Data-Driven Crop Recommendation System for Indian Agriculture: Leveraging Machine Learning for Sustainable Development

Agriculture is a critical sector for many of India's population, contributing significantly to the national economy. However, many farmers rely on traditional practices rather than data-driven insights for crop selection, which can lead to adverse financial and psychological outcomes. The study develops an ensemble of Machine Learning (ML) models that analyze multiple environmental variables to provide evidence-based crop recommendations to farmers. By integrating features such as rainfall, climate, humidity, soil nutrients, and sowing seasons, the proposed ML model ensemble is designed to recommend crops suited to specific farm conditions. The methodology leverages advanced algorithms to process a comprehensive dataset, including critical parameters like soil properties and climate. Results indicate that this data-driven approach provides accurate and tailored crop recommendations, supporting Precision Agriculture (PA). Adopting such technology promises to transform traditional farming practices, reduce risks, and promote sustainable agricultural development for Indian farmers.