Target detection is the key technology of the apple-picking robot. To overcome the limitations of existing apple target detection methods, including low recognition accuracy of multi-species apples in complex orchard environments and a complex network architecture that occupies large memory, a lightweight apple recognition model based on the improved YOLO11 model was proposed, named YOLO-WAS model. The model aims to achieve efficient and accurate automatic multi-species apple identification while reducing computational resource consumption and facilitating real-time applications on low-power devices. First, the study constructed a high-quality multi-species apple dataset and improved the complexity and diversity of the dataset through various data enhancement techniques. The YOLO-WAS model replaced the ordinary convolution module of YOLO11 with the Adown module proposed in YOLOv9, the backbone C3K2 module combined with Wavelet Transform Convolution (WTConv), and the spatial and channel synergistic attention module Self-Calibrated Spatial Attention (SCSA) combined with the C2PSA attention mechanism to form the C2PSA_SCSA module was also introduced. Through these improvements, the model not only ensured lightweight but also significantly improved performance. Experimental results show that the proposed YOLO-WAS model achieves a precision (P) of 0.958, a recall (R) of 0.921, and mean average precision at IoU threshold of 0.5 (mAP@50) of 0.970 and mean average precision from IoU threshold of 0.5 to 0.95 with step 0.05 (mAP@50:95) of 0.835. Compared to the baseline model, the YOLO-WAS exhibits reduced computational complexity, with the number of parameters and floating-point operations decreased by 22.8% and 20.6%, respectively. These results demonstrate that the model performs competitively in apple detection tasks and holds potential to meet real-time detection requirements in resource-constrained environments, thereby contributing to the advancement of automated orchard management.

At present, the potato’s mechanized harvesting rate in hilly and mountainous areas is very low. The reasons for this are that in heavy soil, the separation of potato rhizomes from soil or vines is not sufficient, harvesting machinery is seriously damaged by the potato epidermis, and the harvested potato is easily buried in soil, resulting in a missed harvest. In this paper, a two-stage cleaning potato harvester with wave-type and roller-group-type separating mechanisms was designed, and its overall structure and working principle are introduced in detail. The new cleaning mechanism can increase the effective separating length and effective contact area of the potato–soil mixture so as to achieve the purpose of removing clay and heavy soil. The main separator uses a structure that combines offset waves with opposite waves and a staggered arrangement of large–small diameter straight bars. The secondary separator adopts a device combining left-hand and right-hand separating rollers. The discrete element model of the whole machine was established, and the results of the theoretical analysis were verified by simulation. The key factors affecting the harvest quality were analyzed by variance analysis and response surface analysis, and the field experiment was carried out with the rate of clean potatoes, damaged potatoes, and peeled potatoes harvested as the indexes. The field experiments showed that the machine achieved a rate of photos on or out of the earth of 98.87%, a damaged potato rate of 0.91%, and a peeled potato rate of 1.13%. The research results provide theoretical support and a technical reference for the design and optimization of potato harvesters, as well as the improvement of the potato–soil separating efficiency and harvest quality.

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.

To address the problems of low bright rates and high rates of potato injuries, a left and right-hand rotation combination of potato–soil separation devices was developed. Its overall structure and working principle were introduced. A Texture Analyzer and pressure sensor were used to measure the force threshold of different varieties of potatoes. A discrete element model of separation rollers and potatoes was established. The collision characteristics of potatoes were analyzed using the device inclination angle, rotational speed, and the center distance of the separation rollers as test factors. A field trial was carried out to optimize the best combination of factors by taking the rate of injured potatoes, bright potatoes, and skin-breaking rate as the test indexes. The force threshold for skin-breaking injury in potatoes was found to be 190–195 N. When the inclination angle of the device was 6°, the rotation speed of the separation roller was 100 r/min, and the distance between the centers of the separation rollers was 79 mm. The rate of injury was 1.25%, the rate of bright potatoes was 99.01%, and the rate of skin-breaking was 1.58%. When the inclination angle of the device was 8°, the rotational speed of the separating roller was 80 r/min, and the center distance of the separating roller was 79 mm, the rate of injured potato was 1.43%, the rate of bright potato was 98.64%, and the rate of broken skin was 1.77%. This paper offers an optimized reference for the effectual removal of sticky soil.

In an analysis of the penetration resistance and tillage depth of post-tillage soil, four surface-layer discrimination methods, specifically, three machine learning algorithms—Kmeans, DBSCAN, and GMM—and a curve-fitting method, were used to analyze data collected from the cultivated and uncultivated layers. Among them, the three machine learning algorithms found the boundary between the tilled and untilled layers by analyzing which data points belonged to which layer to determine the depth of the soil in the tilled layer. The curve-fitting method interpreted the intersection among data from the fitted curves of the ploughed layer and the un-ploughed layer as the tillage depth. The three machine learning algorithms were used to process a standard data set for model evaluation. DBSCAN’s discrimination accuracy of this data set reached 0.9890 and its F1 score reached 0.9934, which were superior to those of the other two algorithms. Under standard experimental conditions, the ability of DBSCAN clustering to determine the soil depth was the best among the four discrimination methods, and the discrimination accuracy reached 90.63% when the error was 15 mm. During field-test verification, the discriminative effect of DBSCAN clustering was still the best among the four methods. However, the soil blocks encountered in the field test affected the test data, resulting in large errors in the processing results. Therefore, the combined RANSCA robust regression and DBSCAN clustering algorithm, which can eliminate interference from soil blocks in the cultivated layer and can solve the problem of large depth errors caused by soil blocks in the field, was used to process the data. After testing, when the RANSCA and DBSCAN combined method was used to process all samples in the field and the error was less than 20mm, the accuracy rate reached 82.69%. This combined method improves the applicability of discrimination methods and provides a new method of determining soil depth.

Aiming at the problems of seedling injury and planting leakage due to the lack of seeding clamping force detection and real-time control in vegetable transplanting, a force feedback gripper was developed based on the linear Hall element. The mechanical properties of the stem of pepper cavity seedlings were first analyzed to provide a basis for the design of the gripper. A linear Hall sensor, a magnet, an elastic actuator, and an Arduino Uno development board make up the grasping force detecting system. Upon picking up a seedling, the elastic actuator, which is connected to the magnet, bends like a cantilever beam. As a result of the micro-displacement created by the elastic actuator, the Hall sensor’s voltage changes and can be used to determine the clamping force. Detection avoids direct contact between the sensor and the cavity seedlings, reducing the risk of sensor damage. Finite element method (FEM) simulations were used to determine the initial spacing between the magnet and Hall sensor and the effect of the elastic actuator. Control commands are sent to the servo based on the gripping force collected by the Arduino Uno board. Finally, the functions of accurate measurement, display, storage, and control of the clamping force of the cavity tray seedlings are realized, so that the damage rate of the cavity tray seedlings is reduced. In order to explore the influence of the elastic actuators on the clamping force detection system and the performance of the force feedback gripper, a calibration test of the clamping force detection system and a test of the indoor transplantation of pepper seedlings were carried out. Based on the calibration test, the clamping force detection system has a sensitivity of 0.0693 V/N, linearity of 3.21%, an average linear coefficient of determination of 0.986, and a range of 10 N, which fully meet the clamping force detection accuracy requirements during transplantation. Indoor tests showed that the force feedback gripper was stable and adaptable. This study can provide a reference for detecting and controlling clamping forces during transplantation.