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.

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.