基于U-Net的玉米叶部病斑分割算法

本文旨在提出一种基于U-Net算法模型的玉米病程分级方法,实现对玉米常见4类叶部病害程度的快速、准确、客观分级.该方法以两组U-Net模型并行运算实现对玉米叶部病斑图像的语义分割任务.经测试图像分割试验中病斑分割MIoU值达到93.63％,叶片分割MIoU值达到96.33％,且运算速度均在1秒内完成.试验结果表明,该研...

Image Segmentation for Maize Leaf Disease Based on U-Net

In this paper, a grading method of maize disease course based on U-Net algorithm model is proposed, which can quickly, accurately and objectively classify the degree of four kinds of common maize leaf diseases. In this method, the semantic segmentation of corn leaf disease spot image is realized by parallel operation of two groups of U-Net models. In the test image segmentation experiment, the MIoU value of disease spot segmentation is 93.63%, and the MIoU value of leaf segmentation is 96.33%, and the operation speed is completed in 1 second. The experimental results show that this study collects data sources by means of mobile phone photos, can achieve rapid grading of maize diseases without relying on professional instruments and equipment, and replace the previous method of disease identification by manual visual inspection. The accuracy and objectivity of disease grading are improved. Combined with the Internet of Things equipment, the model can achieve the goal of early warning of maize diseases, reducing the impact of diseases, increasing both production and income by science and technology benefiting farmers.