more reliable weed localization and virtual trigger outputs for selective spraying decision support.

An alternative approach to the utilization of lake sapropel is its mixing with available organic sorbents, such as chopped cereal straw. The application of such mixtures in strips into the soil ensures the formation of a moisture-holding nutrient layer during crop cultivation. To form these strips, an experimental prototype of a single-row machine was developed. Under laboratory conditions, the torque required to drive the machine and the mixture mass flow rate were investigated. The variable parameters included the rotational speed of the metering beater shaft , the blade inclination angle , the width of the hopper outlet opening , and the opening position of the metering beater housing gate . To obtain mathematical models of the machine drive torque and the mixture mass flow rate in the form of regression equations, a statistical design of experiments method for a four-factor experiment based on the Box–Behnken design was applied. Analysis of the obtained regression equations showed that, in order to reduce the energy consumption of the machine drive, it is advisable to maintain the beater rotational speed within the range of n = 550–600 rpm, the blade inclination angle at ? = 45 - 500 , and the maximum opening of the metering beater housing gate ? = 0.06 m. A nomogram was developed for regulating the mixture feeding rate of the machine by changing the width of the hopper gate opening ? . It was also established that, at the maximum mass flow rate of Q 3.5 = kg/s, the torque required for the machine drive ? ranged from 6.9 to 7.2 N·m.

As the pig farming industry evolves towards intelligence and large-scale operations, multi-object tracking technology plays a pivotal role in enhancing farming efficiency and optimizing health management. Addressing the issues of trajectory interruption and jitter caused by frequent occlusion of targets, significant scale variations, rapid motion, and sudden turns in large-scale breeding scenarios, an improved ByteTrack method is presented. This method employs YOLOv8 as the detector and enhanced the ByteTrack framework. By incorporating an Exponential Moving Average (EMA) trajectory smoothing module, it optimizes the trajectory jitter issues encountered by traditional Kalman filtering in fast-moving or sudden turning scenarios, resulting in smoother and more continuous tracking trajectories. Experimental results on a real-world livestock farm video dataset demonstrate that the proposed method achieves improvements across several key evaluation metrics. Specifically, the Multi-Object Tracking Accuracy (MOTA) increased by 1.61%, Identification F1 Score (IDF1) improved by 2.55%, and the Recall rose by 9.68%. The results demonstrate that the proposed method can effectively track pigs in complex farming environments with frequent occlusions, large scale variations, and abrupt motion, providing reliable underlying data support for downstream applications, such as abnormal behavior identification and automated physical activity statistics in intelligent pig farming systems.

Current research on shrub stubble cutting machines primarily focuses on mechanical structure design and operational performance, whereas shrub localization and cutting status still rely on manual judgment. To address this issue, an intelligent desert shrub stubble cutting machine integrating structural design with vision-assisted operation was developed. The overall structure and key component parameters were determined through theoretical analysis, and the disc cutting process was validated via Workbench simulation. The YOLOv5s model incorporating a Focal-CIoU loss function was adopted to enhance shrub detection under complex background conditions. Image processing and binocular vision were employed to obtain the three-dimensional coordinates of shrub roots. Experimental results demonstrate that the cutting device meets the operational requirements; the improved model achieves an accuracy of 91.9%, a recall of 96.5%, and a mAP of 98.3%; and the maximum relative errors for root depth and height measurements are 3.19% and 6.51%, respectively, satisfying the detection and localization requirements for shrub stubble cutting.

This study proposes Smooth-Planner, a robust gradient-based local trajectory planning method for non-holonomic orchard robots operating in narrow row environments. The method employs a B-spline–based optimization framework that explicitly considers kinematic feasibility and collision avoidance without relying on dense global maps. Field experiments conducted in a standardized orchard demonstrate stable and repeatable performance, with lateral deviations effectively constrained and a minimum safety clearance of 0.90 m consistently maintained from crop rows. The results validate the practical safety and reliability of the proposed approach for autonomous agricultural operations.

To reduce miss-seeding and improve the stability of single-clove pickup in garlic seed metering devices, a spoon-clamp type garlic seed metering device was designed. The proposed mechanism combined spoon-type seed pickup with spring-assisted clamping, so garlic cloves with irregular surfaces could be picked up, carried, cleaned, and discharged more stably. Based on the geometric characteristics of three garlic varieties, the key parameters of the seed plate, seed pickup spoon, and opening-closing mechanism were determined by theoretical analysis. A three-dimensional model of the seed metering device and garlic-clove models were established using the discrete element method. Single-factor simulations were then performed to analyze the effects of seed plate speed and seed pickup spoon diameter on the qualified seeding index, reseeding index, and miss-seeding index. The suitable simulated operating range was 1–7 r/min for seed plate speed and 25–35 mm for seed pickup spoon diameter. Bench tests showed that the suitable parameter combination for “Acheng Purple Garlic” and “Nong’an Purple Garlic” was a seed plate speed of 4 r/min and a seed pickup spoon diameter of 32 mm. For “Zhaozhou White Garlic”, the suitable combination was 5.5 r/min and 28 mm. These results showed that the spoon-clamp mechanism was feasible for improving single-clove garlic metering stability under controlled bench-test conditions

