Open AccessAn Integrated Deep Learning Framework of Tomato Leaf Disease Detection
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.k1010.09811s19
Subject(s) - artificial intelligence , computer science , deep learning , machine learning , support vector machine , variety (cybernetics) , agriculture , feature extraction , plant disease , tree (set theory) , feature (linguistics) , pattern recognition (psychology) , microbiology and biotechnology , biology , mathematics , ecology , mathematical analysis , linguistics , philosophy
Plants are a very important part of human life. They have variety of use as food, medication, raw materials and maintaining a balanced ecosystem. Plant disease is a deterioration of normal state of plant that interrupts and modifies its functionality. Pathogens are the main cause of such diseases. For agricultural purposes, a variety of methods have been proposed to detect plant diseases in the recent technological era. However, detecting plant diseases with high accuracy is still a challenge in computer vision. In this study, we propose an integrated deep learning framework where a pre-trained VGG-19 model is used for feature extraction and stacking ensemble model is used to detect and classify leaf diseases from images so as to reduce production and economic loses in agriculture sector. A dataset consisting of two classes (Infected and Healthy) and a total of 3242 images was used to test the system. Our proposed work has been compared with other contemporary algorithms (kNN, SVM, RF and Tree) and have outperformed by obtaining an accuracy of 98.6%