CROP YIELD PREDICTION USING DEEP XGBOOST ALGORITHM

Abstract

Predicting crop yield is a complex task since it depends on multiple factors. Although many models have been developed so far in the literature, the performance of current models is not satisfactory, and hence, they must be improved. In this study, we developed deep learning-based models to evaluate how the underlying algorithms perform with respect to different performance criteria. The algorithms evaluated in our study are the XGBoost machine learning (ML) algorithm, Convolutional Neural Networks (CNN),XGBoost, and Recurrent Neural Networks (RNN). For the case study, we predicted crop yield based on the environmental, soil, silt, nitrogen, clay, ocd, ocs, pHH2O, sand, soc, ceo, water and crop parameters has been a potential research topic. Our proposed method has high performance by preforming feature selection on predicting the crop yield. Our proposed method used for both the soyabeans and corn crop for predicting their yield.