Young apple fruit thinning is important for regulating crop load and improving fruit quality, yet manual thinning is labour-intensive and constrained by narrow operational windows. To address motion-planning challenges in dense fruit clusters containing rigid and flexible obstacles, this study proposes a Semantic Risk-Aware RRT* (SRA-RRT*) algorithm for autonomous robotic thinning. YOLOv11-seg is used for instance segmentation, with detected bounding-box centres serving as nominal anchors for global path planning rather than pedicel cutting points. SRA-RRT* establishes a unified semantic risk-aware planning framework that couples dual-layer risk modelling with risk-guided hybrid sampling and adaptive bidirectional tree expansion, followed by path smoothing for improved trajectory executability. In four controlled 2D benchmarks, SRA-RRT* achieved planning times of 0.15–0.54 s with 100 % success. Eight planners were further compared in 3D environments. In two large-scale 3D benchmarks, SRA-RRT* achieved mean planning times of 0.41 and 0.64 s and path lengths of 173.97 and 173.12 m, respectively, with 100 % success. In the full-scale 1:1 dense fruit-cluster environment, it achieved 1.08 ± 0.18 s, 628.62 ± 18.24 mm, and 100 % success. Continuous thinning simulations achieved 93.33 % task success. Laboratory prototype tests yielded a planning time of 0.26 ± 0.15 s, a path length of 1.52 ± 0.17 m, a total task time of 28.44 ± 5.15 s, a minimum rigid clearance of 18.42 ± 5.23 mm, and 90.00 % thinning success. These results demonstrate the application-oriented feasibility of SRA-RRT* for efficient and risk-aware robotic young apple fruit thinning.

Domestically developed wheat breeding plot combine harvesters in China currently utilize cyclone separation self-cleaning systems. However, these systems struggle to meet the agronomic requirement of zero wheat grain residue. Seed mixing caused by residual grains can compromise the accuracy of entire breeding field trials. This study focused on the structural design of a cyclone separation self-cleaning system based on high self-cleaning agronomic requirements. Research was conducted on the key structural and operational parameters of the cyclone separator and the negative-pressure centrifugal fan, preliminarily determining the ranges for critical parameters such as the diameter of the cylindrical section of the separator wall, the dust outlet diameter, and the rotational speed of the negative-pressure centrifugal fan. A test bench for the cyclone separation self-cleaning system of wheat breeding plot combine harvesters was designed and developed. Through single-factor experiments and Box-Behnken design optimization, the effects of key parameters on system performance were investigated. The optimal parameter combination-cylindrical section diameter of 614 mm, dust outlet diameter of 290 mm, and fan speed of 1495 r/min-achieved a self-cleaning rate of 100%, self-cleaning time <= 12 s, loss rate of 1.70%, and impurity rate of 0.16%, fully meeting the requirements for high-quality, rapid, and effective separation and self-cleaning operations.

Traditional fuel-powered grass trimmers suffer from high fuel consumption, low energy efficiency, excessive noise, and exhaust emissions, whereas many existing electric trimmers still have insufficient output power, limited torque capability, and poor anti-stall reliability under variable grass-cutting loads. To address these problems, this paper presents the design, development, and experimental evaluation of a knapsack-type electric grass trimmer driven by a permanent magnet synchronous motor (PMSM). Rather than simply replacing the prime mover, the study establishes a mechanical-electrical load analysis for grass-cutting operation, selects a 1.5 kW PMSM according to the required cutting torque and power, and develops a speed-current dual closed-loop vector control scheme with current limiting and anti-stall protection. A prototype with an overall length of 1740 mm and a mass of 9.5 kg was developed and tested under different rotational speeds, cutting-line lengths, and vegetation conditions. Under the standard condition of a 30 cm cutting line and 3000 r/min, the grass height was reduced from approximately 30 cm to about 2 cm, and the measured driver input and output powers were 802.1 W and 702.2 W, respectively. The estimated peak torque reached 3.5 N·m, and the torque ripple remained below 16% under steady operating conditions. The tests also showed that reducing the speed from 3000 r/min to 1000 r/min significantly decreased cutting effectiveness, while increasing the cutting-line length improved the end-line speed but could lead to saturated power consumption. Under tough and tall weed conditions, the current-limiting strategy restrained the phase current below 8 A (peak), preventing motor burnout during temporary blockage. These results demonstrate that the PMSM vector-control drive can improve power density, speed regulation, and anti-stall reliability of knapsack-type electric grass trimmers under practical variable-load conditions.

