Rated-M for mesocosm: allowing the multimodal analysis of mature root systems in 3D

Dowd, Tyler; McInturf, Samuel A.; Li, Mao

doi:10.1042/etls20200278

Cited by 15 publications

(17 citation statements)

References 80 publications

(88 reference statements)

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“…Plant science community urgently requires advance approaches in the characterization of RSA using novel image-based technologies [ 7 ], to quantify the 3D dynamics in RSA [ 8 , 9 ]. Three tomographic techniques are currently available for non-destructive 3D phenotyping: X-ray Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Position Emission Tomography (PET).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Fruit appearance is a key trait for many crops and can condi-2 tion market viability of fruit products and the success of cul-successfully implemented to measure the shape and size of fruits such as strawberries (4, 12), apples (5), carrot (6, 14), mangoes (28), and many others. More recently, methodologies for 3D reconstruction of plant organs have been developed with approaches that vary in speed, scale, cost, and accuracy; including laser scanners, x-ray computed tomography, and reconstruction from sequences of 2D images from digital cameras (8, [29][30][31][32][33][34][35][36][37][38][39][40][41]. Methods that rely on sequences of 2D images are numerous and variable with their own complexities and nuances that provide different strengths and weaknesses (8,27,37,(40)(41)(42)(43)(44).…”

Section: Introductionmentioning

confidence: 99%

“…Computer vision has shown great potential to quantify external fruit quality and 2D imaging has been successfully implemented to measure the shape and size of fruits such as strawberries (Ishikawa et al, 2018; Feldmann et al, 2020), apples (Migicovsky et al, 2016a), carrot (Horgan, 2001; Turner et al, 2018), mangoes (Naik et al, 2015), and many others. More recently, methodologies for 3D reconstruction of plant organs have been developed with approaches that vary in speed, scale, cost, and accuracy; including laser scanners, x-ray computed tomography, and reconstruction from sequences of 2D images from digital cameras (Gaillard et al, 2020; Chaudhury et al, 2020; Feldman et al, 2021; Dutagaci et al, 2020; Artzet et al, 2020; Teramoto et al, 2020; He et al, 2017; Paulus et al, 2014; Li et al, 2014; Liu et al, 2020; Dowd et al, 2021; Jiang et al, 2019; Sandhu et al, 2019; Hu et al, 2020). Methods that rely on sequences of 2D images are numerous and variable with their own complexities and nuances that provide different strengths and weaknesses (He et al, 2017; Porter et al, 2016; Sandhu et al, 2019; Hu et al, 2020; Wang et al, 2019; Liu et al, 2017, 2020; Warman et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Cost-effective, high-throughput phenotyping system for 3D reconstruction of fruit form

Feldmann

Tabb

2021

Preprint

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Reliable phenotyping methods that are simple to operate and inexpensive to deploy are critical for studying quantitative traits in plants. Traditional fruit shape phenotyping relies on human raters or 2D analyses to assess form, e.g., size and shape. Systems for 3D imaging using multi-view stereo have been implemented, but frequently rely on commercial software and/or specialized hardware, which can lead to limitations in accessibility and scalability. We present a complete system constructed of consumer-grade components for capturing, calibrating, and reconstructing the 3D form of small-to-moderate sized fruits and tubers. Data acquisition and image capture sessions are 9 seconds to capture 60 images. The initial prototype cost was $1600 USD. We measured accuracy by comparing reconstructed models of 3D printed ground truth objects to the original digital files of those same ground truth objects. The R2 between length of the primary, secondary, and tertiary axes, volume, and surface area of the ground-truth object and the reconstructed models was > 0.97 and root-mean square error (RMSE) was <3mm for objects without locally concave regions. Measurements from 1mm and 2mm resolution reconstructions were consistent (R2 > 0.99). Qualitative assessments were performed on 48 fruit and tubers, including 18 strawberries, 12 potatoes, 5 grapes, 7 peppers, and 4 Bosch and 2 red Anjou pears. Our proposed phenotyping system is fast, relatively low cost, and has demonstrated accuracy for certain shape classes, and could be used for the 3D analysis of fruit form.

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“…Plant science community requires advance approaches in the characterization of RSA using novel image-based technologies [5], to quantify the 3D dynamics in RSA [6,7]. Three tomographic techniques are currently available for non-destructive 3D phenotyping: X-ray Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Position Emission Tomography (PET).…”

Section: Introductionmentioning

confidence: 99%

4D Structural Root Architecture Modeling From Digital Twins By X-Ray Computed Tomography

Herrero¹,

Méline²,

Iyer‐Pascuzzi³

et al. 2021

Preprint

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BackgroundBreakthrough imaging technologies are a potential solution to address the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. X-Ray CT (computed tomography) technology is able to acquire 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. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA). ResultsRoots 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, the volume from the cylindrical model was compared against the root digital twin, reaching a coefficient of determination (R2) of 0.84 and a P < 0.001. ConclusionsThe results confirm the feasibility of the proposed methodology, providing scalability to a comprehensive analysis to high throughput root phenotyping.

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Rated-M for mesocosm: allowing the multimodal analysis of mature root systems in 3D

Cited by 15 publications

References 80 publications

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

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

Cost-effective, high-throughput phenotyping system for 3D reconstruction of fruit form

4D Structural Root Architecture Modeling From Digital Twins By X-Ray Computed Tomography

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