Identifying and Categorizing Plant Diseases Through Deep Learning Techniques - A Survey

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A.Thamaraiselvi; S. Lesa; C. poongodi

Identifying and Categorizing Plant Diseases Through Deep Learning Techniques - A Survey

Authors: A.Thamaraiselvi, S. Lesa, C. poongodi

Unique Paper ID: 171254
Volume: 11
Issue: 7
PageNo: 3794-3800

Keywords: Plant Disease Deep Learning CNN Transfer Learning Image Segmentation Feature Extraction.

Abstract:
Identifying and classifying plant diseases is important to maintain healthy crops and increase agricultural productivity. Early detection and identification of plant diseases from leaf images using deep learning is an important and challenging aspect of agricultural research. There is a need for such research in India because agriculture is one of the major sources of income, accounting for 17% of the gross domestic product (GDP). A good harvest and increased yield can contribute to farmers and the national economy. Traditional diagnostic methods rely on manual inspection and laboratory tests, which are time-consuming and labor-intensive. With the rapid development of deep learning, especially convolutional neural networks (CNN), the ability to improve and refine diagnostic procedures has increased. This study investigates the use of deep learning technology in the identification and classification of leaf diseases. Deep learning models are used to analyze image data in large pages and identify some visual patterns to accurately classify various diseases. The paper also discusses the problems encountered when training deep learning models, such as inconsistent datasets and overloading, and proposes ways to solve these problems. This work demonstrates the potential of deep learning as a powerful tool for monitoring and controlling agricultural diseases and provides an excellent solution for disease detection and control.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 7
PageNo: 3794-3800

Identifying and Categorizing Plant Diseases Through Deep Learning Techniques - A Survey

Available:https://ijirt.org/article?manuscript=171254

27 Mar, 2025

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