Paper Key : IRJ************990
Author: Mr.vikki Nakhate,Prof.poorva Wagh,Mr.yash Bandewar,Mr. Gaurav Khandale,Mr.mohit Karemore
Date Published: 11 Nov 2024
Abstract
During the growth season, several illnesses can affect plants. One of the most significant issues in agriculture is the early diagnosis of plant diseases. Diseases can diminish total yields and farmersincome if they are not identified early enough. Reducing plant diseases and enhancing the quality and yield of food crops can both benefit from early and accurate analysis and identification of plant diseases. Use CNN devised a methodology. This work makes use of 2560 original image dataset and 82,592 augmented image. CNN models have a classification accuracy of 90.07% to 95.3% and can automatically learn features from raw photos. Convolutional neural networks (CNN) and other deep learning techniques can be used to detect plant illnesses.Keywords: Convolutional Neural Networks (CNNs); Confusion Matrix; Deep Learning; Machine Learning, Disease prediction, Data augmentation, Model comparison.