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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM57486
Volume:
10 |
3 |
Mar 2026
Pages:
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.
Crop diseases are one of the major factors that reduce agricultural productivity worldwide. Among these diseases, blight significantly affects plant health and leads to considerable yield loss. Early detection and accurate estimation of blight damage are essential for farmers to take timely preventive measures and improve crop productivity. This paper proposes an intelligent system for estimating blight disease damage and predicting crop yield using machine learning and image analysis techniques. The system analyses images of plant leaves to detect blight symptoms and estimate the severity of damage. By combining disease detection with predictive analytics, the proposed framework provides insights into potential crop yield reduction. The system aims to assist farmers in making informed decisions regarding crop management, pesticide usage, and disease prevention.
KEYWORDS
Publication Date
12/2/2026
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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No funding information available for this research.
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