A hybrid machine learning approach for estimating the water-use efficiency and yield in agriculture

Abstract

This paper introduces the narrow strip irrigation (NSI) method and aims to estimate water-use efficiency (WUE) and yield in apple orchards under NSI in the Miandoab region located southeast of Lake Urmia using a machine learning approach. To perform the estimation, a hybrid method based on an adaptive neuro-fuzzy inference system (ANFIS) and seasons optimization (SO) algorithm was proposed. According to the irrigation and climate factors, six different models have been proposed to combine the parameters in the SO-ANFIS. The proposed method is evaluated on a test data set that contains information about apple orchards in Miandoab city from 2019 to 2021. The NSI model was compared with two popular irrigation methods including two-sided furrow irrigation (TSFI) and basin irrigation (BI) on benchmark scenarios. The results justified that the NSI model increased WUE by 1.90 kg/m3 and 3.13 kg/m3, and yield by 8.57% and 14.30% compared to TSFI and BI methods, respectively. The experimental results show that the proposed SO-ANFIS has achieved the performance of 0.989 and 0.988 in terms of R2 criterion in estimating WUE and yield of NSI irrigation method, respectively. The results confirmed that the SO-ANFIS outperformed the counterpart methods in terms of performance measures.