Estimation of walnut crop evapotranspiration under different micro-irrigation techniques in arid zones based on deep learning sequence models

Abstract

To solve the problems of low accuracy of crop evapotranspiration (ETc) estimates in arid areas and to optimise ETc for precision irrigation in agriculture.Taking walnut in Wensu County, Aksu Region, Xinjiang as the research object, based on the multivariate time series data of walnut fertility, deep learning sequence models such as LSTM, GRU and BiLSTM were utilized to estimate the crop evapotranspiration (ETc) of walnut in arid zones under different micro-irrigation techniques. The results showed that the coefficient of determination (R2) of the LSTM, GRU and BiLSTM models were up to greater than 0.95 for both microirrigation techniques, and the BiLSTM model was relatively optimal. For the 3 g microirrigation technique, the R2, RMSEV and RPDV of the BiLSTM model were 0.978, 0.294 and 6.965 mm/d respectively. For the Hg microirrigation technique, the R2, RMSEV and RPDV of the BiLSTM model were 0.970, 0.339 and 6.026 mm/d respectively.With different scenarios of missing meteorological environmental variables, the BiLSTM model was used to estimate R2 > 0.98 for walnut crop evapotranspiration under different microirrigation techniques, with a range from 0.958 to 0. 986 under 3 g microirrigation technique, RMSEV ranged from 0.235 to 0.412 and RPDV ranged from 5.028 to 8.494. Under Hg microirrigation technique, R2 ranged from 0.942 to 0.979, RMSEV ranged from 0.288 to 0.477 and RPDV ranged from 4.253 to 6.905. Using the deep learning BiLSTM model, we can efficiently estimate the evapotranspiration of the walnut crop in arid zones with different micro-irrigation technologies. The evapotranspiration can provide a scientific theoretical reference for intelligent irrigation of walnut and agricultural water management.