Improved YOLOv5s tomato leaf small disease spot detection method for embedded devices

In the field of tomato disease spot detection, despite significant advancements in convolutional neural network technology, challenges still persist in detecting small-sized tomato disease spots. This study aims to enhance the tomato disease spot detection model to improve the accuracy of detecting small disease spots on tomatoes. Firstly, the concept of DenseNet is introduced, and a Dense_C3 module is constructed, which incorporates two 3×3 convolutional layers within the Bottleneck of C3 to achieve dense feature propagation and reuse. Secondly, the Convolutional Block Attention Module (CBAM) is employed to weight the feature maps, enhancing the focus on local and spatial information to enhance model accuracy. To validate the effectiveness of the proposed improvements, experiments were conducted on tomato disease spots of different scales, including Septoria Leaf Spot, Early Blight, Late Blight, and Tomato Yellow Leaf Curl Virus. The experimental results demonstrate that the introduction of the Dense_C3 structure and CBAM improves the detection accuracy for small-scale targets. In a comprehensive evaluation, the improved YOLOv5s model shows increases of 2.4% in F1 score and 2.1% in mAP.