Soil Fertility Assessment Using Ensemble Methods in Machine Learning

This research work focuses on the development and comparison of soil fertility classification models based on Ensemble methods. This study utilizes machine learning based algorithms for analyzing and assessing the soil fertility levels, a vital feature in sustainable agriculture. The models are developed using three different Ensemble methods in MATLAB. The ensemble learning approach has ability of handling complex datasets. It provides very accurate anticipations. The research considers three factors viz. pH, Nitrogen concentration, and Phosphorous concentration for soil fertility classification. This work offers valuable visions into effectiveness of Ensemble techniques in soil fertility classification, providing a practical approach to farmers & agriculture practitioners for making informed decisions pertaining to soil management practices.