Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms

Abstract

Depending on the severity of the leaf spot disease in the field, it can cause a loss in sugar yield by 10% to 50%. Therefore, disease symptoms should be detected on-time and relevant measures should be taken instantly to prevent further spread or progress of the disease. In this study, an Updated Faster R-CNN architecture developed by changing the parameters of a CNN model and a Faster R-CNN architecture for automatic detection of leaf spot disease (Cercospora beticola Sacc.) in sugar beet were proposed. The method, proposed for the detection of disease severity by imaging-based expert systems, was trained and tested with 155 images and according to the test results, the overall correct classification rate was found to be 95.48%. In addition, the proposed approach showed that changes in CNN parameters according to the image and regions to be detected could increase the success of Faster R-CNN architecture. The proposed approach yielded better outcomes for relevant parameters than the modern methods specified in previous literature. Therefore, it is believed that the method will reduce the time spent in diagnosis of sugar beet leaf spot disease in the large production areas as well as reducing the human error and time to identify the severity and course of the disease.