Open AccessMulberry Leaf Disease Detection using Deep Learning
Author(s) -
D. Deva Hema,
Sougata Dey,
Krishabh,
Anubhav Saha
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1521.109119
Subject(s) - artificial intelligence , deep learning , computer science , segmentation , task (project management) , image segmentation , image processing , convolutional neural network , machine learning , contextual image classification , pattern recognition (psychology) , image (mathematics) , engineering , systems engineering
Disease diagnosis and classification in a mulberry plant using deep learning is an interesting technique which can be useful for farmers and researchers to identify and classify diseases. It helps to manage plant pathogens within fields effectively and automatically at a minimal cost. Major mulberry diseases usually express their symptoms on leaf area at the early stage of infection. Infections can be analysed and classified by processing the image using a computer or machine using different algorithms to interpret the information. This paper gives us a brief knowledge of mulberry leaf diseases which is used for automatic detection of disease. It presents in detail that the algorithm and techniques which are involved in classification based on different criteria for image segmentation. Our goal is to develop a more suitable deep algorithm for our task. These convolutional layers are mostly used for image processing. The system identifies and classify mulberry leaf diseases effectively with complex scenarios from the affected areas using CNN.