With the development of society and the advancement of science and technology, the object detection technology driven by Artificial Intelligence is also constantly innovating. As an important task in the field of agricultural Computer Vision, fruit detection in real environments faces many challenges. This paper reviews and analyzes the latest breakthroughs and representative studies in this field. Based on the existing research, the existing fruit detection algorithms are roughly divided into 5 categories: (1) the traditional fruit detection algorithm based on manual features; (2) the fruit detection algorithm based on two stages; (3) the fruit detection algorithm based on one stage; (4) the fruit detection algorithm based on anchor-free frame, and (5) the fruit detection algorithm based on transfer learning. This paper also discusses various application scenarios, such as multi-object detection, complex backgrounds, and edge computing. It also summarizes the classic and cutting-edge technical methods at the current time point, providing valuable insights for fruit detection in agricultural production.

Automatic orientation and conveying are critical to enhancing the operational efficiency of postharvest cabbage commercial processing (e.g., trimming), reducing labor input costs, and boosting commodity added value. To address this demand, this study presents the optimal design of an automatic orientation and conveying device for postharvest cabbages based on conical treads, leveraging the moment of inertia principle. Through dynamic modeling and analysis, the key structural parameters governing orientation performance and their feasible ranges were quantified. Furthermore, the orientation reliability of the proposed structure was validated via ADAMS-based motion simulation. A prototype of the postharvest cabbage orientation and conveying device was fabricated, and parameter optimization experiments were conducted to determine the optimal operating conditions. The results indicate that the device achieves superior operational performance when the orientation roller angle is set to 20°, the axial clearance is 60 mm, and the conveying chain linear speed is 300 mm/s. Under these optimal parameters, the orientation success rate of postharvest cabbages reaches 98.67 ± 1.05%, with an orientation angle deviation of 5.18 ± 0.39°. This design remarkably improves the orientation and conveying precision of postharvest cabbages, laying a solid theoretical foundation and providing technical support for subsequent commercial processing operations such as root cutting.

Panax notoginseng is a high-value medicinal rhizome crop widely cultivated in the hilly and mountainous regions of southwest China, where traditional manual harvesting remains labor-intensive and inefficient. To address these challenges, this study focuses on the design, kinematic analysis, optimization, and validation of a separation conveyor device for a self-propelled Panax notoginseng combine harvester. The separation conveyor was developed based on the agronomic characteristics of Panax notoginseng and the mechanical requirements of soil-rhizome separation. A comprehensive methodology integrating theoretical modeling, kinematic and mechanical analysis, coupled discrete element-multibody dynamics (DEM-MBD) simulations using EDEM and RecurDyn, and laboratory experiments was employed. Key operational parameters, including conveyor speed, vibration frequency, and lifting angle, were systematically evaluated through quadratic regression and orthogonal experimental design. Simulation results indicated that effective soil separation could be achieved within 2.25 s, with fine soil particles separating earlier than larger aggregates. Laboratory tests identified optimal parameters of 0.8 m·s?¹ conveyor speed, 1.5 Hz vibration frequency, and a 20° lifting angle. Field validation experiments confirmed the reliability of the optimized design, achieving average separation and conveying rates of 96.87% and 96.42%, respectively, with a low rhizome damage rate of 1.88%. The results demonstrate that the proposed separation conveyor provides an effective solution for mechanized harvesting of Panax notoginseng and offers valuable reference for similar rhizome crop harvesters.

Detasseling the female parent plants is a critical operation in hybrid corn seed production. This article systematically reviews global developments in mechanized detasseling technology for corn seed production. First, it provides an in-depth analysis of two key technologies supporting mechanized detasseling: the biomechanical characteristics of corn tassels and intelligent tassel-detection technologies based on machine vision and photoelectric sensing. Second, the current development of mechanized detasseling equipment is reviewed, and the technical parameters and field performance of representative domestic and international machines are compared in detail. The comparison identifies four major constraints limiting the wider adoption of this technology in China: an insufficient single-pass detasseling rate, poor coordination between machinery and agronomic practices, a high crop-damage rate, and low acceptance among farmers. Finally, three targeted optimization strategies are proposed: developing intelligent and precision detasseling equipment, promoting closer integration of agricultural machinery and agronomy, and strengthening field demonstration and extension activities. This review aims to provide a theoretical basis and practical reference for improving mechanized detasseling processes and supporting the development of more effective detasseling equipment.

