Open AccessPlant Disease Detection Using Deep Learning
Author(s) -
Prof. Ksn Sushma,
Nishant Upadhyay,
Abhishek Singh,
Prasenjeet Kr. Singh,
Tanzeelah Firdaus
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.41451
Subject(s) - deep learning , computer science , artificial intelligence , categorization , convolutional neural network , variety (cybernetics) , machine learning , plant disease , visualization , microbiology and biotechnology , biology
Early diagnosis of plant diseases is critical since they have a substantial impact on the growth of their unique species. Many Machine Learning (ML) models have been used to detect and categorize plant diseases, but recent breakthroughs in a subset of ML called Deep Learning (DL) look to hold a lot of promise in terms of improved accuracy. A variety of developed/modified DL architectures, as well as several visualization techniques, are utilized to recognize and identify the symptoms of plant ailments. In addition, a number of performance measurements are used to evaluate various architectures/techniques. This article explains how to use DL models to display a variety of plant diseases. Furthermore, several research gaps are identified, allowing for improved efficiency in detecting plant illnesses even before issues emerge. Keywords: Plant disease; deep learning; convolutional neural networks (CNN), Google Net Architecture, Tensorflow, and PyTorch are some of the tools that can be used;