Jute Leaf Disease Prediction Using Deep Neural Network

Abstract

Deep constitutional neural network is a diverting area where research researches and achievement are taking geometrical progress in the agriculture field. Various researches are going on vigorously in plant diseases detection. Plant contribution is highly important for human life and environment. Plants also suffer from diseases as human and animals. There are many plant diseases that occurs and affected natural growth of plant. These diseases infected complete plants including leaf, stem, root, fruit and flowers. This research propose is a diseases detection and classification technique with the help of Deep learning convolution neural network. The latest generation of constitutional neural networks (CNN's) has gained magnificent results in the field of image classification. This research is related with a new approach to the development of plant disease detection model, based on leaf image classification, by using deep constitutional neural networks (DCNN). All essential steps required for implementing this disease detection model is fully discussed throughout the report, starting from collecting images in order to generate a dataset, evaluated by agricultural experts. This research is mainly focused how to detect jute plant leaves diseases using training and testing data using Deep learning convolution neural network (DCNN), stable and data set.