Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques

Abstract

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.

Keywords

• equivalent water thickness
• UAV
• deep learning
• vegetation indices
• multispectral images