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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64207
Volume:
10 |
5 |
May 2026
Pages:
1-3
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.
Department Of Computer Science & Engineering
Vidya Vikas Pratishthan Institute Of Engineering & Technology Solapur
Department Of Computer Science & Engineering
Vidya Vikas Pratishthan Institute Of Engineering & Technology Solapur
Department Of Computer Science & Engineering
Vidya Vikas Pratishthan Institute Of Engineering & Technology Solapur
Department Of Computer Science & Engineering
Vidya Vikas Pratishthan Institute Of Engineering & Technology Solapur
Crop disease detection and prediction play a significant role in improving agricultural productivity and reducing crop losses. This study presents an intelligent system for early identification and prediction of crop diseases using image processing and machine learning techniques. The proposed approach collects crop leaf images, performs preprocessing operations such as noise removal, image enhancement, and feature extraction, and applies classification algorithms to identify disease patterns. Deep learning models are utilized to improve detection accuracy and predict disease occurrence based on historical and visual data patterns. Experimental results demonstrate high prediction accuracy and effective disease classification across multiple crop categories. The developed system enables early diagnosis, reduces dependency on manual inspection, minimizes economic losses, and assists farmers in making timely decisions regarding crop management. The findings indicate that the proposed model can contribute significantly to precision agriculture by providing a reliable, efficient, and automated solution for crop disease monitoring and prediction.
KEYWORDS
This project on crop disease detection and prediction successfully demonstrates the use of modern technologies to identify plant diseases accurately and efficiently. By applying image processing and machine learning techniques, the system supports early diagnosis, improves crop management, reduces losses, and helps farmers make timely decisions, ultimately increasing agricultural productivity and sustainability.
[1] D. P. Hughes and M. Salathe , “An open access repository of images on plant health to enable the development of mobile disease diagnostics,” arXiv preprint arXiv:1511.08060, 2015. [2] A. Kamilaris and F. X. Prenafeta-Boldu , “Deep Learning in Agriculture: A Survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70–90, 2018. [3] S. S. Harakannanavar, J. M. Rudagi, V. I. Puranikmath, A. Siddiqua, and R. Pramodhini, “Plant Leaf Disease Detection Using Computer Vision and Machine Learning Algorithms,” Global Transitions Proceedings, vol. 3, no. 1, pp. 305–310, 2022. [4] S. M. Hassan, A. K. Maji, M. Jasiński, Z. Leonowicz, and E. Jasińska, “Identification of Plant-Leaf Diseases Using CNN and Transfer Learning Approach,” Electronics, vol. 10, no. 12, 2021.
Publication Date
5/28/2026
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.
Funding Information
No funding information available for this research.
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