Open AccessAutomated Quality Assessment of Crops Using CNN - Keras
Author(s) -
Meghashree,
Alwyn Edison Mendonca,
Ashika S Shetty
Publication year - 2021
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit2173186
Subject(s) - convolutional neural network , agriculture , artificial intelligence , preprocessor , computer science , production (economics) , quality (philosophy) , agricultural engineering , plant disease , deep learning , food security , feature extraction , machine learning , microbiology and biotechnology , engineering , geography , biology , philosophy , archaeology , epistemology , economics , macroeconomics
Plant disease is an on-going challenge for the farmers and it has been one of the major threats to the income and the food security. This project is used to classify plant leaf into diseased and healthy leaf,to improve the quality and quantity of agricultural production in the country. The innovative technology that helps in improve the quality and quantity in the agricultural field is the smart farming system. It represented the modern method that provides cost-effective disease detection and deep learning with convolutional neural networks (CNNs) has achieved large successfulness in the categorisation of different plant leaf diseases. CNN reads a really very larger picture in a simple way. CNN nearly utilised to examine visual imagery and are frequently working behind the scenes in image classification. To extract the general features and then classify them under multiple based upon the features detected. This project will help the farmers financially in increasing the production of the crop yield as well as the overall agricultural production. The paper reviews the expected methods of plant leaf disease detection system that facilitates the advancement in agriculture. It includes various phases such as image preprocessing, image classification, feature extraction and detecting healthy or diseased.