To obtain the discrete element simulation model parameters suitable for the interaction between cinnamon soil and soil-engaging components, the Hertz–Mindlin with JKR contact model in EDEM simulation software was used to calibrate the relevant model parameters of cinnamon soil. Firstly, the particle size distribution, moisture content, volume density, Poisson’ s ratio, shear modulus, and other parameters of the cinnamon soil were measured with cinnamon soil as the research object. Further, taking the stacking angle as the response value, the Plackett–Burman test, the steepest climbing test, and the Box–Behnken were designed by using the Design-Expert software to calibrate and optimize the physical parameters of soil simulation. The optimal parameter combination was obtained: cinnamon soil–cinnamon soil rolling friction coefficient was 0.08, soil JKR surface energy was 0.37 J/m⁻², and cinnamon soil–steel static friction coefficient was 0.64. Finally, the discrete element simulation verification test of stacking angle and cutting resistance was carried out under the calibrated parameters. The comparative calculation showed that the relative error between the simulated stacking angle and the measured stacking angle was 0.253%, and the maximum relative error between the simulated cutting resistance and the measured cutting resistance was 10.32%, which was within the acceptable range, indicating the high accuracy and reliability for the calibration parameters. The research results have important reference value for the energy-saving and consumption-reducing design of soil tillage components and provide basic data for the simulation of cutting resistance research of cinnamon-soil-engaging components.

In view of the plugged-out end-effector that can adapt only to a specific size of the tray, the needle spacing and angle of the seedling needle are fixed. In this paper, a new type of plugged-out transplanting end-effector is proposed. The end-effector adopts a double-cam structure to automatically adjust the spacing and angle of the seedling needle, which solves the problem of picking seedlings for different sizes of trays. Firstly, the working principle of 72-hole, 128-hole, and 200-hole trays and a plugged-out end-effector was analyzed. The overall structure of the end-effector was designed. Subsequently, the EDEM software was used to construct the pot seedling model and conduct single-factor simulation experiments to identify the range of factors for the subsequent regression orthogonal experiment. Finally, a tray transplanting test platform was built. With the grasping acceleration, penetration angle, insertion depth, and insertion margin ratio as the test factors and the pot seedling breakage rate as the test evaluation indicators. A four-factor three-level orthogonal regression experiment was conducted to establish a regression model of the seedling breakage rate, and its parameters were optimized. The optimal combination is detailed as follows: a 72-hole tray grasping acceleration of 0.28 m/s², a penetration angle of 13°, an insertion depth of 40 mm, and an insertion margin ratio of 15%; a 128-hole tray grasping acceleration of 0.28 m/s², a penetration angle of 12°, an insertion depth of 36 mm, and an insertion margin ratio of 15%; a 200-hole tray grasping acceleration of 0.28 m/s², a penetration angle of 11°, an insertion depth of 32 mm, and an insertion margin ratio of 10%. Under the optimal combination, the breakage rate of 72 holes reached 2.92%. The breakage rate of 128 holes was stable at 1.76%, while that of 200 holes was stable at 0.68%, which is conducive to the study of a general end-effector. The device developed in this study provides an effective solution to taking and throwing different sizes of cavitation trays, thus providing a practical reference for the study of a generic end-effector.

Seedling transplanting is an important part of vegetable mechanized production in modern agriculture. After the seedlings are cultivated on a large scale by the nursery tray, they are planted into the field by the transplanter. However, unlike manual transplanting, transplanter is unable to judge the status of seedlings in the hole during seedling planting, which leads to problems such as damaged seedlings and empty holes being picked in the same order and planted into the field, resulting in yield reduction and missed planting. Aiming at this problem, we designed a seedling selective planting control system for vegetable transplanter which includes vision unit, seedling picking mechanism, seedling feeding mechanism, planting mechanism, pneumatic push rod unit, limit sensor, industrial computer and logic controller. We used asymmetrical light to construct visual identification scenes for planting conditions, which suppresses environmental disturbances. Based on the intersection operation of mask and image, a fast framework of tray hole location and seedling identification (FHLSI) was proposed combined with FCM segmentation algorithm. The vision unit provides the transplanting system with information on the status of the holes to be transplanted. Based on the information, planting system chooses the healthy seedlings for transplanting, improving the survival rate and quality of transplanting. The results show that the proposed visual method has an average accuracy of 92.35% for identification with the selective planting control system of seedlings and improves the transplanting quality by 15.4%.

Rapid estimation of apple phenotypic parameters based on 3D reconstruction

To investigate the interaction mechanism between agricultural tillage machinery and soil, this study established a precise simulation model by integrating physical and numerical experiments using typical yellow cinnamon soil collected from western Henan Province, China. The discrete element parameters for soils with varying moisture contents were calibrated based on the Hertz–Mindlin (no slip) contact model. Through Plackett–Burman screening, steepest ascent optimization, and Box–Behnken response surface methodology, a predictive model correlating moisture content, parameters, and repose angle was developed, yielding the optimal contact parameter combination: interparticle static friction coefficient (0.6), soil–65Mn static friction coefficient (0.69), and interparticle rolling friction coefficient (0.358). For the Bonding model, orthogonal experiments coupled with NSGA-II multi-objective optimization determined the optimal cohesive parameters targeting maximum load (673.845 N) and displacement (9.765 mm): normal stiffness per unit area (8.8 × 10⁷ N/m³), tangential stiffness per unit area (6.85 × 10⁷ N/m³), critical normal stress (6 × 10⁴ Pa), critical tangential stress (3.15 × 10⁴ Pa), and bonding radius (5.2 mm). Field validation using rotary tillers and power harrows demonstrated less than 6% deviation in soil fragmentation rates between simulations and actual operations, confirming parameter reliability and providing theoretical foundations for constructing soil-tillage machinery interaction models.

To address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of Agaricus bisporus, in this study, a machine vision-based intelligent harvesting device was designed according to its agronomic characteristics and morphological features. This device mainly comprised a frame, camera, truss-type robotic arm, flexible manipulator, and control system. The FES-YOLOv5s deep learning target detection model was used to accurately identify and locate Agaricus bisporus. The harvesting control system, using a Jetson Orin Nano as the main controller, adopted an S-curve acceleration and deceleration motor control algorithm. This algorithm controlled the robotic arm and the flexible manipulator to harvest Agaricus bisporus based on the identification and positioning results. To confirm the impact of vibration on the harvesting process, a stepper motor drive test was conducted using both trapezoidal and S-curve acceleration and deceleration motor control algorithms. The test results showed that the S-curve acceleration and deceleration motor control algorithm exhibited excellent performance in vibration reduction and repeat positioning accuracy. The recognition efficiency and harvesting effectiveness of the intelligent harvesting device were tested using recognition accuracy, harvesting success rate, and damage rate as evaluation metrics. The results showed that the Agaricus bisporus recognition algorithm achieved an average recognition accuracy of 96.72%, with an average missed detection rate of 2.13% and a false detection rate of 1.72%. The harvesting success rate of the intelligent harvesting device was 94.95%, with an average damage rate of 2.67% and an average harvesting yield rate of 87.38%. These results meet the requirements for the intelligent harvesting of Agaricus bisporus and provide insight into the development of intelligent harvesting robots in the industrial production of Agaricus bisporus.