A Deep CNN Model Approach for The Early Detection of Plant Diseases In Android

Abstract

A deep CNN model approach for the early detection of plant diseases is to detect the plant diseases in advance and to detect the diseases with the help of modern computer technology. Automatic plant disease detection provides benefits in monitoring the large crop fields and helps in detecting the symptoms of the disease when they are found on the leaves. In this paper, the primarily focus on finding the plant diseases and which will reduce the crop loss and hence increases the production efficiency. The dataset used here consists of several varieties of plants of both affected and healthy, and all these images are collected from various freely available sources and manually. Deep learning with convolutional neural networks has achieved great success in the classification of various plant diseases. In this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset. The database obtained is properly segregated and the different plant species are identified and are renamed to form a proper database then obtain test-database which consists of various plant diseases that are used for checking the accuracy and confidence level of the project. Then using training data we will train our classifier and then output will be predicted with optimum accuracy. We use Convolution Neural Network (CNN) which comprises of different layers which are used for prediction. The dataset used here consists of several varieties of plants of both affected and healthy, and all these images are collected from various freely available sources and manually. A new CNN model was trained and tested. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks.