Open AccessImage Processing based Plant Disease Detection using CNN
Author(s) -
Rachana Sable
Publication year - 2021
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.2021.35040
Subject(s) - computer science , field (mathematics) , process (computing) , plant disease , productivity , image processing , artificial intelligence , agriculture , population , machine learning , image (mathematics) , pattern recognition (psychology) , geography , microbiology and biotechnology , mathematics , environmental health , medicine , macroeconomics , archaeology , pure mathematics , economics , biology , operating system
In the era of Scientific Development, many technologies and new ways of solving real-life problems are being invented every day. The basic need of the food is increasing parallelly, due to an increase in population. According to FAO of the UN, annually 20 to 40 percent of crops were lost due to diseases. That’s why technological development in the agricultural field is important to improve the productivity of crops. This major issue can be overcome by implementing disease detection techniques to identify the disease from an input image. This process involves steps like dataset collection, image pre-processing and training a classification model. The dataset consists of plants like cotton, grapes and tomatoes. CNN classification model is used for disease classification. The proposed model gives an accuracy of 96%. After disease classification it also provides information like causes, symptoms, management and diagnostic solutions.