Tomato Leaf Detection Algorithm Based on Improved YOLOv5

To address the challenges of complex backgrounds and low accuracy in detecting tomato leaves, we propose an improved tomato leaf detection model, YOLOv5s-SPD, based on the YOLOv5 network, for identifying tomato leaf diseases. First, the SSConv module is introduced into the backbone network of the YOLOv5 algorithm, replacing the C3 module with the SSConv module to reduce feature redundancy in both spatial and channel dimensions, thus lowering the computational load of the model. Additionally, a PSA attention mechanism is incorporated to enhance the model's feature extraction capability for tomato leaf boundaries in harsh environments, reducing the miss detection rate. The bounding box loss function is also replaced with DIoU, taking into account the distance and overlap between bounding boxes, thereby enabling more precise measurement of detection box accuracy and improving the positioning accuracy of tomato leaves. Experimental results show that compared to the original YOLOv5 algorithm, the improved algorithm increases the mean precision by 2.4%, reaching 85.9%. The recall rate also shows a significant improvement.