The Performance of Crop Yield Forecasting Model based on Artificial Intelligence

Abstract

This paper presents the performance of crop yield forecasting model by comparing the accuracy of the crop yield forecasting models based on artificial intelligence. To compare the error values, researchers use 2 statistical values which are the Coefficient of Multiple Determination (R2) and the Root Mean Square Error (RMSE). The model has the highest R2 value and the lowest RMSE value will be the appropriate model. The models used for comparison in this paper include Linear Regression, Decision Tree Regression, K-Nearest Neighbors Regression, Support Vector Regression, and Multilayer Perceptron Network. The learning dataset comes from the survey of planting area and yield of lychee in Thailand during 2004-2018 and dataset in 2019 to test the performance of the model. The results show that Support Vector Regression has the highest accuracy in forecasting, followed by Multilayer perceptron network, Linear Regression, Decision Tree Regression and k-Nearest Neighbors Regression, respectively.