Manual weeders reduce labour requirements compared with hand weeding but remain time-consuming and physically demanding in paddy cultivation. A rotary power weeder powered by a 4.5 hp single-cylinder, air-cooled engine and equipped with three rotary units was developed for direct-seeded paddy and evaluated under field conditions. The machine was tested at three forward speeds and compared with manual weeding using a khurpi. Weeding efficiency decreased with increasing forward speed, whereas plant damage and power requirement increased. The highest grain yield and benefit–cost ratio were obtained at 1.27 km h?¹, which was identified as the optimum operating speed. Field evaluation demonstrated that the developed machine reduced labour requirements, provided effective weed control, and improved the operational efficiency of mechanical weed management in direct-seeded paddy cultivation.

To address the high power consumption, limited load adaptability, and inadequate parameter matching of conventional axial-flow threshing devices, power-consumption tests were conducted on an axial-flow threshing device with adjustable threshing intensity using rice as the test material. The tests examined different threshing-intensity modes, separating-screen types, and operating parameters. The effects of these factors on no-load power consumption, effective power consumption, total power consumption, and mechanical efficiency were analyzed. The results showed that total power consumption was minimized when the threshing-intensity adjustment plates were fully open and the rectangular-hole separating screen was used. Total power consumption increased with increasing drum speed, deflector angle, and feed rate, but decreased with increasing concave clearance. Mechanical efficiency initially remained stable and then decreased as drum speed increased, whereas it increased with increasing deflector angle and feed rate. The specific threshing power consumption, expressed per unit feed rate, ranged from 4.4 to 5.2 kW/(kg/s). It increased with increasing drum speed, deflector angle, and feed rate, and decreased with increasing concave clearance. This study provides a reference for reducing power consumption and optimizing parameter matching in axial-flow threshing devices with adjustable threshing intensity.

This study proposes a real-time method for detecting combine-harvester feed quantity based on multi-source information fusion and error correction, with the aim of addressing control delays caused by information lag. Field tests were conducted to obtain key parameters, including feeding-auger torque and threshing-drum torque, determine their relational weights, and establish a prediction model. The model achieved a maximum relative error of 9.64% and a mean relative error of 4.55%, meeting the requirements of practical field operation.

The soybean-maize strip intercropping mode has been widely popularized, yet existing plant protection machinery fails to dynamically adjust pesticide application according to crop zones, suffers from poor pesticide application accuracy, and is prone to causing crop damage. Aiming at the above problems, this study proposed a precise strip-zoning pesticide application method for soybean and maize and established an automatic strip-zoning pesticide application control system based on machine vision. Perspective transformation was adopted to correct image viewing angles; after subsequent processing via excess green binarization and opening-closing operations, a dynamic ROI (Region of Interest) positioning and recognition algorithm for crop rows based on the peak value method was applied. The least square method was used to extract crop row lines, the offset was calculated according to the geometric relationship of the centerlines of soybean and maize zones, and an automatic strip-zoning offset compensation model was constructed to achieve precise strip-zoning. Field experiment results show that: under conditions with little shadow and stable light, the recognition algorithm achieved the optimal performance with an accuracy of up to 92%. At operating speeds of 2–5 km/h, the automatic strip-zoning pesticide application control system had a mean strip-zoning deviation of =3.16 cm, with the proportion of deviations within 5 cm reaching 84% (up to 94%). Under wind speeds of 1.4–2.9 m/s, the maximum droplet drift deposition amount of the closed anti-drift device was 9.41 droplets per cm², delivering favorable strip-zoning pesticide application performance, which provides data support and a reference for field precision plant protection operations under the strip intercropping mode.

Mechanised furrowing and fertiliser application is an effective engineering solution for improving operational efficiency and reducing labour intensity in hilly and mountainous orchards. In this study, a systematic optimisation methodology for a disc-type furrowing tool is proposed by integrating discrete element method (DEM) simulations using EDEM with quadratic orthogonal regression experiments and response surface analysis. Key structural parameters, including bending radius, bending angle, and alpha angle, were selected as design variables, and their individual and interactive effects on furrowing power consumption were investigated. The results of the response surface analysis indicate that appropriate structural optimisation can significantly reduce trenching power consumption. The optimal structural parameters were determined as follows: bending radius of 36.79 mm, bending angle of 126.94°, and alpha angle of 43.65°. Under these conditions, the predicted power consumption was 0.85 kW, representing a reduction of 8.88% compared with the original tool design. Soil bin experiments were conducted to validate the DEM simulation results, and the experimental trends showed good agreement with the simulation predictions, demonstrating the reliability of the proposed optimisation approach. This study provides a practical reference for the engineering design and performance improvement of soil-engaging tools.

