Ensemble Machine Learning Approach For Agricultural Crop Selection

The importance of agricultural earnings and employment in most countries has decreased with time. That is also true for Bangladesh. Farmers usually design the cultivation process based on their previous experience. Due to a lack of precise agricultural knowledge, they probably end up farming undesirable crops. Several research has employed machine learning methods to forecast agricultural output, but only a few used ensemble machine learning approaches. We use three major crop data which are Aus rice, Aman rice and Potato from the Bangladesh Bureau of Statistics and the seven weather parametrized data from the Bangladesh Meteorological Department over 43 years. The main contribution of this research is the development of an Ensemble Machine Learning Approach (EMLA) by using Catboost Regressor and XGBoost Regressor with their novel combination of Machine Learning Algorithms on the collected dataset. The study compares the accuracy and error rate of the proposed EMLA with eight well-known machine learning algorithms. Our proposed EMLA achieved a high degree of accuracy with R-squared scores of 88.084%,91.776% and 90% respectively for Aus rice, Aman rice and Potato. The results show that the EMLA technique improves the output and prediction by relying on the strong performance of another model. The primary goal of this research is to improve the predictability for overcoming food difficulties and create an intelligent information prediction analysis on farming in Bangladesh for efficient and profitable farming decisions. In this research, we proposed our Ensemble Machine Learning Approach for agricultural crop selection and yield prediction.