UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection

The foundation of contemporary weed management practices in many parts of the world is glyphosate. However, dependency on the effectiveness of herbicide practices has led to overuse through continuous growth of crops resistant to a single mode of action. In order to provide a cost-effective weed man...

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Autores principales:	Austin Eide, Cengiz Koparan, Yu Zhang, Michael Ostlie, Kirk Howatt, Xin Sun
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	weed identification glyphosate thermal image multispectral image UAV Science Q
Acceso en línea:	https://doaj.org/article/c7280891f6b04d329b437f93ae96bd3f
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spelling	oai:doaj.org-article:c7280891f6b04d329b437f93ae96bd3f2021-11-25T18:54:41ZUAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection10.3390/rs132246062072-4292https://doaj.org/article/c7280891f6b04d329b437f93ae96bd3f2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4606https://doaj.org/toc/2072-4292The foundation of contemporary weed management practices in many parts of the world is glyphosate. However, dependency on the effectiveness of herbicide practices has led to overuse through continuous growth of crops resistant to a single mode of action. In order to provide a cost-effective weed management strategy that does not promote glyphosate-resistant weed biotypes, differences between resistant and susceptible biotypes have to be identified accurately in the field conditions. Unmanned Aerial Vehicle (UAV)-assisted thermal and multispectral remote sensing has potential for detecting biophysical characteristics of weed biotypes during the growing season, which includes distinguishing glyphosate-susceptible and glyphosate-resistant weed populations based on canopy temperature and deep learning driven weed identification algorithms. The objective of this study was to identify herbicide resistance after glyphosate application in true field conditions by analyzing the UAV-acquired thermal and multispectral response of kochia, waterhemp, redroot pigweed, and common ragweed. The data were processed in ArcGIS for raster classification as well as spectral comparison of glyphosate-resistant and glyphosate-susceptible weeds. The classification accuracy between the sensors and classification methods of maximum likelihood, random trees, and Support Vector Machine (SVM) were compared. The random trees classifier performed the best at 4 days after application (DAA) for kochia with 62.9% accuracy. The maximum likelihood classifier provided the highest performing result out of all classification methods with an accuracy of 75.2%. A commendable classification was made at 8 DAA where the random trees classifier attained an accuracy of 87.2%. However, thermal reflectance measurements as a predictor for glyphosate resistance within weed populations in field condition was unreliable due to its susceptibility to environmental conditions. Normalized Difference Vegetation Index (NDVI) and a composite reflectance of 842 nm, 705 nm, and 740 nm wavelength managed to provide better classification results than thermal in most cases.Austin EideCengiz KoparanYu ZhangMichael OstlieKirk HowattXin SunMDPI AGarticleweed identificationglyphosatethermal imagemultispectral imageUAVScienceQENRemote Sensing, Vol 13, Iss 4606, p 4606 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	weed identification glyphosate thermal image multispectral image UAV Science Q
spellingShingle	weed identification glyphosate thermal image multispectral image UAV Science Q Austin Eide Cengiz Koparan Yu Zhang Michael Ostlie Kirk Howatt Xin Sun UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection
description	The foundation of contemporary weed management practices in many parts of the world is glyphosate. However, dependency on the effectiveness of herbicide practices has led to overuse through continuous growth of crops resistant to a single mode of action. In order to provide a cost-effective weed management strategy that does not promote glyphosate-resistant weed biotypes, differences between resistant and susceptible biotypes have to be identified accurately in the field conditions. Unmanned Aerial Vehicle (UAV)-assisted thermal and multispectral remote sensing has potential for detecting biophysical characteristics of weed biotypes during the growing season, which includes distinguishing glyphosate-susceptible and glyphosate-resistant weed populations based on canopy temperature and deep learning driven weed identification algorithms. The objective of this study was to identify herbicide resistance after glyphosate application in true field conditions by analyzing the UAV-acquired thermal and multispectral response of kochia, waterhemp, redroot pigweed, and common ragweed. The data were processed in ArcGIS for raster classification as well as spectral comparison of glyphosate-resistant and glyphosate-susceptible weeds. The classification accuracy between the sensors and classification methods of maximum likelihood, random trees, and Support Vector Machine (SVM) were compared. The random trees classifier performed the best at 4 days after application (DAA) for kochia with 62.9% accuracy. The maximum likelihood classifier provided the highest performing result out of all classification methods with an accuracy of 75.2%. A commendable classification was made at 8 DAA where the random trees classifier attained an accuracy of 87.2%. However, thermal reflectance measurements as a predictor for glyphosate resistance within weed populations in field condition was unreliable due to its susceptibility to environmental conditions. Normalized Difference Vegetation Index (NDVI) and a composite reflectance of 842 nm, 705 nm, and 740 nm wavelength managed to provide better classification results than thermal in most cases.
format	article
author	Austin Eide Cengiz Koparan Yu Zhang Michael Ostlie Kirk Howatt Xin Sun
author_facet	Austin Eide Cengiz Koparan Yu Zhang Michael Ostlie Kirk Howatt Xin Sun
author_sort	Austin Eide
title	UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection
title_short	UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection
title_full	UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection
title_fullStr	UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection
title_full_unstemmed	UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection
title_sort	uav-assisted thermal infrared and multispectral imaging of weed canopies for glyphosate resistance detection
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c7280891f6b04d329b437f93ae96bd3f
work_keys_str_mv	AT austineide uavassistedthermalinfraredandmultispectralimagingofweedcanopiesforglyphosateresistancedetection AT cengizkoparan uavassistedthermalinfraredandmultispectralimagingofweedcanopiesforglyphosateresistancedetection AT yuzhang uavassistedthermalinfraredandmultispectralimagingofweedcanopiesforglyphosateresistancedetection AT michaelostlie uavassistedthermalinfraredandmultispectralimagingofweedcanopiesforglyphosateresistancedetection AT kirkhowatt uavassistedthermalinfraredandmultispectralimagingofweedcanopiesforglyphosateresistancedetection AT xinsun uavassistedthermalinfraredandmultispectralimagingofweedcanopiesforglyphosateresistancedetection
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UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection

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