In greenhouse cultivation of cherry tomatoes, the accurate identification of pollination status is a key prerequisite for achieving automated robotic pollination. As the site of direct interaction during pollination, changes in the colour and morphology of the stigma most accurately indicate whether pollination has been successful. However, due to the minuscule size of the stigma and the extremely subtle visual differences among the three states—unpollinated, pollinated, and fruiting—combined with interference from fluctuating lighting conditions and complex backgrounds in greenhouse environments, traditional methods struggle to provide high-precision visual perception data for pollination robots. To address this issue, this paper proposes an improved model, YOLO11-RCSP, which focuses on the stigma as the core recognition object to achieve automatic classification of the three pollination states: introducing a novel attention mechanism, RFA (Receptive Field Attention), adding an enhancement module (CPA-Enhancer) to the original model, and incorporating SAConv switchable dilated convolutions. In addition, the loss function is improved using the Powerful-IoU method (adaptive penalty factor and gradient adjustment function based on anchor box quality). The precision rate of the improved model reached 88.40%, the recall rate reached 84.60%, and the average precision was 90.80% when the IoU was 0.50. The average precision ranged from 0.50 to 0.95, reaching 63.20%. These metrics represent improvements of 5.20%, 3.30%, 4.40%, and 5.30%, respectively, compared to the original YOLO11 network model, demonstrating stronger feature extraction capabilities and robustness. Ablation experiments further confirm that the attention mechanism and multi-scale optimization have a synergistic effect on enhancing the performance of stigma pollination status recognition. This research will be applied to pistil-level pollination status recognition tasks, helping to advance the development and application of pollination robots, and providing critical technical support for the development of automated pollination equipment in protected agriculture.

Accurate detection of grape diseases in controlled facility environments is of significant importance. In this study, a novel detection model, PWE-YOLO, is proposed based on YOLO v11. In this model, the C3k2 modules in the Neck network are replaced with C3k2-PConv modules, which maintain high-precision feature extraction while reducing model parameters and memory consumption. Furthermore, a WaveletPool module is introduced to replace all Conv modules in the model, except for the 0th and 1st layers, enhancing feature representation and detection accuracy for grape diseases while further reducing model parameters and floating-point operations. EMA attention modules are incorporated at the 17th and 24th layers to improve small-object detection capabilities, thereby increasing overall detection precision. Experimental results demonstrate that PWE-YOLO achieves a precision of 85.0%, recall of 83.6%, and mAP of 86.9%, with 1.96 × 106 parameters and 5.1 × 10? floating-point operations. Compared with the original YOLO v11 model, precision, recall, and mAP increase by 4.5, 1.4, and 1.9 percentage points, respectively, while parameters and floating-point operations decrease by 24.03% and 10.05%. Relative to YOLO v8, v9, v10, v12, and v13, precision improves by 2.2%, 5.3%, 3.3%, 2.2%, and 2.5%, recall by 6.0%, 2.8%, 5.2%, 8.8% and 3.8%, and mAP by 1.9%, 1.8%, 2.8%, 5.0%, and 2.3%, respectively, with corresponding reductions in model parameters and floating-point operations. These results indicate that PWE-YOLO not only provides high-accuracy detection for grape diseases but also reduces computational complexity, achieving a lightweight architecture and faster detection speed, making it suitable for deployment in resource-constrained target detection scenarios.

To address the challenges of low transplanting efficiency, poor planting accuracy, and high labor intensity associated with manual Panax notoginseng transplanting in the hilly and mountainous regions of Yunnan, as well as the limited adaptability of existing transplanting equipment to narrow ridged fields and steep, complex terrain, this study developed a self-propelled, fully electric tracked chassis tailored to the agronomic requirements of Panax notoginseng transplanting. The study included the overall structural design, selection and matching of the power battery and drive transmission system, and development of a dimensionless ratio-coefficient control model relating planting spacing to travel and transplanting speeds under the constraint of the manual seedling-feeding speed. An integrated electronic control system was developed to provide remote-controlled travel, speed-synchronized transplanting control, and straight-line deviation correction. The stability and dynamic performance of the chassis were analyzed theoretically and subsequently validated through field tests under multiple operating conditions. Theoretical results showed that the chassis had a maximum lateral roll angle of 39.19°, a maximum traction force of 7,524 N, and sufficient power reserve for operation on slopes of up to 25°. Field tests demonstrated that, after correction using the speed-adjustment coefficient, the straight-line deviation over a travel distance of 30 m was limited to within 1.1%, exceeding the requirements of the relevant national standard. The measured pivot-turning offset was approximately 191 mm, in close agreement with the theoretical value. Key performance indicators, including slope-climbing stability, ridge-travel stability, and motor thermal stability, all met the applicable national standards. The developed chassis effectively improves the adaptability of transplanting equipment for Panax notoginseng production in hilly and mountainous regions and significantly enhances the automation level and planting accuracy of transplanting operations. This study provides theoretical and technical support for the intelligent development of transplanting equipment for rhizomatous medicinal plants.

