The accurate severity assessment of wheat stripe rust is the basis for the pathogen-host interaction phenotyping, disease prediction, and disease control measure making.
To realize the rapid and accurate severity assessment of the disease, the severity assessment methods of the disease were investigated based on machine learning in this study. Based on the actual percentages of the lesion areas in the areas of the corresponding whole single diseased wheat leaves of each severity class of the disease, obtained after the image segmentation operations on the acquired single diseased wheat leaf images and the pixel statistics operations on the segmented images by using image processing software, under two conditions of considering healthy single wheat leaves or not, the training and testing sets were constructed by using two modeling ratios of 4:1 and 3:2, respectively. Then, based on the training sets, two unsupervised learning methods including
Regardless of whether the healthy wheat leaves were considered or not, when the modeling ratios were 4:1 and 3:2, satisfactory assessment performances on the training and testing sets can be achieved by using the optimal models based on unsupervised learning and those based on supervised learning. In particular, the assessment performances obtained by using the optimal random forest models were the best, with the accuracies, precisions, recalls, and F1 scores for all the severity classes of the training and testing sets equal to 100.00% and the overall accuracies of the training and testing sets equal to 100.00%.
The simple, rapid, and easy-to-operate severity assessment methods based on machine learning were provided for wheat stripe rust in this study. This study provides a basis for the automatic severity assessment of wheat stripe rust based on image processing technology, and provides a reference for the severity assessments of other plant diseases.