SMDC-YOLOv4: an improved lightweight target detection model for soybean seed detection and counting

The development of an automated soybean seed counting tool is instrumental in predicting yield automatically before harvest. Traditional counting methods are labor-intensive, prone to errors, and time-consuming. To facilitate rapid and accurate detection and counting of soybean seeds, as well as to enhance the breeding speed and level of soybeans, this paper proposes a lightweight YOLOv4 model named SMDC-YOLOv4. Built upon the original YOLOv4 architecture, MobileNetV1 is employed as the backbone network to streamline the model. The Spatial Pyramid Pooling (SPP) structure is enhanced into the Spatial Pyramid Pooling—Fast (SPPF) structure to improve model speed, and depth-wise separable convolutions are applied to the feature fusion network. Activation functions are replaced with the more comprehensive Mish activation function to enhance model stability. Additionally, the CBAM attention mechanism is integrated into the PANet network to further enhance model recognition accuracy. The improved SMDC-YOLOv4 model achieves an accuracy of 79.19% and a recall rate of 30.06%, with an average precision (mAP) of 52.33% and an F1 score of 0.44. This lightweight model demonstrates outstanding detection performance, striking an optimal balance between soybean seed detection efficiency and recognition accuracy.