Lightweight grading method for potato late blight severity based on enhanced YOLOv8-Unet3Plus network

Artificial intelligence for science is a methodology that integrates artificial intelligence into scientific research to improve the precision and efficiency of data analysis and experimental processes. Specifically in potato late blight severity grading, due to the demand for both accuracy and cost-effective deployment, traditional methods are limited by subjective evaluation and timeconsuming manual measurement. In this paper, a lightweight grading model based on an enhanced YOLOv8-UNet3Plus network is proposed to enable objective and accurate potato late blight severity grading. In detail, the YOLOv8 network is optimized by integrating Spatial and Channel Reconstruction Convolution module, Bi-directional Feature Pyramid Network and Powerful-IoU loss, the UNet3Plus network is optimized by embedding Ghost convolution and Multi-Scale Local Response Attention. Experiments on real-world potato late blight datasets demonstrate that our model achieves an precision of 95.73% for leaf localization and an mean Intersection over Union of 82.65% for infected region segmentation with reduced parameters and computational cost. This AI4Science-based model provides an effective solution for potato late blight severity grading.