Building a System for Predicting the Yield of Grain Crops Based On Machine Learning Using the XGBRegressor Algorithm

Abstract

The use of machine learning to predict crop yields is important for development, since the proposal of this solution will improve and facilitate the task of the whole department of the agricultural sector in calculations and predictions, will help to focus on optimizing the infrastructure for the production of crops. In this paper, a solution is proposed for automating the forecast of grain yield based on machine learning using the XGBRegressor algorithm with a collected dataset of 44 parameters in the territory of the Republic of Kazakhstan and for each region of the Republic of Kazakhstan separately. Comparisons were made with the Linear Regression and Decision Tree Regressor algorithms. Validation was carried out for the period 01.2012 - 09.2020, the test was carried out at 10.2020. As a result, a model was obtained that predicts the yield quite accurately compared to other algorithms, and the results were interpreted using the RMSE metric to understand the difference more accurately between the algorithms and the errors made by the model.