REAL-TIME GRAPE DISEASE DETECTION MODEL BASED ON IMPROVED YOLOv8s

Abstract

This research is dedicated to enhancing the accuracy and processing speed of grape disease recognition. As a result, a real-time grape disease detection model named MSCI-YOLOv8s, based on an improved YOLOv8s framework is proposed. The primary innovation of this model lies in replacing the backbone network of the original YOLOv8s with the more efficient MobileNetV3. This alteration not only strengthens the ability of the model to capture features of various disease manifestations in grape leaf images but also improves its generalization capabilities and stability. Additionally, the model incorporates the SPPFCSPC pyramid pooling structure, which maintains the stability of the receptive field while significantly enhancing processing speed. The integration of the CBAM attention mechanism further accentuates the ability of the model to identify key features, substantially increasing the accuracy of disease detection. Moreover, the model employs Inner-SIoU as the loss function, optimizing the precision of bounding box regression and accelerating model convergence, thereby further enhancing detection efficiency. Rigorous testing has shown that the MSCI-YOLOv8s model achieves an impressive average precision (mAP) of 97.7%, with an inference time of just 37.2 milliseconds and a memory footprint of 39.3 MB. These advancements render the MSCI-YOLOv8s not only highly efficient but also extremely practical for real-time grape disease detection, meeting the actual demands of grape orchard disease identification and demonstrating significant potential for application.