AGRISMART: Revolutionizing with AI-Driven Smart Systems

Abstract

This project focuses on developing an intelligent farming system powered by Artificial Intelligence (AI) and Machine Learning (ML), without relying on physical sensors. Instead, it leverages existing agricultural data sources—including weather patterns, crop varieties, soil characteristics, and fertilizer application—to forecast crop yields, suggest the most appropriate crops, and enhance decision-making in agriculture. The primary objective is to boost farm productivity, minimize resource wastage, and promote a more data-driven approach to farming.

Introduction

Agriculture is crucial for food supply and economic growth, yet traditional farming faces challenges like low productivity and climate variability. This project, AgriSmart, uses Artificial Intelligence (AI) and Machine Learning (ML) to provide farmers with smart, data-driven advice on crop selection, yield estimation, and fertilizer use—without relying on physical sensors.

The system collects historical and publicly available agricultural data, focusing on key features like soil nutrients (N, P, K), temperature, humidity, soil pH, and rainfall. Data preprocessing ensures quality and accuracy, while a Random Forest model is trained and optimized to predict the most suitable crops for given conditions with over 98% accuracy.

AgriSmart includes modules for user-friendly prediction, providing instant crop recommendations and explanations to build trust and transparency. The clean, responsive web interface facilitates easy data input and clear output display.

Conclusion

The AI and ML-powered Smart Farming System provides an effective solution to assist farmers without the need for costly sensors. Leveraging data analysis, it delivers precise advice on crops, forecasts yields, and recommends fertilizers. This method lowers expenses, enhances efficiency, and introduces advanced technology to conventional farming, promoting sustainable agriculture while using minimal resources.

References

[1] IEEE Xplore – Academic papers focusing on artificial intelligence applications in agriculture and crop forecasting. [2] Kaggle Datasets – Collections of data related to crop suggestions, soil characteristics, and weather conditions. [3] Scikit-learn Documentation – Resources and tutorials for implementing machine learning models. [4] Government Agricultural Portals – Reports on soil quality, climate data, and crop production statistics. [5] Google Scholar – Research articles covering sensor-free smart farming technologies. [6] Medium & GitHub – Practical AI farming initiatives and publicly available source code.

Copyright

Copyright © 2025 Angelin Rosy M, Chandru Aadithya V. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.