In arid irrigation zones, conservation tillage with surface residue retention is an effective strategy to reduce evaporation and improve water use efficiency. However, achieving consistent seeding depth under stubble-covered terrain remains a major challenge, as depth variability can negatively affect seed emergence and early crop development. To provide a controllable and repeatable environment for algorithm development and system validation, this study designs a simulation testing platform for precision seeding in residue-covered field conditions. The platform integrates terrain simulation, RGB–3D vision-based ground-distance perception, and electric-cylinder-driven depth control, enabling dynamic testing of seeding responses under varying soil and residue configurations. Experimental results demonstrate that the platform effectively replicates complex field conditions, validates control strategies, and reduces the need for extensive field trials. The proposed simulation testing environment provides a valuable tool for the development and optimization of precision seeding systems in conservation tillage applications.

Field breeding trial-plot harvesting is one of the key processes in crop breeding, as any mixing between varieties during harvest directly leads to the invalidation of breeding data. Therefore, achieving zero-residue self-cleaning inside the machine during harvesting is essential. Existing studies have largely relied on simulations to optimize cleaning parameters. However, research specifically targeting the synergistic design of the mechanical and pneumatic components of the cleaning device to achieve efficient and thorough self-cleaning in complex real-world conditions remains lacking. To address this issue, this paper presents a novel cleaning system specifically designed for efficient self-cleaning and optimizes its key parameters. Key structural parameters of the straw walker, vibrating sieve, and cleaning fan were analyzed, establishing preliminary ranges for crank speed, sieve-airflow angle, and fan speed. A test bench was developed, and single-factor experiments were conducted to investigate the effects of these parameters on core self-cleaning indicators, including the self-cleaning rate and self-cleaning time. The optimal parameter combination was obtained using the Box-Behnken design (BBD) response surface methodology: a crank speed of 390.80 r/min, a sieve-airflow angle of 29.88 degrees, and a fan speed of 1995 r/min. Bench tests validated that the system achieved excellent cleaning performance while ensuring a self-cleaning rate of 100% and a reduced self-cleaning time of 20 s. The system's effectiveness was further validated through field experiments using a 4LX1 prototype harvester on three wheat varieties. Results demonstrated zero grain mixing between plots, with self-cleaning times of 9-12 s. Both bench and field test results exceeded the relevant standards, effectively resolving the long-standing issue of grain residue in trial plot harvesting. Through dual validation, this study provides a referential solution for addressing grain residue and establishes a theoretical foundation for the synergistic design of efficient and precision breeding harvest technologies.

Conventional pneumatic precision planters still face challenges in combining high-speed operation with accurate seed placement and embryo protection under zero-velocity seeding conditions. This study presents a dual-motor rotating–throwing seed-metering device that simultaneously overcomes these challenges. Instead of relying on conventional imprecise airflow to generate initial velocity, seeds are accelerated and released by a motor-driven spoon with precisely defined kinematic profiles. By accurately controlling seed-throwing velocity and angle, the system compensates for the forward motion of the machine to achieve zero-velocity seeding and accurate landing point control across the full speed range. The elimination of seed tubes prevents frictional embryo damage, particularly benefiting fragile seeds such as cotton or peanuts. High-speed imaging (1000 fps) verified uniform initial seed ejection conditions, stable trajectories, and landing position errors below 1.5 cm at 7–13 km/h. The proposed electromechanical approach provides accurate metering, zero-velocity seeding, and seed protection under high-speed conditions, overcoming the inherent limitations of airflow-dependent systems and offering a robust alternative for precision agriculture. Compared with conventional pneumatic meters, the proposed system reduced seed landing variation by over 50%, demonstrating superior robustness under 7–13 km/h operation.

