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

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 performance...

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Autores principales:	Adama Traore, Syed Tahir Ata-Ul-Karim, Aiwang Duan, Mukesh Kumar Soothar, Seydou Traore, Ben Zhao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	equivalent water thickness UAV deep learning vegetation indices multispectral images Science Q
Acceso en línea:	https://doaj.org/article/c79f61f95aa648aebaafe8e999af4efb
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spelling	oai:doaj.org-article:c79f61f95aa648aebaafe8e999af4efb2021-11-11T18:58:43ZPredicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques10.3390/rs132144762072-4292https://doaj.org/article/c79f61f95aa648aebaafe8e999af4efb2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4476https://doaj.org/toc/2072-4292The 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 R<sup>2</sup> = 0.934, NSE = 0.933, RMSE = 0.028 g/cm<sup>2</sup>, and MAE of 0.017 g/cm<sup>2</sup> 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.Adama TraoreSyed Tahir Ata-Ul-KarimAiwang DuanMukesh Kumar SootharSeydou TraoreBen ZhaoMDPI AGarticleequivalent water thicknessUAVdeep learningvegetation indicesmultispectral imagesScienceQENRemote Sensing, Vol 13, Iss 4476, p 4476 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	equivalent water thickness UAV deep learning vegetation indices multispectral images Science Q
spellingShingle	equivalent water thickness UAV deep learning vegetation indices multispectral images Science Q Adama Traore Syed Tahir Ata-Ul-Karim Aiwang Duan Mukesh Kumar Soothar Seydou Traore Ben Zhao Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques
description	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 R<sup>2</sup> = 0.934, NSE = 0.933, RMSE = 0.028 g/cm<sup>2</sup>, and MAE of 0.017 g/cm<sup>2</sup> 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.
format	article
author	Adama Traore Syed Tahir Ata-Ul-Karim Aiwang Duan Mukesh Kumar Soothar Seydou Traore Ben Zhao
author_facet	Adama Traore Syed Tahir Ata-Ul-Karim Aiwang Duan Mukesh Kumar Soothar Seydou Traore Ben Zhao
author_sort	Adama Traore
title	Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques
title_short	Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques
title_full	Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques
title_fullStr	Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques
title_full_unstemmed	Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques
title_sort	predicting equivalent water thickness in wheat using uav mounted multispectral sensor through deep learning techniques
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c79f61f95aa648aebaafe8e999af4efb
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Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques

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