4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography

Abstract Background Breakthrough imaging technologies may challenge the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. In this context, X-Ray CT (computed tomography) technology can accurately obtain the digital twin of root system architecture (RSA) but computa...

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Autores principales:	Monica Herrero-Huerta, Valerian Meline, Anjali S. Iyer-Pascuzzi, Augusto M. Souza, Mitchell R. Tuinstra, Yang Yang
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Phenotyping Imaging Proximal sensing 3D modeling Skeleton Root system architecture (RSA) Plant culture SB1-1110 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/951f363c867645d38869c35ee8bbc921
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spelling	oai:doaj.org-article:951f363c867645d38869c35ee8bbc9212021-12-05T12:07:42Z4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography10.1186/s13007-021-00819-11746-4811https://doaj.org/article/951f363c867645d38869c35ee8bbc9212021-12-01T00:00:00Zhttps://doi.org/10.1186/s13007-021-00819-1https://doaj.org/toc/1746-4811Abstract Background Breakthrough imaging technologies may challenge the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. In this context, X-Ray CT (computed tomography) technology can accurately obtain the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial–temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting to spatially quantify several traits. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA). Results Roots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, several traits were computed. Two of them were accurately validated using the root digital twin as a ground truth against the cylindrical model: number of branches (RRMSE better than 9%) and volume, reaching a coefficient of determination (R2) of 0.84 and a P < 0.001. Conclusions The experimental results support the viability of the developed methodology, being able to provide scalability to a comprehensive analysis in order to perform high throughput root phenotyping.Monica Herrero-HuertaValerian MelineAnjali S. Iyer-PascuzziAugusto M. SouzaMitchell R. TuinstraYang YangBMCarticlePhenotypingImagingProximal sensing3D modelingSkeletonRoot system architecture (RSA)Plant cultureSB1-1110Biology (General)QH301-705.5ENPlant Methods, Vol 17, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Phenotyping Imaging Proximal sensing 3D modeling Skeleton Root system architecture (RSA) Plant culture SB1-1110 Biology (General) QH301-705.5
spellingShingle	Phenotyping Imaging Proximal sensing 3D modeling Skeleton Root system architecture (RSA) Plant culture SB1-1110 Biology (General) QH301-705.5 Monica Herrero-Huerta Valerian Meline Anjali S. Iyer-Pascuzzi Augusto M. Souza Mitchell R. Tuinstra Yang Yang 4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography
description	Abstract Background Breakthrough imaging technologies may challenge the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. In this context, X-Ray CT (computed tomography) technology can accurately obtain the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial–temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting to spatially quantify several traits. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA). Results Roots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, several traits were computed. Two of them were accurately validated using the root digital twin as a ground truth against the cylindrical model: number of branches (RRMSE better than 9%) and volume, reaching a coefficient of determination (R2) of 0.84 and a P < 0.001. Conclusions The experimental results support the viability of the developed methodology, being able to provide scalability to a comprehensive analysis in order to perform high throughput root phenotyping.
format	article
author	Monica Herrero-Huerta Valerian Meline Anjali S. Iyer-Pascuzzi Augusto M. Souza Mitchell R. Tuinstra Yang Yang
author_facet	Monica Herrero-Huerta Valerian Meline Anjali S. Iyer-Pascuzzi Augusto M. Souza Mitchell R. Tuinstra Yang Yang
author_sort	Monica Herrero-Huerta
title	4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography
title_short	4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography
title_full	4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography
title_fullStr	4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography
title_full_unstemmed	4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography
title_sort	4d structural root architecture modeling from digital twins by x-ray computed tomography
publisher	BMC
publishDate	2021
url	https://doaj.org/article/951f363c867645d38869c35ee8bbc921
work_keys_str_mv	AT monicaherrerohuerta 4dstructuralrootarchitecturemodelingfromdigitaltwinsbyxraycomputedtomography AT valerianmeline 4dstructuralrootarchitecturemodelingfromdigitaltwinsbyxraycomputedtomography AT anjalisiyerpascuzzi 4dstructuralrootarchitecturemodelingfromdigitaltwinsbyxraycomputedtomography AT augustomsouza 4dstructuralrootarchitecturemodelingfromdigitaltwinsbyxraycomputedtomography AT mitchellrtuinstra 4dstructuralrootarchitecturemodelingfromdigitaltwinsbyxraycomputedtomography AT yangyang 4dstructuralrootarchitecturemodelingfromdigitaltwinsbyxraycomputedtomography
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4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography

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