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

Syed Tahir Ata-Ul-Karim,Ben Zhao,Adama Traore,Aiwang Duan,Seydou Traore,Mukesh Kumar Soothar

doi:10.3390/rs13214476

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.

Highlights

Licensee MDPI, Basel, Switzerland.Wheat production accounts for nearly 50% of China’s National Agricultural output.Increasing wheat consumption requires effective decision-making during the wheat growth period
From the early stem elongation stage to the late stem elongation stage, there was an increase in Fresh weight (FW), dry weight (DW), and EWTcanopy
From the late stem elongation to anthesis growth stages, there was a decrease in EWTcanopy that can be explained by an increase in DW in favor of FW

Summary

Introduction

Wheat production accounts for nearly 50% of China’s National Agricultural output. Increasing wheat consumption requires effective decision-making during the wheat growth period. Field data collection has been employed to diagnose plant biophysical parameters, including equivalent water thickness (EWT) [1]. Improving crop water management requires the accurate and timely monitoring of water in the plant [2]. EWT has previously been used to derive the expected grain yield [3] by managing irrigation. H.; Miao, Y.; Wang, T.; Li, Y.; Zhang, J.; Sun, W. Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine. Potential of UAV-based active sensing for monitoring rice leaf nitrogen status.

Objectives

Methods

Results

Discussion

Conclusion