CROP YIELD PREDICTION USING DEEP XG BOOST ALGORITHM

Abstract: Because crop yield is dependent on a number of variables, it is a difficult task to predict. Even though a lot of models have been created so far in the literature, they still need to be improved because their performance is inadequate. In order to assess the performance of the underlying algorithms in relation to various performance criteria, we created deep learning-based models for this study. The XGBoost machine learning (ML) algorithm, convolutional neural networks (CNN), XGBoost, and recurrent neural networks (RNN) are the algorithms that were assessed in this study. According to the environmental, soil, silt, nitrogen, clay, ocd, ocs, pHH2O, sand, soc, ceo, water, and crop parameters, we estimated crop yield for the case study.

Keywords: XGBOOST,Crop yield learning algorithms.