Aiming to resolve the problem of the poor peanut seed-filling effect under high-speed operation when developing high-speed peanut sowing with precision, a peanut precision seed-metering machine with an auxiliary air-suction seed-filling device was designed. Focusing on the force analysis of peanuts in the seed chamber, the peanut seed disturbance principle in the seed-metering machine for the blowing structure of an auxiliary air-suction seed-filling device was clarified. The seed-filling process was analyzed via DEM-CFD coupled simulation, and three factors affecting the seed-filling effect were identified, namely the seed-filling chamber ‘V’ angle γ, the bottom blow-air-hole cross-sectional area S, and the bottom blow-air-hole airflow velocity vq, and the ranges of values of the three factors were determined. The Box–Behnken test was conducted using the seed-filling index and leakage index as the indexes. The results show that the seed-filling chamber ‘V’ angle γ is 56.59°, the bottom blowhole cross-sectional area S is 1088.4 mm2, and the blowhole air velocity vq is 12.11 m·s−1. At this point, the peanut seed suction qualification index and leakage index are optimal, the seed suction qualification index is 96.33%, and the seed leakage index is 2.59%. At the same time, the field test shows that a sowing operation speed of 8–12 km·h−1, a qualified index > 93%, and a leakage index < 4.5% are required to meet the agronomic requirements of peanut precision sowing.

Considering the problems of a low soil fragmentation rate and low straw mulching rate in the traditional rotary tiller tillage mode in the saline-alkali land, the layered stubble and soil crushing rotary tillage knife was designed, and the key structural parameters were determined by the analysis of rotary tillage knife-soil-straw movement. The soil-straw movement behavior in saline-alkali land under different working parameters of rotary tillage was analyzed, and the discrete element modeling of soil-straw-rotary tillage in saline-alkali land was established. In addition, the dynamic process of soil-straw aggregate fragmentation in saline-alkali land from a microscopic perspective was systematically explored. Combined with experimental optimization analysis, the optimum working parameters of the saline-alkali rotary tiller were obtained with a forward speed of 2.02 km/h, a working depth of 178.83 mm, and a rotation speed of 324.48 r/min. To verify the field performance of the machine, the soil fragmentation rate, straw burial rate, and tillage depth stability were chosen as test indices for the field trial. The average soil fragmentation rate was 91.85%, the average straw returning rate was 91.09%, and the average stability of tillage depth was 91.12%, indicating that the designed rotary tiller can effectively improve soil crushing and straw burial in saline-alkali land and meet the basic requirements of high-performance seedbed preparation in saline-alkali land.

Vibrators are widely used in agriculture, such as for vibrating trees to harvest fruits and nuts, or for vibrating screens to separate different materials (e.g. plants and soil or grain and debris) in the harvesting process. Traditional vibrators are bulky and configured with fixed mechanical transmission, so they cannot be precisely controlled and cannot adapt to different conditions, causing negative effects such as ineffective vibration or damaging tree barks. In this paper, a full-directional and lightweight electric vibrator is designed. The unidirectional vibration force is produced through the utilization of two centrifugal forces that are generated by the eccentric mass rotation of two motors. Firstly, the vibration direction can be adjusted to any direction by adjusting the meeting position of the two centrifugal forces. Secondly, the vibration force can be adjusted by changing the motor speed, as the centrifugal force is proportional to the square of the rotation speed. The vibrator is tested with laboratory bench experiment and with agricultural application for vibrating a tree. The prototype vibrator can produce 680N with the weight of 7.2kg, the force can be further improved by increasing the eccentric mass, increasing the rotation speed or decreasing the rotation arm length. The vibrator can be applied to smart agriculture, such as nut and fruit harvesting, or adaptive vibration screening.

