Modelling coffee yield in Dak Lak province using machine learning algorithms

Abstract

Application of machine learning algorithms in simulating crop yield has attracted more attention from plenty of scientists in recent years. The objective of this study is to estimate the coffee yields in Dak Lak province by using three machine learning algorithms, namely, artificial neural network (ANN), support vector regression (SVR), and random forest (RF), respectively. Input data in simulating processes includes maximum and minimum temperature, effective rainfall, reference evapotranspiration, and crop water requirement in the period 2000-2020. In which, the percentage of data in the training and testing phases is 70% and 30%, respectively. The results indicated that three machine learning models (i.e., SVR, ANN, and RF) have reasonable performance in simulating the coffee yield, out of which, the RF model performs best with NSE values of approximately 0.918 for the training phase and 0.818 for the testing phase.