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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63530
Volume:
10 |
5 |
May 2026
Pages:
1-4
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.
Master Of Computer Application's
Bapuji Institute Of Engineering And Technology
Master Of Computer Application's
Bapuji Institute Of Engineering And Technology
The agricultural sector is currently experiencing a profound technological shift yet the fundamental process of assessing soil health and predicting crop yield remains largely manual fragmented and heavily dependent on experience rather than data-driven precision. It often forces farmers to rely on multiple disconnected methods: laboratory soil tests weather advisories historical yield records and manual field inspections making delayed decisions inconsistent accuracy and resource mismanagement. This paper presents a novel AI-based Soil and Yield Prediction System to be used for changing Modern farming by unifying the fragmentation of soil analysis crop recommendation and yield forecasting into a single intelligent decision-support ecosystem. This system is powered at the core with an integrated powerful machine learning/deep learning and IoT-based engine.sensing and remote satellite analytics.Unlike the questionnaires and other conventional assessment tools this system does not provide static and general insights but rather functions like a Dynamic hyper-personalized Agricultural advisor By processing large datasets that include soil nutrient profiles moisture levels pH values and historical crop The system produces precise predictions for yields real-time weather fluctuations and other environmental stress factors.It must be planned according to the land particulars of each farmer and the goals for cultivation.The platform identifies nutrient deficiencies and soil constraints but also prescribes optimized fertilizer.Usage irrigation schedules and apt varieties of crops according to the season and region.Beyond this the system will be integrated with autonomous decision intelligence to guide farming.activities throughout the crop lifecycle.t continuously monitors weather variable conditions adjusting recommendations in real time to counter the weather.anomalies pest threats or soil degradation patterns.This project democratizes access to high-level precision-farming technology through the provision of an affordableIt is a scalable data-driven solution to enhance agriculture productivity and reduction of input wastages promoting farming in such a way that is sustainable.The result is an integrated platform whereby conventional farming is turned into a scientifically optimized process.This establishes a new yardstick towards the future of smart agriculture.
KEYWORDS
The AI-Based Soil and Yield Prediction System marks a definitive shift toward data-driven agriculture by unifying IoT telemetry and deep learning into a singular decision-support framework. By translating complex soil-health parameters into site-specific prescriptions, the platform mitigates historical information lags and democratizes access to precision farming for both smallholders and enterprise operations. Ultimately, this system functions as a resilience engine that empowers the agricultural workforce to transform environmental uncertainties into managed, scientifically optimized processes for long-term land stewardship. Future enhancements will transition the platform into a proactive, decentralized intelligence hub through the integration of autonomous UAVs and multispectral analytics for high-resolution stress mapping. The roadmap includes the deployment of blockchain-enabled ledgers to ensure tamper-proof provenance and the implementation of voice-driven natural language interfaces for hands-free field interaction. By incorporating swarm robotics and market intelligence forecasting, the evolution of the system will move beyond advisory support to active field orchestration and economic optimization.
[1] V. C. Patil, N. Tandon, and K. D. Kokate, “Precision Farming: Soil Health Monitoring and Crop Yield Prediction Using Machine Learning Techniques,” International Journal of Agricultural Sciences, vol. 13, no. 4, pp. 112– 120, 2021. [2] C. Zhang and J. M. Kovacs, “The Application of UAV-Based Remote Sensing in Precision Agriculture,” Remote Sensing, vol. 4, no. 4, pp. 2726–2752, 2012. [3] R. Bisht, S. Kumar, and A. Pandey, “Soil Nutrient Analysis and Crop Recommendation Using Machine Learning Algorithms,” in Proceedings of the IEEE International Conference on Computing Power & Communication Technologies (GUCON), pp. 585–590, 2020. [4] K. Adhikari and A. E. Hartemink, “Soil Science and Digital Soil Mapping: Advancements and Use in Agricultural Decision-Making,” Geoderma, vol. 270, pp. 99–112, 2016. [5] Food and Agriculture Organization (FAO), “Climate-Smart Agriculture and Digital Tools for Sustainable Farming,” Rome, Italy: FAO Publications, 2019. [6] Y. Huang, Y. Lan, W. C. Hoffmann, and R. E. Lacey, “Agricultural Remote Sensing, Big Data, and Machine Learning for Crop Monitoring,” Computers and Electronics in Agriculture, vol. 70, no. 1, pp. 33–45, 2010.
Publication Date
5/20/2026
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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