This manuscript addresses the issue of irrigation water management with high efficiency and effectiveness and focuses on systems associated with significant water losses, which is sprinkler irrigation. This article presents mathematical modeling that enables the application of precision irrigation using a gun sprinkler robot. The sprinkler robot was fabricated in the Faculty of Agriculture and Natural Resources workshop at As-wan University. The experiments were conducted using 12, 14, and 16 mm nozzle sizes and three gun heights, 1.25, 1.5, and 2 m, at three forward speeds, 25, 50, and 75 m/h. The results revealed that at nozzle 12, the actual wetted diameter would be less than the theoretical diameter by a percentage of 2–5%, while at nozzle 14, it ranged from 2 to 7%, but at nozzle 16, it increased from 6 to 9%. The values of evaporation and wind drift losses were always less than 2.8 mm. The highest efficiency was achieved at the lowest forward speed (25 m/h) and using a 1.5 m gun height. The highest water application efficiency was 81.8, 82.5, and 81.1% using nozzle 12, nozzle 14, and nozzle 16, respectively. Precise irrigation control using sensor and variable rate technology will be the preferred option in the future.

To enhance peanut sowing depth consistency, an active depth adjustment planter was designed. This study employs inclination and pressure sensors for ridge surface detection, coupled with a hydraulic cylinder and profiling mechanism to dynamically adjust furrow depth according to ground variations. A mathematical model integrating detection, adjustment, and execution processes was established. The control system adopts an improved DLF-Fuzzy PID (double-loop feedback fuzzy PID) control strategy, with co-simulation in MATLAB/AMESIM for performance comparison. The results demonstrate the improved algorithm’s superiority in sowing depth accuracy. Field experiments evaluated three operational parameters (vehicle speed, pressure, and sowing depth) with the qualification rate as the metric. At 50 mm sowing depth and 3 km/h speed, the system achieved a 94.6% dynamic qualification rate and 2.38% maximum depth variation coefficient. Compared with existing methods, this approach enhances sowing depth control effectiveness by 6.05% and reduces variation by 2.85%.

This paper presents the design of a seed-pressing mechanism for a high-speed suction-type precision peanut planter to address the issue of poor seeding performance at high travel speeds and to reduce seed bounce within furrows. To clarify the working principle of the mechanism, a force analysis of peanut seeds in the furrow and a numerical study using discrete element analysis were conducted under high-speed operating conditions. Simulation results show that when the distance between the center of the seed-pressing wheel and the seeding-tube outlet (DCSPW-STO) is 146.11 mm, the seed-pressing wheel diameter is 198.13 mm, and the machine operating velocity is 6.45 km h−1, the plant spacing qualification index and seeding depth compliance index for peanuts planted after rolling reach their maximum values. The corresponding germination rates of 93.78% and 90.65% indicate satisfactory sowing performance. Field validation trials demonstrate that when DCSPW-STO (lfz) is 146 mm, the seed-pressing wheel diameter (dfz) is 198 mm, and the machine operating velocity (v) is 6.45 km h−1, the post-seeding plant-spacing qualification index and the seeding-depth compliance index reach 90.31% and 89.18%, respectively. Although slightly lower than the simulation results, these values meet the operational requirements for peanut seeding. Field performance comparisons with non-pressure seeding units further confirm that units equipped with the seed-pressing and soil-covering mechanisms significantly improve both the plant-spacing qualification index and the seeding-depth compliance index, satisfying agronomic requirements for high-speed peanut cultivation.

In view of the difficulty and high cost of monitoring the invasion of small aggregations of Spartina alterniflora in coastal wetlands, this study proposes a SA-YOLO detection model. First, by adopting a lightweight cascade attention mechanism as the feature extraction part of the network, the model's ability to extract features from Spartina alterniflora images is optimized. Secondly, the convolution layer with an improved adaptive attention mechanism is added to optimize feature extraction, dynamically adjust the weight of the feature map, and reduce the amount of calculation. Thirdly, the improved adaptive convolution network is used to optimize the original neck layer, improve the model's ability to integrate Spartina alterniflora image features, and reduce the amount of calculation. Finally, a Spartina alterniflora recognition system is independently built. The system effectively implements the proposed method and realizes the detection and recording of Spartina alterniflora information. This study successfully verifies the effectiveness of the proposed method by conducting experiments on the actual collected Spartina alterniflora dataset. The test results show that the recall rate and accuracy of the proposed SA-YOLO Spartina alterniflora detection model are 94.5% and 92.4%, respectively, both reaching a high level. It can be seen that the model can complete the identification and detection tasks of Spartina alterniflora, providing a solution for the identification and information collection of Spartina alterniflora in coastal areas.

