In order to improve the quality of transplanting devices and solve the problems of the poor effect on soil moisture conservation and more weeds easily growing due to the high mulching-film damage rate with an excessive number of hole openings, we developed a dibble-type transplanting device consisting of a dibble-type transplanting unit, a transplanting disc, and a dibble axis. The ADAMS software Adams2020 (64bit) was used to simulate and analyze the kinematic track of the transplanting device. The results of the analysis show that, when the hole opening of the envelope in the longitudinal dimension was the smallest, the transplanting characteristic coefficient was 1.034, the transplanting angle was 95°, and the transplanting frequency had no influence. With the help of the ANSYS WORKBENCH software Ansys19.2 (64bit), an analysis of the process of the formation of an opening in the mulching film and a mechanical simulation of this process were completed. The results indicate that, when the maximum shear stress of the mulching film was the smallest, the transplanting characteristic coefficient was 1.000, the transplanting frequency was 36 plants·min⁻¹, and the transplanting angle was 95°. In addition, the device was tested in a film-breaking experiment on a soil-tank test bench to verify the hole opening in the mulching film. The bench test showed that, when the longitudinal dimension was the smallest, the transplanting characteristic coefficient was 1.034, the transplanting frequency was 36 plants·min⁻¹, and the transplanting angle was 95°. When the lateral dimension was the smallest, the transplanting characteristic coefficient was 1.034, the transplanting frequency was 36 plants·min⁻¹, and the transplanting angle was 90°. The theoretical analysis, kinematic simulation, and soil-tank test results were consistent, verifying the validity and ensuring the feasibility of the transplanting device. This study provides a reference for the development of transplanting devices.

Multiparameter collaborative optimization of the vibrating screen based on the behavior of oil sunflower seed penetrating screen holes

The phenotypic features and yield of Lentinula edodes fruiting bodies are key metrics in breeding, cultivation, and quality evaluation. To overcome the inefficiency and physical damage associated with manual measurement, this paper proposes an automated approach using a lightweight YOLOv11-Seg model. On the basis of the YOLOv11-Seg model, the ShuffleNetV2 network, the C3k2-FasterBlock feature extraction module, and the convolutional block attention module (CBAM) were introduced to construct a lightweight YOLO-SFCB model, which significantly reduced the complexity and computational cost of the model. The experimental results show that the parameters, floating point operations (FLOPs), and mAP50-95 of the YOLO-SFCB model reach 2.0 M, 7.8 G, and 80.5%, respectively, while the GPU-based inference time is only 15.7 ms. Compared with the original model, parameters and FLOPs were reduced by 29% and 25%, inference time was shortened by 9.8%, and mAP50-95 increased by 0.9%. Based on the YOLO-SFCB model, OpenCV was used to extract the minimum rotation circumscribed rectangle of the stipe and pileus segmentation areas, and the stipe height, stipe diameter, pileus width, and pileus thickness were measured; the average residual is less than 0.35 mm. Finally, using the least squares method, a yield prediction model for Lentinula edodes fruiting bodies was developed. The average prediction errors for fresh weight and dry weight were controlled within 0.5 g and 0.15 g, respectively. The YOLO-SFCB model and the method for extracting phenotypic features and predicting yield of Lentinula edodes proposed in this study can help promote the development of Lentinula edodes breeding and cultivation and stabilize market supply and demand.

Precise detection of cotton apical buds is the primary step toward achieving intelligent topping operations. Existing object detection models still struggle to accurately recognize dense small targets under complex field conditions. In this study, we propose an improved model, MCA-YOLO, based on YOLOv11n, and optimize it from three aspects: feature extraction, computational efficiency, and multi-scale feature fusion. First, we introduce the MLCA attention mechanism into the PSABlock to construct the C2PSA_MLCA module, enhancing the model’s capability to represent both local and global features. Second, a CSPHet module is reconstructed using heterogeneous convolution (HetConv) combined with a dual-path design to reduce convolutional redundancy and improve feature extraction efficiency. Finally, the original YOLOv11n detection head is replaced with an ASFFHead, enabling adaptive multi-scale feature fusion, thereby improving detection performance for small, dense, and scale-varying targets. Experimental results show that MCA-YOLO achieves Precision, Recall, mAP@0.5, and F1-score of 89.0%, 83.1%, 90.6%, and 85.9%, corresponding to improvements of 3.0, 8.1, 7.1, and 5.8 percentage points over YOLOv11n. Compared with YOLOv11n, the parameters and GFLOPs increase by 50.0% and 31.7%. Even with this increase in model complexity, MCA-YOLO achieves 75 FPS with a model size of 7.76 MB, indicating that it maintains real-time detection capability while improving detection accuracy.