To address the challenges of low transplanting efficiency, poor planting accuracy, and high labor intensity associated with manual Panax notoginseng transplanting in the hilly and mountainous regions of Yunnan, as well as the limited adaptability of existing transplanting equipment to narrow ridged fields and steep, complex terrain, this study developed a self-propelled, fully electric tracked chassis tailored to the agronomic requirements of Panax notoginseng transplanting. The study included the overall structural design, selection and matching of the power battery and drive transmission system, and development of a dimensionless ratio-coefficient control model relating planting spacing to travel and transplanting speeds under the constraint of the manual seedling-feeding speed. An integrated electronic control system was developed to provide remote-controlled travel, speed-synchronized transplanting control, and straight-line deviation correction. The stability and dynamic performance of the chassis were analyzed theoretically and subsequently validated through field tests under multiple operating conditions. Theoretical results showed that the chassis had a maximum lateral roll angle of 39.19°, a maximum traction force of 7,524 N, and sufficient power reserve for operation on slopes of up to 25°. Field tests demonstrated that, after correction using the speed-adjustment coefficient, the straight-line deviation over a travel distance of 30 m was limited to within 1.1%, exceeding the requirements of the relevant national standard. The measured pivot-turning offset was approximately 191 mm, in close agreement with the theoretical value. Key performance indicators, including slope-climbing stability, ridge-travel stability, and motor thermal stability, all met the applicable national standards. The developed chassis effectively improves the adaptability of transplanting equipment for Panax notoginseng production in hilly and mountainous regions and significantly enhances the automation level and planting accuracy of transplanting operations. This study provides theoretical and technical support for the intelligent development of transplanting equipment for rhizomatous medicinal plants.

To address the contradiction between obstacle avoidance safety and irrigation operation quality in 3D path planning of fixed-wing UAVs for mountainous agricultural crop irrigation, this paper proposes a multi-strategy improved dung beetle optimization (MDBO) algorithm. First, safe turning radius and pitch angle constraints are embedded into the optimization objective to restrict flight attitude, ensure flight stability under complex mountain terrain, reduce collision risk, and maintain consistent spray deposition. Second, four improvement strategies, including Bernoulli chaotic mapping initialization, golden sine position update mechanism, dynamic adaptive weight, and adaptive Gaussian-Cauchy hybrid mutation perturbation, are integrated into the basic DBO algorithm to enhance global search capability, local exploitation accuracy and convergence performance. Comparative tests on 9 benchmark functions show that MDBO outperforms the basic DBO in convergence speed and solution stability. Furthermore, three irrigation performance indicators (coverage rate, overlap ratio and coverage uniformity) are constructed for posterior evaluation of spray distribution quality. Simulation experiments under three mountainous terrain scenarios verify that MDBO achieves better path accuracy, convergence performance and irrigation effect than traditional algorithms, and can meet the efficient irrigation path requirements of mountainous agricultural crops.

Smartphones have become a convenient tool in agriculture due to their mobility, affordability, and capacity to enhance more efficient crop monitoring, including MATAG variety identification. Detecting MATAG hybrid varieties remains challenging because their morphological traits are difficult to differentiate macroscopically, but reflectance values can be generated using a spectral signature graph. This review aims to explore the development of an intelligent identification system for the MATAG variety based on spectral signature analysis using a smartphone device. It discusses characterisation of MATAG hybrids, spectral signature differences between each coconut species, the development of spectral signature libraries, and the potential of smartphone-spectral signature-based applications compared with manual inspection and laboratory analysis. It also highlights the mechanisms of MATAG variety detection based on spectral signature analysis using a smartphone and practical considerations for farmers. Case studies and testimonials from farmers on how smartphone devices have revolutionised crop monitoring practices were also discussed, together with the potential benefits of using this technology for the agricultural industry. It also elaborates on the opportunities and challenges of detecting MATAG variety using smartphone-spectral approaches, which will be a useful resource for app developers to solve the problems in the field. Overall, this review provides a comprehensive overview of smartphone-spectral signature integration for MATAG detection that eventually helps farmers in the plantation.

To overcome the problems of low germination rate and irregular emergence caused by dense surface structure, poor water permeability, and dormancy of forage seeds, this study screened the optimal plasma generation mode suitable for continuous treatment of forage seeds under atmospheric pressure and evaluated its efficacy and mechanism. Through a comparative analysis of the uniformity and stability of corona, spark, gliding arc, and dielectric barrier discharge (DBD), the results showed that DBD can generate large-area, low-temperature, and uniform plasma in atmospheric air. Taking Leymus chinensis seeds as the research object, the effects of different treatment voltages (0–16 kV) on the glume microstructure and the electrical conductivity of the leachate were investigated. Scanning electron microscopy (SEM) observations indicated that DBD treatment with appropriate parameters caused significant etching on the seed glume surface, forming micropores and fissures, and removing impurities. The electrical conductivity measurements further revealed a “dosage effect”: moderate voltage helped maintain the integrity of the membrane system, whereas excessive voltage led to severe damage to the glume and leakage of intracellular contents. The study confirmed that atmospheric pressure DBD plasma, through physical etching and surface modification, can effectively improve the water absorption channels and physiological activity of Leymus chinensis seeds, and an optimal parameter window exists. These findings provide a technical basis for the development of continuous plasma seed treatment equipment.

