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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63487
Volume:
10 |
5 |
May 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 Computer Engineering
Sinhgad Academy of Engineering
Department of Computer Engineering
Sinhgad Academy of Engineering
Department of Computer Engineering
Sinhgad Academy of Engineering
Department of Computer Engineering
Sinhgad Academy of Engineering
Department of Computer Engineering
Sinhgad Academy of Engineering
The agriculture sector is the backbone of India’s economy, yet many farmers face challenges in crop selection, soil management, and timely planning. Inadequate knowledge about soil fertility, improper crop selection, and lack of structured farming schedules often result in reduced yield and economic loss. This paper proposes AI-Based Farmer Guidance and Agricultural Support System designed to provide an intelligent solution for modern farming. The system predicts suitable crops based on the farmer’s soil fertility report, offers a Smart Crop Calendar to guide farmers through each stage from sowing to export, and integrates a Farmer Community Platform for experience sharing and collaboration. By combining Artificial Intelligence (AI), Machine Learning (ML), and data-driven agriculture, This Project aims to enhance productivity, optimize crop management, and empower farmers with digital tools for sustainable growth.
KEYWORDS
This paper presents an innovative overview of transforming traditional agriculture into a data-driven, intelligent system. By combining AI-based crop prediction, smart scheduling, and community support, it empowers farmers to make accurate decisions and achieve higher productivity. The system encourages sustainable farming by optimizing resource usage and reducing risks linked to guesswork.
[1] P. Kamble, V. Kapse, S. Khandekar, M. Mali, D. Meher, and M. Mahindrakar, “An Investigation on Smart Agriculture System Using AI,” International Journal of Research Publication and Reviews, vol. 6, no. 3, pp. 6452–6456, Mar. 2025. [2] A. Sattigeri, A. S., M. Faisal, S. A., and M. S., “AI Based Crop Recommendation System in Karnataka,” International Journal of Creative Research Thoughts (IJCRT), vol. 13, no. 7, pp. 719–721, Jul. 2025. [3] A. Katharria, K. Rajwar, M. Pant, J. D. Velásquez, V. Snášel, and K. Deep, “Information Fusion in Smart Agriculture: Machine Learning Applications and Future Research Directions,” arXiv preprint arXiv:2405.17465v2, Mar. 2025. [4] S. K. K. V. and Y. G. B., “Krushiseva: A One-Stop Agricultural Support Platform for Farmers Using AI,” International Journal of Research Publication and Reviews, vol. 6, no. 8, pp. 511–519, Aug. 2025. [5] A. R. Onteddu, R. R. Kundavaram, A. Kamisetty, J. C. S. Gummadi, and A. Manikyala, “Enhancing Agricultural Efficiency with Robotics and AI-Powered Autonomous Farming Systems,” Malaysian Journal of Medical and Biological Research, vol. 12, no. 1, pp. 7–22, Feb. 2025. [6] M. M. Khalid and O. Karan, “Deep Learning for Plant Disease Detection,” International Journal of Mathematics, Statistics, and Computer Science, vol. 2, 2024.
Publication Date
5/21/2026
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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