Plant Disease Identification Using Deep Learning
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© 2025 by IJCTT Journal
Volume-73 Issue-5
Year of Publication : 2025
Authors : P. Anil Kumar, M V A Naidu, Dyanaveni Tarun, Tandra Shravan, Ettam Rithika
DOI :  10.14445/22312803/IJCTT-V73I5P106

How to Cite?

P. Anil Kumar, M V A Naidu, Dyanaveni Tarun, Tandra Shravan, Ettam Rithika, "Plant Disease Identification Using Deep Learning," International Journal of Computer Trends and Technology, vol. 73, no. 5, pp. 41-46, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I5P106

Abstract
Agriculture is the backbone of human existence, cradling the responsibility to support human life and our global economy and provide necessities such as food, fiber and raw materials. Nevertheless, this essential industry suffers from many problems, among which crop devastation caused by plant diseases is a severe, primary concern. If not quickly detected and controlled, these diseases can devastate our crops' quantity and quality, endangering food security and farmers' livelihoods. For one, traditional methods of identifying these diseases — including visual inspections and lab tests — can be slow, labor intensive and need specialized knowledge. To overcome these tracks, in this study, this project proposes a system for plant disease diagnosis using CNN as a special machine learning approach. This strategy is intended to result in low-cost, scalable and effective disease detection at an early stage, aiding farmers and industry partners in early intervention and minimizing crop loss. The system presented here is a critical step in the right direction to increase agricultural productivity and sustainability in the face of growing environmental and economic pressures.

Keywords
Agricultural technology, Convolutional Neural Networks (CNN), Deep Learning, Image classification, Machine Learning, Plant Disease Detection.

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