Agriculture has traditionally relied on farmers’ experiential knowledge to select crops based on seasonal patterns and regional familiarity. Yet, as climate conditions become more unpredictable, soil degradation, and the growing global demand for food, such traditional methods are no longer sufficient. The Nonexistence of timely, data-driven decision-making tools often results in poor crop selection, suboptimal yields, and inefficient resource utilization, particularly in resource-constrained regions. This paper proposes a ML-based CRS designed to analyze essential agricultural parameters such as soil nutrients (nitrogen, phosphorus, potassium), pH level, rainfall, and temperature to identify the most suitable crop for a given environment. The system utilizes supervised learning algorithms including RF, Decision Trees, and SVM, trained on comprehensive historical datasets containing crop yields and environmental profiles. A lightweight web-based interface enables users to input their soil data and receive real-time, region-specific crop recommendations. Experimental evaluation demonstrates high predictive accuracy, with the RF algorithm consistently outperforming others in terms of generalization and reliability across diverse agricultural zones. The proposed solution aids in mitigating crop mismatch risks, promotes sustainable land use, and enhances agricultural productivity through intelligent, data-driven support. The system is scalable and adaptable to make it usable on a larger scale in different areas agro-climatic regions
Girish Kumar D (Fri,) studied this question.