GDS-YOLO: A Rice Diseases Identification Model With Enhanced Feature Extraction Capability

Abstract

Accurate identification of rice diseases is a prerequisite for improving rice yield and quality. However, the rice diseases are complex, and the existing identification models have the problem of weak ability to extract rice disease features. To address this issue, this paper proposes a rice disease identification model with enhanced feature extraction capability, named GDS-YOLO. The proposed GDS-YOLO model improves the YOLOv8n model by introducing the GsConv module, the Dysample module, the spatial context-aware module (SCAM) and WIoU v3 loss functions. The GsConv module reduces the model's number of parameters and computational complexity. The Dysample module reduces the loss of the rice diseases feature during the extraction process. The SCAM module allows the model to ignore the influence of complex backgrounds and focus on extracting rice disease features. The WIoU v3 loss function optimises the regression box loss of rice disease features. Compared with the YOLOv8n model, the P and mAP50 of GDS-YOLO increased by 5.4% and 4.1%, respectively, whereas the number of parameters and GFLOPS decreased by 23% and 10.1%, respectively. The experimental results show that the model proposed in this paper reduces the model complexity to a certain extent and achieves good rice diseases identification results.