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Stream/Area:
-
Subject:
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Paper ID:
10.55041/IJSREM.RACCES008
Volume:
10 |
4 |
April 2026
Pages:
1--
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 Artificial Intelligence & Data Science,
AVS Engineering college , Salem, Tamil Nadu, India
Department of Artificial Intelligence & Data Science,
AVS Engineering college , Salem, Tamil Nadu, India
Department of Artificial Intelligence & Data Science,
AVS Engineering college , Salem, Tamil Nadu, India
Department of Artificial Intelligence & Data Science,
AVS Engineering college , Salem, Tamil Nadu, India
Every year, Indian farmers lose a massive share of their harvest — not because of bad farming, but because diseases on their crops are caught too late. Estimates suggest that 20 to 40 percent of annual yield is lost this way, and for small farmers, that loss can be devastating. Most AI tools built to help with this problem require a working internet connection, which simply does not exist in many villages across India. This paper presents AgriMindEdge, a mobile app built with Flutter that detects plant diseases and identifies soil types directly on the phone — no internet needed, ever. The system identifies 38 plant disease classes at 95.8% accuracy and four soil types at 92.4% accuracy, and it does all of this in under 87 milliseconds. What makes this app different is a built-in Score-CAM module that shows the farmer exactly which part of the leaf the AI looked at when making its decision. The model itself is only 8.3 MB and runs on any basic Android phone. AgriMindEdge was built for real farmers, in real fields, with real constraints in mind.
KEYWORDS
AgriMindEdge set out to answer one question: can a farmer in a village with no internet get AI-powered crop disease diagnosis on their phone? The answer is yes. The system achieves 95.8% accuracy for plant disease detection and 92.4% for soil classification, with results in 87 milliseconds, running entirely offline on a standard Android device. The Score-CAM explainability module ensures the farmer does not have to take the AI's word for it — they can see exactly what the model looked at. Live testing on real field photographs confirmed that these numbers hold outside the lab. The broader point is this: the gap between what AI can do in a research paper and what actually reaches a farmer in rural India is enormous. AgriMindEdge is one attempt to close that gap — not by building the most sophisticated model possible, but by building one that works under the actual constraints farmers face. No internet. Basic phone. No technical background. It works.
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Publication Date
5/2/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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