Open AccessAnalysis and Implementation of Fruit/Leave Disease Detection using Image Processing and Neural Approach
Author(s) -
Prashant Richhariya and Dr. Anita Soni Krishna Madheshiya
Publication year - 2021
Publication title -
international journal of modern trends in science and technology
Language(s) - English
Resource type - Journals
ISSN - 2455-3778
DOI - 10.46501/ijmtst0709030
Subject(s) - artificial intelligence , convolutional neural network , computer science , plant disease , deep learning , artificial neural network , image processing , field (mathematics) , machine learning , convolution (computer science) , pattern recognition (psychology) , image (mathematics) , computer vision , microbiology and biotechnology , mathematics , pure mathematics , biology
The latest generation of convolution neural networks (CNNs) has achieved impressive results in the field of imageclassification. This paper is concerned with a new approach to the development of fruit/plant disease detection model, based onleaf image processing and classification, by the use of ANN. Novel way of training and the methodology used facilitate a quickand easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out ofhealthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method forplant disease recognition has been proposed for the first time. All essential steps required for implementing this diseaserecognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessedby agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to performthe deep CNN training