Target recognition and localization of picking robots are very important in orchard environments. This paper analyzed the You Only Look Once version 8 normal (YOLOv8n) method in deep learning for citrus picking robots, introduced FasterNet, efficient multi-scale attention (EMA) mechanism, and Wise Intersection over Union (WIoU) loss function, developed an improved YOLOv8n method, and collected a citrus fruit dataset to analyze the recognition and positioning effects of the proposed methods. The selected FasterNet, EMA, and WIoU were all superior to the other types compared, and the improved YOLOv8n method achieved an accuracy of 0.893, a recall rate of 0.894, and a mean average percentage (mAP) of 0.895 for citrus recognition, outperforming other recognition methods. In terms of citrus localization, the average errors on X, Y, and Z axes were 3.56 mm, 3.54 mm, and 4.74 mm, respectively. The findings demonstrate the reliability of the proposed model, and it can be applied to actual citrus picking robots.

This study presents a particle-scale coupled CFD–DEM numerical approach for elucidating aerodynamic sorting mechanisms and optimizing process parameters. Non-spherical particle models were developed to represent the geometric and physical differences between shells and seed-bearing particles. The Stokes number, which characterizes the balance between aerodynamic drag and particle inertia, was identified as the primary determinant of sorting performance. Under the optimized conditions, a primary airflow velocity of 8.4 m/s, a secondary airflow velocity of 7.85 m/s, and a feed mass flow rate of 80 g/s, the system achieved a shell purity of 95.3% and a recovery rate of 58.2% when processing feedstock with an initial shell content of 5%. The results further revealed an inherent trade-off between purity and recovery. These findings provide a theoretical and numerical basis for the design and optimization of aerodynamic sorting equipment.

Automatic transplanting of plug seedlings is critical for improving agricultural efficiency. However, existing systems often suffer from functional fragmentation and use rigid end-effectors prone to damaging seedlings. This study presents a fully integrated automatic transplanting system that uniquely combines multi-tray storage and conveying, precise step-moving, manipulator-based picking and placing with a novel flexible end-effector, and automatic tray collection. In experiments using pepper seedlings, conducted over repeated trials (n=5) with 576 seedlings per trial, the integrated system achieved a picking success rate of 93.24% and a damage rate of 1.43%, with a maximum conveying positioning deviation of 1.79 mm. Compared with previously reported transplanting systems, the proposed system achieves a superior success rate while delivering a substantially lower seedling damage rate. This research provides a practical, highly integrated solution for small-to-medium scale nurseries.

To address the high labour intensity, low operating efficiency, and safety risks associated with loading and unloading flue-cured tobacco leaves in bulk-curing barns, a tobacco-clip loading and unloading device suitable for the confined barn environment was developed. The device integrates a powered platform, a two-stage scissor-lift mechanism, and an online weighing and conveying system. Mechanical modeling and finite element analysis were conducted to verify the structural strength and overturning stability of the lifting mechanism under a rated load of 350 kg and a maximum lifting height of 3 m. The resulting overturning stability coefficient was approximately 2.9. Within an inclination range of 0°–25°, the mass-measurement error of the conveying system did not exceed 0.40%. Field performance tests showed that the maximum conveying rate reached one tobacco clip every 2.7 s. The loading time for a single bulk-curing barn was reduced from 4 h to 1.5 h, while the required labour force decreased from four workers to two. Overall operating efficiency increased by 4.33 times, and the mean time between failures met the requirements for continuous operation during the tobacco-curing season. The developed device enables efficient, low-cost, and reliable loading and unloading of flue-cured tobacco leaves and provides technical support for improving the quality and efficiency of flue-cured tobacco production.

In the process of agricultural intelligence, precise detection of plant organs serves as the foundation for core tasks such as crop phenotyping analysis and yield prediction. However, in complex field environments, small targets such as citrus flowers and shoots face challenges including scale variation, background interference, and dense occlusion, which severely impact detection accuracy. This study improves the YOLOv10 model by introducing the BAM (Bottleneck Attention Module) attention mechanism and GIoU (Generalized Intersection over Union) loss function, constructing a YOLOv10s-BAM-GIoU model suitable for citrus flower, fruit, and shoot recognition. The BAM attention mechanism enhances the models feature extraction capability for small target organs under complex backgrounds through parallel channel and spatial attention branches; the GIoU loss function improves the localization accuracy of densely occluded targets by optimizing the geometric alignment between predicted and ground-truth boxes. Validation experiments were conducted on a self-constructed dataset. The experimental results show that the improved YOLOv10s achieves significant advantages in comprehensive detection accuracy, with an mAP50 of 89.1%, representing an improvement of 2.9%~9.5% over the original YOLOv10s and other comparative models. In fine-grained category detection, the model achieves mAP50 of 91.2%, 83.6%, and 92.5% for shoots, flowers, and fruits, respectively. Furthermore, while maintaining high detection accuracy, the model achieves a detection speed of 23.6 ms per frame, meeting real-time detection requirements. The research results demonstrate that the improved YOLOv10s model integrating the BAM attention mechanism and GIoU loss function achieves an optimal balance between accuracy and speed in citrus organ detection tasks, providing a preferred solution for field real-time detection systems.

