Improved YOLOv7-Tiny for the Detection of Common Rice Leaf Diseases in Smart Agriculture

Rapid and accurate detection of rice foliar diseases is essential for yield prediction and food security. This study proposes a multi-size rice leaf disease detection model, YOLOv7-tiny, for fast and accurate detection of rice leaf diseases. The MobileNetV3 lightweight network is introduced to replace the backbone network of YOLOv7-tiny, which reduces the size of the model parameters and improves the extraction capability of features of different sizes; the RCS-OSA is used to replace the original ELAN-1 module, which improves the extraction capability of interlayer features; the TSCODE detector head is designed to enhance the extraction capability of the model for small targets; and the MPDIoU loss function is used to improve the model’s convergence speed and effect. The experimental results show that the average accuracy of ofYOLOv7-TMRTM is 97.9%, and compared with the baseline YOLOv7-tiny model, the accuracy of leaf spot detection is improved for different sizes and types of small target detection results, the YOLOv7-TMRTM model improves mAP0.5 by 4.4%, recall by 4.7% and precision by 8.8% compared to YOLOv7-tiny. The comparison with Faster RCNN, SSD, YOLOv4, YOLOv5s, YOLOv8s, and other mainstream target detection models shows that this method greatly solves the field environment. The problem of small spots and fuzzy edges of photographed rice diseases provides a basis for intelligent management of diseases in the field, which in turn promotes food security in China.