Application of stacking ensemble technique in agriculture for prediction of crop yield

Abstract

In comparison to other methodologies, Machine Learning has proven to be an effective tool for making reasonable predictions. Initially, a single machine learning model is used on data to make predictions; however, instead of using a single machine learning model, a collection of ML models is employed instead. Models can reduce prediction variance, bias, or both. The result provided will be more appealing and correct if appropriate features affecting the prediction values are supplied. This study presents a framework for selecting feature subsets using the L2 regularization feature selection technique. The study took into account 11 crops and 15 characteristics from 300 districts across India. For predicting yield, basic learners' models such as Linear Regression, LASSO Regression, Decision Trees, Random Forests, Extreme Gradient Boosting (XGBoost), and Support Vector Machines are initially trained and tested. Base learners are used to create the stacking ensemble and the proposed optimum stacking ensemble. In terms of the performance measures employed, the proposed Optimized stacking ensemble shows approximately (MSE =6408, Accuracy=85%) outperforms the Normal stacking ensemble (MSE=8315, Accuracy=83%).