This paper proposes an innovative back-rotating flinging method for digging out oilseed beans, which is more suitable for lifting the excavated material, in response to the short service life of the existing digging device caused by the entanglement of the root system with the cutting tool. Through the analysis of the interaction and influence between the rotary tiller and the soil-root complex of the plant, a kinetic model of the soil-root complex of the kudzu rhizome being chopped and thrown was constructed, and the operating mechanism of the reverse rotary tiller for the overall overturning, digging and collision fragmentation of the soil-root complex of the plant root system was clarified. A discrete element model of the soil-enveloped root system of the hyacinth bean plant was constructed in its entirety. The structural parameters of the reverse rotary digging device were optimised using discrete element simulation and field bench tests.

Conservation agriculture features a soil surface covered with crop residues, which brings benefits of improving soil health and saving water. However, one significant challenge in conservation agriculture lies in precisely controlling the seeding depth on the soil covered with crop residues. This is constrained by the lack of ground distance information, since current distance measurement techniques, like laser, ultrasonic, or mechanical displacement sensors, are incapable of differentiating whether the distance information comes from the residue or the soil. This paper presents an image-based method to get the ground distance information for the crop-residues-covered soil. This method is performed with 3D camera and RGB camera, obtaining depth image and color image at the same time. The color image is used to distinguish the different areas of residues and soil and finally generates a mask image. The mask image is applied to the depth image so that only the soil area depth information can be used to calculate the ground distance, and residue areas can be recognized and excluded from ground distance detection. Experimentation shows that this distance measurement method is feasible for real-time implementation, and the measurement error is within plus or minus 3mm. It can be applied in conservation agriculture machinery for precision depth seeding, as well as other depth-control-demanding applications like transplant or tillage.

Aiming at the problems of low intelligence level of peanut seeder, unstable quality of sowing and fertilization, and poor coordination ability of one-time multi-work, this paper proposes a cooperative control method for simultaneous sowing and fertilization in electric-driven peanut planters. In this method, an improved cross-coupling control structure is proposed to realize the cooperative control of sowing and fertilization, and a fuzzy PID controller is designed. In addition, in order to solve the problem of high overshoot and poor system follow-up when the target speed of the control motor changes greatly during the operation process, an improved particle swarm optimization algorithm is introduced to reduce overshoot, improve response speed, and improve the control accuracy and stability of the seed and fertilizer simultaneous sowing control system. The method was simulated and analyzed on the Matlab/Simulink simulation platform, and the simulation results indicate that the dynamic performance and anti-interference capability of the improved controller have been significantly enhanced. To verify the effectiveness of this control method, an experiment on simultaneous sowing and fertilization of peanuts was designed. The experimental data showed that under stable operation, the average sowing qualification rate was 98.67% and the average fertilization qualification rate was 98.34%; under sudden load conditions, the average sowing qualification rate was 97.33% and the average fertilization qualification rate was 98.18%. The method maintained a low fluctuation range under different working conditions, effectively achieving precise simultaneous sowing and fertilization of peanuts. This research can provide an effective technical reference for efficient peanut cultivation.

When potato harvester operates in hilly areas, there are problems such as poor crushing capacity, low potato separation efficiency, high injury caused by slope and lightweight clay characteristics. A reuleaux triangular chain vibrating potato-soil separation device (RTCVPSD) is proposed to solve these problems, using the black loam soil from the hilly areas of Ningxia as the working object. And the structure parameters of RTCVPSD are determined based on the potato planting agronomic model and soil characteristics. Considering the impact of slopes in hilly areas on the potato-soil separation effect, a dynamic model of vibrating lifting potato-soil separation is constructed, which determines the ranges of factors. A coupling simulation model of the interaction between RTCVPSD and potato-soil adopted on Discrete Element Method (DEM) and Multi-Body Dynamics (MBD) is constructed according to operation parameters. The optimal combination of operation parameters of RTCVPSD is determined using efficiency of potato-soil separation and injury rate of potatoes as evaluation indicators. With the participation of the evaluation indicators, the optimal parameter combination of RTCVPSD obtained through simulation is a working inclination angle of 31 degrees, a chain velocity of 0.8 m s-1, and a vibration frequency of 4.9 Hz. The field validation tests of the RTCVPSD for potato harvester are done based on the above operation combination parameters. The results show that the efficiency of potato-soil separation and injury rate of potatoes of the RTCVPSD are 96.6 % and 1.4 % respectively, with relative errors of approximately 0.21 % and 2.86 % compared to the simulation results, validating the constructed simulation model's accuracy. And the skin breakage rate is 1.48 %. The device shows good harvesting performance and meets the technical specification requirements. This study can serve as a theoretical guidance for simulating analysis of the separation process and optimizing parameter configurations of potato harvesters in hilly areas.

