Enhancing Fertilizer Prediction: A Comprehensive Analysis with Grid Search CV and Multiple Machine Learning Algorithms

Agriculture has held immense historical and economic significance in India for millennia. Despite encountering challenges such as climate fluctuations, soil degradation, and limited water resources, agriculture continues to make substantial contributions to India's economy and way of life. To bolster these contributions, the agricultural sector is increasingly reliant on modern technology. The research presents and implements a soil parameter-based system for predicting optimal fertilizer usage. Through the utilization of machine learning algorithms coupled with the Grid Search CV technique applied to soil data, tailored fertilizer recommendations are generated for specific crops. The aim of this research is to create a predictive model capable of anticipating future fertilizer requirements, thereby enhancing crop yields. The proposed approach aims to elevate the efficiency of agricultural practices.