Open AccessPlant Disease Detection and Classification using CNN
Author(s) -
R Rinu,
S H Manjula
Publication year - 2021
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c6458.0910321
Subject(s) - computer science , convolutional neural network , artificial intelligence , machine learning , segmentation , feature extraction , field (mathematics) , artificial neural network , feature (linguistics) , deep learning , contextual image classification , pattern recognition (psychology) , plant disease , class (philosophy) , image processing , image (mathematics) , mathematics , microbiology and biotechnology , linguistics , philosophy , pure mathematics , biology
Agriculture is one field which has a high impact on life and economic status of human beings. Improper management leads to loss in agricultural products. Diseases are detrimental to the plant’s health which in turn affects its growth. To ensure minimal loss to the cultivated crop, it is crucial to supervise its growth. Convolutional Neural Network is a class of Deep learning used majorly for image classification, other mainstream tasks such as image segmentation and signal processing. The main aim of the proposed work is to find a solution to the problem of 38 different classes of plant diseases detection using the simplest approach while making use of minimal computing resources to achieve better results compared to the traditional models. VGG16 training model is deployed for detection and classification of plant diseases. Neural network models employ automatic feature extraction to aid in the classification of the input image into respective disease classes. This proposed system has achieved an average accuracy of 94.8% indicating the feasibility of the neural network approach even under unfavorable conditions.