Classification of potato and corn leaf diseases using deep learning

Abstract

One of the major hindrances to sustainable agriculture and an imminent threat to food security is plant disease. Constantly monitoring a plant’s health and spotting the problems in it is quite painstaking because it demands a lot of work, human resources for visualization, and knowledge of plant diseases. However, deep learning can be extremely useful in the early diagnosis of plant disease, which will minimize productivity loss and help to achieve the objective of sustainable agriculture. In this study, we will use image processing of the leaves to detect plant illness using a vision-based automatic method that uses deep learning models for disease classification such as ResNet50,Densenet121, VGG-16, Inception V3 and Vision Transformers. These techniques are plant image based algorithms.