Predicting Crop Yield Based on Stacking Ensemble Model in Machine Learning

This paper explores and implies on a variety of machine Learning (ML) techniques to estimate the crop yield based on various factors. An ensemble machine learning model called stacking is utilized taking weak learners as the base model and a model is created. The most common ML algorithms utilized for crop yield prediction are Support Vector machines (SVM), Random trees (RT), Linear Regression (LR), K Nearest Neighbour (KNN), Random Forest (RF), Multiple Regression (MR), Support Vector Regression (SVR), Artificial Neural Networks (ANN) and Deep Neural networks (DNN). Of these in this paper, Multiple Regression, K Nearest Neighbour (KNN), Support vector Regressor (SVR), and Random Forest Regressor (RF) were implemented as base learners with Stacking as the ensemble technique. LASSO regression (LR) is used as the meta learner and is used for making the final predictions.