Machine learning techniques in estimation of eggplant crop evapotranspiration

Abstract

This study predicted the daily evapotranspiration of eggplant (Solanum melongena L.) under full and deficit irrigation in the Bafra district of Samsun province, Turkey, using machine learning methods. Artificial neural networks (ANNs), deep neural networks (DNN), M5 model tree (M5Tree), random forest (RF), support vector machine (SVM), k-nearest neighbor (kNN), and adaptive boosting were investigated as machine learning approaches. Determination of evapotranspiration in this study consists of three methods: (i) The reference evapotranspiration (ETo) was obtained from the Food and Agriculture Organization-56 Penman–Monteith equation, (ii) the values of evapotranspiration (ETc) calculated by multiplying the reference evapotranspiration by the crop coefficient (Kc), and (iii) the values of evapotranspiration (ETa) measured using soil water balance between successive soil water measurements as the outputs. The model’s performance in ETo estimation was higher when minimum and maximum temperature (Tmax and Tmin), wind speed (u2), average relative humidity (RHavg), solar radiation (Rs), and days of the year were used as inputs. The best performance was obtained in the ANN model with a coefficient of determination (R2) value of 0.984, a mean absolute error (MAE) of 0.098 mm d−1, a root-mean-square error (RMSE) of 0.153 mm d−1, and Nash–Sutcliffe efficiency of 0.983. The model’s performance in ETc estimation was significantly improved with the addition of leaf area index (LAI) and crop height (hc) to the climate parameters (MAE and RMSE values decreased by 22.6 and 23.2%, respectively). The accuracy of ETc estimation for some plant traits (hc and LAI) and average temperature (Tavg) was sufficient. The best statistical performance in estimating ETa was obtained by the RF model (Tavg, u2, RHavg, and Rs) using climate parameters. DNN proved to be the least successful model compared to the other six models in predicting ETo, ETc, and ETa.