Deep Learning Architectures Performance in Plant Leaf Diseases

Abstract

Developing artificial intelligence applications continue to make our lives easier. Image processing technology has been developed for field-useful studies in medical science, education, finance, agriculture, industry, security, and many other sectors. For agricultural products, good works are done with artificial intelligence to detect plant diseases and take precautions accordingly. In our study, a comparison was made with deep learning methods on the images of the sugar cane plant in different categories. The VGG-19 architecture, which was classified separately from the 5 pre-trained architectures AlexNet, DarkNet-53, GoogLeNet, ResNet-50, and VGG-19, reached the highest accuracy with 92.2%.