Leveraging Machine Learning for Enhanced Agricultural Productivity: A Crop Recommendation System Approach

Abstract

Agriculture is fundamental to global food security, but challenges such as climate change, soil degradation, and fluctuating market prices hinder productivity. The integration of Machine Learning (ML) into agricultural practices has shown promising results in improving crop yield predictions and resource optimization. This paper explores a novel machine learning-based crop recommendation system that tailors crop choices based on various environmental, economic, and agricultural factors. By utilizing ML algorithms, this system can offer precise recommendations that help farmers make informed decisions, ultimately increasing agricultural productivity and sustainability. The proposed method promises to revolutionize crop selection, enabling better utilization of available resources and enhancing food security in a changing climate.