To address the low control precision caused by the high nonlinearity and pure time-delay of hydraulic pipelines in variable-rate soybean foliar fertilization, this study developed an automatic variable-rate control system for boom sprayers. Based on Fluent dynamic mesh simulations, the throttling characteristics of the electric control valve were revealed, and a second-order transfer function model with pure time-delay was accurately identified for the actuator. To overcome the reliance of conventional fuzzy controllers on expert experience, an improved Sparrow Search Algorithm-optimized Fuzzy PID (ISSA-Fuzzy PID) control strategy was proposed. By integrating optimized population initialization and adaptive global search, this improvement mechanism effectively avoids premature convergence during dynamic parameter tuning. Simulation results indicate that the proposed algorithm compresses the rise time to 0.35 s, limits the overshoot to within 20%, and achieves steady-state convergence within 1.0 s. Bench tests verified that the average detection errors for system flow rate and pressure were merely 2.14% and 2.34%, respectively. Under the working pressure of 250–350 kPa, dynamic pressure regulations were completed stably within 1.9 s without significant overshoot, exhibiting improved regulation precision under high-flow conditions. This research effectively overcomes the lag bottleneck of actuating mechanisms, providing an integrated control solution with high dynamic response and steady-state precision for variable-rate applications. Practically, this optimized system ensures rapid dosage correction and pressure stability, providing technical support for improving the operational timeliness and fertilizer-use efficiency of field spraying operations.

To address the limited maneuverability and terrain adaptability of conventional liquid-fertilizer injection equipment in hilly and small-plot farmland, a compact self-propelled injector integrating a narrow-track four-wheel-drive chassis, an angle-adjustable injection unit, and a crank-slider penetration mechanism was developed. Unlike large tractor-mounted or fixed-orientation systems, the proposed machine integrates inter-row movement, posture adjustment, needle penetration, and timed point injection on a lightweight platform. The discharge characteristics of the injection needle were investigated using computational fluid dynamics, and a three-factor, three-level Box–Behnken design was employed to optimize injection duration, inlet pressure, and outlet-hole number, with fertilizer volume per injection as the response. Response surface optimization identified an injection duration of 1 s, an inlet pressure of 0.45 MPa, and six outlet holes as the preferred parameter combination. Field validation yielded a measured fertilizer volume of 675.4 mL per injection, compared with the predicted value of 700 mL, corresponding to a relative difference of 3.51%. The results demonstrate the feasibility of controllable deep point placement under the tested conditions and provide a lightweight equipment solution for liquid-fertilizer application in hilly and small-plot farmland.

Irregular branches in agricultural picking environments require rapid collision-free path planning and smooth manipulator motion. This study developed an improved RRT algorithm guided by grid numbering, termed Op-timized-RRT, based on a unified indexed-set representation of numbered primary and backup free-grid regions. Within this framework, dynamic region adjustment, target biasing, node occupancy, and local burst expansion jointly guide the search, while greedy pruning and quintic polynomial interpolation are applied to post-process the resulting path. Unlike geometric-region-based methods that generate coordinate samples within an up-dated sampling window, Optimized-RRT selects a valid grid number from the active set and maps it directly to the corresponding state. Compared with RRT, GB-RRT*, and Informed-RRT*, the complete algorithm reduced planning time, the number of iterations, and the final number of path nodes by 40.61%–94.32%, 57.51%–88.66%, and 89.29%–94.59%, respectively, in two-dimensional scenarios, and by 88.95%–98.92%, 79.26%–94.67%, and 67.31%–90.83%, respectively, in three-dimensional scenarios. The reduction in the number of nodes resulted from the combined effects of guided search and path pruning. Manipulator simulations and a demonstration on a single experimental platform confirmed the executability of the generated trajectories under the tested conditions.

