DISEASE DETECTION FROM CASSAVA LEAF IMAGES WITH DEEP LEARNING METHODS IN WEB ENVIRONMENT

Abstract

In this article, it is aimed to classify healthy and four different plant diseases from Cassava plant leaf images. For this purpose, the “Cassava-Leaf-Disease-Classification” data set, which is an up-to-date and difficult data set published in 2020, was used. The used data set includes a total of 21,397 images consisting of healthy and four different diseases. In the study, in the MATLAB environment, the images were first subjected to the Chan-Vese (CV) Segmentation method and the area of interest was determined. Features were extracted with the ResNet 50 and MobileNetV2 deep learning architectures from the detected areas. Extracted features are classified by Support Vector Machine and K-Nearest Neighbor algorithms. The images are divided into two as training and testing according to the K-fold 5 value. The average highest success rates in training and test data were achieved by using the ResNet50 architecture and SVM classifier together, as a result of segmentation. As a result of training and testing processes, 85.4% and 84.4% success rates were obtained, respectively. At the end of the test process of the study, a trained network was obtained according to ResNet50, where the highest success rates were obtained, and MobileNetV2, another deep learning architecture used in the study. It has been compiled with MATLAB Builder NE in order to run these two networks in the web environment. The methods obtained as a result of the compilation are integrated into the ASP.NET MVC5 programming language. Finally, it has been made available to manufacturers with a web-based embedded interface. Thus, a deep learning-based decision support system has been developed that can be easily used by all manufacturers in the web environment.