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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62165
Volume:
10 |
4 |
April 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Independent Researcher
Independent Researcher
Independent Researcher
Independent Researcher
Independent Researcher
Independent Researcher
Independent Researcher
Independent Researcher
Independent Researcher
Independent Researcher
Problem Statement: Plant diseases significantly reduce crop yield and quality. Traditional disease detection methods rely on manual observation, which is often slow, inaccurate, and not easily accessible to all farmers. There is a need for an automated system that can quickly and accurately detect plant diseases using technology. Solution: The Plant AI-Based Disease Detection System is a desktop web application that uses deep learning to identify plant leaf diseases. Users upload a leaf image, which is analyzed by a trained CNN model to detect the disease. The system then displays the disease name along with precautionary measures, helping users take timely action and improve crop productivity.
KEYWORDS
The Plant AI-Based Disease Detection System successfully demonstrates how Artificial Intelligence and Computer Vision can be applied to agriculture for early and accurate disease detection. By analyzing leaf images through a deep learning model, the system efficiently identifies plant diseases and provides useful precautionary measures. The desktop web application ensures accessibility and ease of use, helping farmers and researchers make informed decisions. Overall, the project contributes to improving crop health, reducing losses, and promoting the adoption of smart farming practices.
1. N. Nigar, H. M. Faisal, M. Umer, O. Oki, and J. M. Lukose, “Improving Plant Disease Classification with Deep-LearningBased Prediction Model Using Explainable Artificial Intelligence,” IEEE Access, 2024. 2. S. M. Javidan, A. Banakar, K. Rahnama, K. A. Vakilian, and Y. Ampatzidis, “Feature engineering to identify plant diseases using image processing and artificial intelligence: A comprehensive review,” Smart Agricultural Technology, Elsevier, 2024. 3. S. N. Shelke, M. P. Yadav, and S. Salve, “A Systematic Review of AI Driven Crop Disease Detection using Deep Learning: Analysis, Research Challenges and Future Trends,” IIIT Pune, 2024. 4. A. Jafar, N. Bibi, R. A. Naqvi, A. Sadeghi-Niaraki, and D. Jeong, “Revolutionizing agriculture with artificial intelligence: Plant disease detection methods, applications, and their limitations,” Frontiers in Plant Science, 2024. 5. S. Ahmed, M. B. Hasan, T. Ahmed, M. R. K. Sony, and M. H. Kabir, “Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification,” IEEE Access, 2022.
Publication Date
4/27/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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