Abstract: Agriculture remains the foundational pillar of the Indian economy, ensuring both food security and rural livelihood for millions; however, the sector faces significant hurdles as farmers often lack the data-driven guidance necessary for optimal crop selection. This disconnect frequently results in suboptimal yields, resource wastage, and long-term soil degradation. To address these challenges, this paper presents the Smart Crop Recommendation System (SCRS), a comprehensive precision agriculture framework designed to modernize traditional decision-making. At the heart of the SCRS is a robust Random Forest classification algorithm, chosen for its superior ability to handle the non-linear complexities of agricultural datasets. The model is trained on a high-dimensional feature set comprising critical soil parameters—nitrogen (N), phosphorus (P), potassium (K), and pH levels—alongside environmental variables such as temperature, humidity, and rainfall. A key innovation of this system is its departure from static historical models through the integration of the OpenWeatherMap API. This allows the SCRS to fetch real-time meteorological data, enabling the system to provide dynamic, hyper-localized recommendations tailored to the specific micro-climates of districts in Tamil Nadu, including Coimbatore, Erode, and Salem. To bridge the gap between complex machine learning and field application, the system is deployed via a Flask-based web dashboard. This user interface provides an intuitive experience where farmers and agronomists can access not only crop suitability predictions but also targeted fertilizer advice and historical weather visualizations. Experimental evaluations demonstrate that the Random Forest model achieves high classification accuracy and F1-scores, validating its reliability for real-world deployment. Ultimately, this research offers a scalable, intelligent decision-support tool that empowers stakeholders to transition toward resource-efficient, high-yield cultivation practices.
Keywords— Crop Recommendation, Random Forest, Machine Learning, Precision Agriculture, Weather Integration, Flask, Tamil Nadu, Soil Analysis, Fertilizer Recommendation

Downloads: | DOI: 10.17148/IJIREEICE.2026.14440