Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation

Abstract Alfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements h...

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Autores principales:	Zhou Tang, Atit Parajuli, Chunpeng James Chen, Yang Hu, Samuel Revolinski, Cesar Augusto Medina, Sen Lin, Zhiwu Zhang, Long-Xi Yu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a60bd3bc401647559ea34f685d583f84
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spelling	oai:doaj.org-article:a60bd3bc401647559ea34f685d583f842021-12-02T12:09:26ZValidation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation10.1038/s41598-021-82797-x2045-2322https://doaj.org/article/a60bd3bc401647559ea34f685d583f842021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82797-xhttps://doaj.org/toc/2045-2322Abstract Alfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.Zhou TangAtit ParajuliChunpeng James ChenYang HuSamuel RevolinskiCesar Augusto MedinaSen LinZhiwu ZhangLong-Xi YuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhou Tang Atit Parajuli Chunpeng James Chen Yang Hu Samuel Revolinski Cesar Augusto Medina Sen Lin Zhiwu Zhang Long-Xi Yu Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation
description	Abstract Alfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.
format	article
author	Zhou Tang Atit Parajuli Chunpeng James Chen Yang Hu Samuel Revolinski Cesar Augusto Medina Sen Lin Zhiwu Zhang Long-Xi Yu
author_facet	Zhou Tang Atit Parajuli Chunpeng James Chen Yang Hu Samuel Revolinski Cesar Augusto Medina Sen Lin Zhiwu Zhang Long-Xi Yu
author_sort	Zhou Tang
title	Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation
title_short	Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation
title_full	Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation
title_fullStr	Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation
title_full_unstemmed	Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation
title_sort	validation of uav-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a60bd3bc401647559ea34f685d583f84
work_keys_str_mv	AT zhoutang validationofuavbasedalfalfabiomasspredictabilityusingphotogrammetrywithfullyautomaticplotsegmentation AT atitparajuli validationofuavbasedalfalfabiomasspredictabilityusingphotogrammetrywithfullyautomaticplotsegmentation AT chunpengjameschen validationofuavbasedalfalfabiomasspredictabilityusingphotogrammetrywithfullyautomaticplotsegmentation AT yanghu validationofuavbasedalfalfabiomasspredictabilityusingphotogrammetrywithfullyautomaticplotsegmentation AT samuelrevolinski validationofuavbasedalfalfabiomasspredictabilityusingphotogrammetrywithfullyautomaticplotsegmentation AT cesaraugustomedina validationofuavbasedalfalfabiomasspredictabilityusingphotogrammetrywithfullyautomaticplotsegmentation AT senlin validationofuavbasedalfalfabiomasspredictabilityusingphotogrammetrywithfullyautomaticplotsegmentation AT zhiwuzhang validationofuavbasedalfalfabiomasspredictabilityusingphotogrammetrywithfullyautomaticplotsegmentation AT longxiyu validationofuavbasedalfalfabiomasspredictabilityusingphotogrammetrywithfullyautomaticplotsegmentation
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Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation

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