Aiming at the problem that there are few studies on the turnover device in the current peanut laying operation equipment and lack of simulation parameters, this study measured the characteristic parameters of upright peanut plants and tested the feasibility of conveying the turnover device to flip the vine. By studying the mechanical properties of peanut plants, the suitable clamping height range of plants was determined. By studying the size, mass parameters and distribution of peanut plants, the possibility of plant flipping and falling was analyzed, and the main structure and working principle of the conveying flipping device were determined. Based on the RECURDYN-EDEM coupling, the joint simulation of the conveying and overturning device was carried out to complete the dynamic analysis of the plant conveying and overturning process, and the simulation parameters were further optimized. The test results show that the device can achieve 95.1 % plant turnover completion and improve the quality of field drying, which can meet the requirements of peanut laying and harvesting operation. It can provide a theoretical basis for the design of two-stage peanut harvesting device in the future.

To address the issues of high pod damage rate and unpicked pod rate in the picking device of peanut picking combine harvesters during the harvesting of sun-dried peanuts, a low-damage peanut picking device was developed. This device combines flat pin teeth with a two-stage round steel concave screen. Contact models between the picking components and peanut pods, as well as between pods and the concave screen, were analyzed to determine the optimal structural parameters of the picking components and the most suitable concave screen type. Using peanut plants that had been dug, windrowed, and naturally sun-dried in the field for 3–5 days as test material, bench tests were conducted with pod breakage rate and unpicked pod rate as evaluation indices. The installation direction of the picking elements and the combination form of the concave screen were used as experimental factors. The optimal configuration was determined to be flat pin teeth installed with parallel axial forward bending with a tip fillet radius of 6 mm, and a concave screen composed of right round steel + straight round steel with front sparse and rear dense type. Field comparative experiments with a conventional picking device—comprising cylindrical bar teeth and a straight round steel concave screen—showed that the pod breakage rate decreased from 1.92% to 1.17%, and the unpicked pod rate decreased from 1.14% to 0.62%. This study provides a theoretical basis for the structural optimization and performance enhancement of the threshing device in peanut picking combine harvesters.

To address behavioral interferences such as head turning and lowering during rumination in group-housed dairy cows, an enhanced network algorithm combining the YOLOv5s and DeepSort algorithms was developed. Initially, improvements were made to the YOLOv5s algorithm by incorporating the C3_CA module into the backbone to enhance the feature interaction and representation at different levels. The Slim_Neck paradigm was employed to strengthen the feature extraction and fusion, and the CIoU loss function was replaced with the WIoU loss function to improve the model’s robustness and generalization, establishing it as a detector of the upper and lower jaws of dairy cows. Subsequently, the DeepSort tracking algorithm was utilized to track the upper and lower jaws and plot their movement trajectories. By calculating the difference between the centroid coordinates of the tracking boxes for the upper and lower jaws during rumination, the rumination curve was obtained. Finally, the number of rumination chews and the false detection rate were calculated. The system successfully monitored the frequency of the cows’ chewing actions during rumination. The experimental results indicate that the enhanced network model achieved a mean average precision (mAP@0.5) of 97.5% and 97.9% for the upper and lower jaws, respectively, with precision (P) of 95.4% and 97.4% and recall (R) of 97.6% and 98.4%, respectively. Two methods for determining chewing were proposed, which showed false detection rates of 8.34% and 3.08% after the experimental validation. The research findings validate the feasibility of the jaw movement tracking method, providing a reference for the real-time monitoring of the rumination behavior of dairy cows in group housing environments.