Agriculture Resources for Plant-Leaf Disease Identification using Deep Learning Techniques

Hema, L K and Vijendra Babu, D. and Navaneetharajan, A. and Vijayakumar, K. and Dhayanithi, S. (2021) Agriculture Resources for Plant-Leaf Disease Identification using Deep Learning Techniques. Journal of Physics: Conference Series, 1964 (6). 062027. ISSN 1742-6588

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Abstract

Agriculture is an essential food supply. In developing countries like India, agriculture provides farmers with large-scale livelihood opportunities. The recent advances in computer vision brought on by in-depth learning have paved the way for detecting and diagnosing plant diseases by using a camera to take pictures. This study is an important means of distinguishing different diseases in various plant species. The system has been developed to detect and classify many plant varieties, including apples, wheat, grapes, potatoes, sugar cane and tomatoes. The computer is also able to diagnose a host of herbal diseases. The experts were able to create profound learning models that identified and differentiated plant diseases and non-attention of ailments with 25000 images of infected sound plant leaves and disease. The model produced was 95,3 percent accurate, and the gadget was able to report the accuracy up to 100 percent to classify and differentiate between the plant variety and the types of diseases that were infected by the plant. © 2021 Elsevier B.V., All rights reserved.

Item Type: Article
Subjects: Agricultural and Biological Sciences > Agricultural Sciences
Divisions: Engineering and Technology > Aarupadai Veedu Institute of Technology, Chennai > Electronics & Communication Engineering
Depositing User: Unnamed user with email techsupport@mosys.org
Last Modified: 03 Dec 2025 12:09
URI: https://vmuir.mosys.org/id/eprint/3155

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