An Enhanced Light GBM Model with Data Analytical Approach for Crop Recommendation

The economy of our country depends on crop cultivation. In India, agriculture serves as the backbone of a growing financial system, so maintaining this financial development is crucial. It makes a significant contribution to the global economic and agricultural prosperity of all nations. Unpredictable weather conditions and soil parameters are frequently the cause of low crop productivity. The main objective is to suggest an ML (machine learning) based agriculture system that can assist farmers regarding crops that can be harvested with specific values of soil and environmental parameters. Through several benchmark tests, LightGBM demonstrates improved performance in terms of prediction accuracy, model stability, and computing efficiency. This paper also evaluates the elements necessary to guarantee the optimal functioning of the crop recommendation system.