RESNET BASED DISEASE DETECTION IN WHEAT PLANTS

Wheat is one among the foremost developed crops in Asian continent. The crop is infected by totally different diseases at different stages of its development. It is exceptionally hard for the agriculturist’s to physically distinguish the infections exactly, with their limited information. Latest enhancements in Deep Learning for the Automated image recognition through utilizing Convolution Neural Networks (CNN) models are exceptionally helpful for such issues. Infected wheat leaf image datasets are collected from the web sources and then the Transfer Learning model is used to form deep learning model for detection of diseases in the wheat plants. The proposed architecture on CNN through ResNet-50 model is trained for detecting diseases and tested on the dataset collected from wheat leaf images.