Crop water use estimation of drip irrigated walnut using ANNs and ANFIS models

Abstract

Walnut trees, as well as their fruits, represent an important sector of the agricultural industry and their cultivation significantly contributes to the global economy. Irrigation is a key factor in walnut cultivation and the most important problem is related to accurately estimating the need for irrigation water. Walnut water use was estimated in this study through the artificial intelligence methods of Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) using meteorological data in western Türkiye, which has semi-arid climatic conditions. Probabilistic scenarios based on maximum, minimum and average temperature, wind speed and sunshine hours over the period 2016-2019 were developed and tested with ANNs and ANFIS models to estimate walnut evapotranspiration. Results indicate that the optimum performance in the training and testing for ANNs and ANFIS models was obtained from the fourth scenario with R = 0.95 and two climate parameters -sunshine duration and mean temperature-. Both ANNs and ANFIS models were able to predict crop water use obtaining high correlation and the minimum number of climatic parameters. Nevertheless, the ANFIS model had a higher predictive capacity, with smaller MSE (0.36 for training and 0.29 for testing) compared to the ANNs model.