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.

A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications

“…Through this index system, rice yield was estimated by taking multi-spectral images to detect the early stage of rice flowering. Traore et al [161] proposed a neural network method based on multi-spectral remote sensing data to detect equivalent water thickness (EWT). Ndlovu et al [162] proposed a random forest regression method based on multi-spectral remote sensing image data to estimate corn water content.…”

Early estimation of faba bean yield based on unmanned aerial systems hyperspectral images and stacking ensemble

“…With the advent of remote sensing technology, especially through unmanned aerial systems equipped with spectral imaging capabilities, an increasing number of researchers are investigating the collection of phenotypic characteristics of crops. These studies encompass crop phenotype estimation through remote sensing models [7], water and fertilizer management [8][9], and environmental stress [10][11], all yielding promising results. For instance, Li et al [7] fused light detection and ranging (LiDAR) image data and multispectral data to construct a machine learning model that accurately predicted (R 2 = 0.891) wheat yields two months before harvest.…”

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

“…However, the system is not able to provide rapid variations that occur in leaf chlorophyll [37]. Light detection and ranging (LiDAR) construct the crop into 3D images [38], geometrical characteristics, and water and nutrient management [38][39][40][41][42][43]. However, LiDAR has not been extensively tested for crop studies, which may lead to problems in canopy estimation [44], higher costs, and complexity in data management in diverse weather conditions [45].…”