Control of greenhouse microclimate requires the interpretation of several environmental and substrate-related parameters, including air temperature, relative humidity, light intensity, carbon dioxide concentration and soil moisture. This paper compares three machine learning models, Random Forest, Extreme Gradient Boosting (XGBoost) and a multilayer perceptron (MLP), for 30-minute-ahead prediction of irrigation, ventilation and shading states in a greenhouse. The models were trained using data collected from a wireless sensor network (WSN) with four sensor nodes installed in the protected cultivation area. After preprocessing, scaling, error removal, temporal synchronization and resampling at 10-minute intervals, a structured dataset of 7063 records was obtained. Individual sensor readings, spatially aggregated variables, time-related features, rolling averages and short-term differences were used to describe both the current and recent evolution of the greenhouse environment. The dataset was split chronologically into training, validation and test subsets, corresponding to 70%, 15% and 15% of the records, respectively, while preserving the temporal order of the measurements. The best average performance was obtained by XGBoost, with a mean F1-score of 0.9237, followed by Random Forest with 0.9045 and MLP with 0.5862. Random Forest achieved the best results for irrigation and ventilation control, with F1-scores of 0.9630 and 0.9611, respectively, while XGBoost achieved the best result for shading control, with an F1-score of 0.8571. XGBoost also achieved the highest mean accuracy, 0.9506, and mean balanced accuracy, 0.8904. The results indicate that tree-based ensemble models are more suitable than the tested MLP architecture for predictive greenhouse control based on multi-sensor WSN data. The proposed framework supports the selection of a final control model according to prediction performance, stability and interpretability.

Accurate and efficient rice-pest detection is essential for field scouting, early warning and precision control. This study presents a lightweight YOLOv11n-based framework for accurate edge deployment. It integrates a Multi-cognitive Visual Adapter (Mona), a Detail-Preserving Contextual Fusion module (DPCF) and Wise-IoU-based non-maximum suppression (Wise-IoU-NMS). Mona improves multi-scale feature representation with limited overhead; DPCF preserves fine-grained pest details during contextual fusion; and Wise-IoU-NMS stabilizes localization of overlapping and densely distributed targets. On a public dataset of 5,212 images from six near-balanced rice-pest classes, divided 8:1:1 for training, validation and testing, the proposed model achieved 95.4% mAP@50, 94.8% precision and 94.7% recall with 2.65 M parameters and 6.5 GFLOPs. The F1-confidence curve identified 0.476 as the alarm threshold. Deployment on Jetson Nano using ONNX and frame-level logging demonstrated a practical workflow for real-time monitoring and selective spraying.

To address the deterioration in the seed-metering performance of mechanical seed-metering devices caused by vibration during field operation, a conical deflector-type seed meter was selected as the research object. A vibration-system model of the seed meter under field operating conditions was established, and the principal operating parameters affecting seed-metering performance were determined. The seed-metering process under vibration was then simulated using the discrete element method to investigate changes in the working angle of the seed population and its dynamic characteristics. In addition, vibration bench tests were conducted to evaluate the effects of vibration on seed-metering performance. The main results were as follows: (1) seed-filling efficiency, seed-clearing efficiency, and horizontal seed-throwing displacement were significantly affected by operating speed, vibration acceleration, and vibration frequency; (2) the adverse effect of vibration on seed-metering performance increased with increasing vibration acceleration but decreased with increasing operating speed or vibration frequency; and (3) when the vibration acceleration was below 0.63 g and the vibration frequency exceeded 4.28 Hz, the qualified-seed rate was greater than 95%, and the coefficient of variation of seed spacing was less than 20%. This study clarified the effects of vibration parameters on seed-metering performance and identified appropriate operating ranges, thereby providing theoretical support for the vibration-resistant design and optimization of mechanical seed meters.

Chinese solar greenhouses with earthen walls can provide a suitable environment for crop production during winter. In this study, air and soil temperatures inside solar greenhouses in Shanxi Province, China, were monitored from January 1 to March 31, 2024. The results showed that, during nighttime, vertical air-temperature variation among the measurement points was greater than horizontal variation. Zones with approximately equal soil temperatures were also identified among the measurement points. The onset of the increase in weighted mean soil temperature occurred 4 h later than that of the weighted mean air temperature, whereas the onset of the decrease in weighted mean soil temperature occurred 5 h later. These findings provide a data-based foundation for regulating the thermal environment inside Chinese solar greenhouses with earthen walls.

This study aims to investigate how sustainable agriculture is represented in the digital public sphere by examining technology-driven agricultural systems through artificial intelligence–based text analytics. A dataset of 13,354 English posts collected from the X platform during January 2026 was used, with 10,782 posts retained after preprocessing. The methodology integrates text mining, TF-IDF keyword extraction, BERT-based sentiment analysis, and LDA topic modelling. The optimal number of LDA topics was determined using coherence score evaluation to ensure statistical robustness and thematic interpretability. This study integrates text mining, BERT-based sentiment analysis, and LDA topic modelling within a unified artificial intelligence framework to provide a comprehensive analysis of sustainable agriculture discourse on social media. The results show predominantly neutral discourse (65.27%), reflecting informational content, while positive discourse (33.17%) highlights smart farming and innovation. Negative discourse (1.56%) primarily addresses structural challenges. Three dominant themes emerged: climate-oriented sustainability, community-based practices, and technology-driven agriculture, emphasizing the role of digital technologies in shaping sustainable agricultural systems. The findings provide practical insights for policymakers, agricultural stakeholders, and researchers by supporting evidence-based communication strategies and technology-oriented sustainability policies.