Potato Leaf Disease Segmentation Method Based on Improved UNet

Abstract

The precise control of potato diseases is an urgent demand in smart agriculture, with one of the key aspects being the accurate identification and segmentation of potato leaf diseases. Some disease spots on potato leaves are relatively small, and to address issues such as information loss and low segmentation accuracy in the process of potato leaf disease image segmentation, a novel approach based on an improved UNet network model is proposed. Firstly, the incorporation of ResNet50 as the backbone network is introduced to deepen the network structure, effectively addressing problems like gradient vanishing and degradation. Secondly, the unique characteristics of the UNet network are fully utilized, using UNet as the decoder to ingeniously integrate the characteristics of potatoes with the network. Finally, to better enable the network to learn disease spot features, the SE (squeeze and excitation) attention mechanism is introduced on top of ResNet50, further optimizing the network structure. This design allows the network to selectively emphasize useful information features and suppress irrelevant ones during the learning process, significantly enhancing the accuracy of potato disease segmentation and identification. The experimental results demonstrate that compared with the traditional UNet algorithm, the improved RS-UNet network model achieves values of 79.8% and 88.86% for the MIoU and Dice metrics, respectively, which represent improvements of 8.96% and 6.33% over UNet. These results provide strong evidence for the outstanding performance and generalization ability of the RS-UNet model in potato leaf disease spot segmentation, as well as its practical application value in the task of potato leaf disease segmentation.