The equivalent water thickness (EWT) is an important biophysical indicator of water status
in crops. The effective monitoring of EWT in wheat under different nitrogen and water treatments
is important for irrigation management in precision agriculture. This study aimed to investigate
the performances of machine learning (ML) algorithms in retrieving wheat EWT. For this purpose,
a rain shelter experiment (Exp. 1) with four irrigation quantities (0, 120, 240, 360 mm) and two
nitrogen levels (75 and 255 kg N/ha), and field experiments (Exps. 2–3) with the same irrigation
and rainfall water levels (360 mm) but different nitrogen levels (varying from 75 to 255 kg N/ha)
were conducted in the North China Plain. The canopy reflectance was measured for all plots at
30 m using an unmanned aerial vehicle (UAV)-mounted multispectral camera. Destructive sampling
was conducted immediately after the UAV flights to measure total fresh and dry weight. Deep
Neural Network (DNN) is a special type of neural network, which has shown performance in
regression analysis is compared with other machine learning (ML) models. A feature selection
(FS) algorithm named the decision tree (DT) was used as the automatic relevance determination
method to obtain the relative relevance of 5 out of 67 vegetation indices (Vis), which were used
for estimating EWT. The selected VIs were used to estimate EWT using multiple linear regression
(MLR), deep neural network multilayer perceptron (DNN-MLP), artificial neural networks multilayer
perceptron (ANN-MLP), boosted tree regression (BRT), and support vector machines (SVMs). The
results show that the DNN-MLP with R2 = 0.934, NSE = 0.933, RMSE = 0.028 g/cm2, and MAE of
0.017 g/cm2 outperformed other ML algorithms (ANN-MPL, BRT, and SVM- Polynomial) owing to
its high capacity for estimating EWT as compared to other ML methods. Our findings support the
conclusion that ML can potentially be applied in combination with VIs for retrieving EWT. Despite
the complexity of the ML models, the EWT map should help farmers by improving the real-time
irrigation efficiency of wheat by quantifying field water content and addressing variability

Equivalent water thickness; UAV; deep learning; vegetation indices; multispectral images