Prediction of Soil Properties for Agriculture Using Ensemble Learning Techniques

Farmers can benefit from the knowledge of soil characteristics to implement efficient and sustainable agricultural practices which yield more crops with less use of resources. Using a machine learning technique, research is attempting to predict soil characteristics. Sand, pH value, Soil organic Carbon, Calcium, and Phosphorus are the primary characteristics of soil forecasts. These characteristics have a significant influence on crop yields. Six commonly used machine learning models are used for the prediction of these soil characteristics: Linear Regression, Random Forest Regression, Support Vector Machines, Gradient Boosting, XGBoost, and AdaBoost. The performance of these models is accessed by reference to the African Soil Property Prediction Dataset. The prediction performance of each model was evaluated using mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R2 score). The results suggested that among all the evaluated models, the XGBoost Regressor model was the most suitable prediction model for soil properties.