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Paper ID:10.55041/IJSREM.RACCES008

AgriMindEdge: An Explainable Edge AI-Based Mobile System

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM.RACCES008

Volume:

10 |

4 |

April 2026

Pages:

1--

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem.RACCES008

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.

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Authors

NA

Nikitha A.

Department of Artificial Intelligence & Data Science,

AVS Engineering college , Salem, Tamil Nadu, India

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DSS

Dinak Saju S.

Department of Artificial Intelligence & Data Science,

AVS Engineering college , Salem, Tamil Nadu, India

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NG

Nandarasi G.

Department of Artificial Intelligence & Data Science,

AVS Engineering college , Salem, Tamil Nadu, India

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Mentors

MNS

Mrs. N. Savitha

Department of Artificial Intelligence & Data Science,

AVS Engineering college , Salem, Tamil Nadu, India

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Abstract

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

Keywords: Edge AI; CNN; TensorFlow Lite; Flutter; Score-CAM; Crop Disease Detection; Soil Classification; Android.

Cite This Article

Nikitha A., Dinak Saju S., Nandarasi G., Mrs. N. Savitha, "AgriMindEdge: An Explainable Edge AI-Based Mobile System," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1--, April 2026. https://doi.org/10.55041/ijsrem.RACCES008

Full Article/Conclusion

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.

References

[1] FAO, "The State of Food and Agriculture 2023," FAO, Rome, Italy, 2023. [2] R. N. Strange and P. R. Scott, "Plant disease: A threat to global food security," Annual Review of Phytopathology, vol. 43, pp. 83–116, 2005. [3] S. P. Mohanty, D. P. Hughes, and M. Salathé, "Using deep learning for image-based plant disease detection," Frontiers in Plant Science, vol. 7, p. 1419, 2016. [4] K. P. Ferentinos, "Deep learning models for plant disease detection and diagnosis," Computers and Electronics in Agriculture, vol. 145, pp. 311–318, 2018. [5] S. Sladojevic et al., "Deep neural networks based recognition of plant diseases by leaf image classification," Computational Intelligence and Neuroscience, 2016. [6] W. Shi et al., "Edge computing: Vision and challenges," IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, 2016. [7] J. G. A. Barbedo, "Factors influencing the use of deep learning for plant disease recognition," Biosystems Engineering, vol. 172, pp. 84–91, 2018. [8] S. Ramesh and R. Vardhan, "Weather parameter integration for disease prediction," International Journal of Agricultural Information Systems, 2015. [9] R. Gopalan et al., "Lightweight CNN architectures for mobile edge deployment," Journal of Mobile Computing, vol. 12, no. 3, pp. 145–159, 2025. [10] A. Gani et al., "Multi-modal image fusion for plant disease classification," Computers and Electronics in Agriculture, 2025. [11] P. Upadhyay et al., "Weather-aware deep learning for crop disease prediction," Smart Agricultural Technology, 2025.

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Publication Date

5/2/2026

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Double-Blind Peer Review

Review Rounds: 2

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