RhizoVision Crown: An Integrated Hardware and Software Platform for Root Crown Phenotyping

Seethepalli, Anand; Guo, Haichao; Liu, Xiuwei; Griffiths, Marcus; Almtarfi, Hussien; Li, Zenglu; Liu, Shuyu; Zare, Alina; Fritschi, Felix B.; Blancaflor, Elison B.; Ma, Xuefeng; York, Larry M.

doi:10.34133/2020/3074916

Cited by 92 publications

(69 citation statements)

References 55 publications

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“…The challenge now is to develop new and improved analytical tools, capable of transforming this wealth of data into valuable knowledge [ 15 ]. This is a rapidly evolving field, and numerous software and pipelines to automatize phenotype collection are already available [ 18 – 22 ]. Many of these tools focus on the analysis of root images and, as far as we know, require more user intervention than we propose, making it impractical to analyze hundreds of images.…”

Section: Introductionmentioning

confidence: 99%

Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry

2021

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Automatizing phenotype measurement will decisively contribute to increase plant breeding efficiency. Among phenotypes, morphological traits are relevant in many fruit breeding programs, as appearance influences consumer preference. Often, these traits are manually or semiautomatically obtained. Yet, fruit morphology evaluation can be enhanced using fully automatized procedures and digital images provide a cost-effective opportunity for this purpose. Here, we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry (Fragaria x ananassa) images. The pipeline segments, classifies, and labels the images and extracts conformation features, including linear (area, perimeter, height, width, circularity, shape descriptor, ratio between height and width) and multivariate (Fourier elliptical components and Generalized Procrustes) statistics. Internal color patterns are obtained using an autoencoder to smooth out the image. In addition, we develop a variational autoencoder to automatically detect the most likely number of underlying shapes. Bayesian modeling is employed to estimate both additive and dominance effects for all traits. As expected, conformational traits are clearly heritable. Interestingly, dominance variance is higher than the additive component for most of the traits. Overall, we show that fruit shape and color can be quickly and automatically evaluated and are moderately heritable. Although we study strawberry images, the algorithm can be applied to other fruits, as shown in the GitHub repository.

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Section: Introductionmentioning

confidence: 99%

Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry

2021

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“…This study relied on manual measurements and scoring of root traits, an approach that has been used for a variety of crops (Trachsel et al, 2011 ; Burridge et al, 2016 ). With the advent of image-based methods, higher throughput and possibly greater precision of trait measurement due to less time spent evaluating traits and human bias or error (Bucksch et al, 2014 ; Colombi et al, 2015 ; Seethepalli et al, 2020 ) will allow more ambitious studies to expand on the findings reported here. Alleviating the phenotyping bottleneck for roots through the utilization of new technologies like imaging and image analysis for root traits is expected to enhance future genetic and physiological studies.…”

Section: Discussionmentioning

confidence: 90%

Genome-Wide Association Study of Topsoil Root System Architecture in Field-Grown Soybean [Glycine max (L.) Merr.]

et al. 2021

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Water and nutrient acquisition is a critical function of plant root systems. Root system architecture (RSA) traits are often complex and controlled by many genes. This is the first genome-wide association study reporting genetic loci for RSA traits for field-grown soybean (Glycine max). A collection of 289 soybean genotypes was grown in three environments, root crowns were excavated, and 12 RSA traits assessed. The first two components of a principal component analysis of these 12 traits were used as additional aggregate traits for a total of 14 traits. Marker–trait association for RSA traits were identified using 31,807 single-nucleotide polymorphisms (SNPs) by a genome-wide association analysis. In total, 283 (non-unique) SNPs were significantly associated with one or more of the 14 root traits. Of these, 246 were unique SNPs and 215 SNPs were associated with a single root trait, while 26, four, and one SNPs were associated with two, three, and four root traits, respectively. The 246 SNPs marked 67 loci associated with at least one of the 14 root traits. Seventeen loci on 13 chromosomes were identified by SNPs associated with more than one root trait. Several genes with annotation related to processes that could affect root architecture were identified near these 67 loci. Additional follow-up studies will be needed to confirm the markers and candidate genes identified for RSA traits and to examine the importance of the different root characteristics for soybean productivity under a range of soil and environmental conditions.

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“…Many algorithms and software packages have been devised to extract complex features and traits from still images [39]. Programs such as DIRT [42,43], archiDART [44], EZ-Root-VIS [45], GiA Roots [46], WinRHIZO (Regent Instrument Inc., Ville de Québec, QC Canada), and RhizoVision [47] provide a means to measure of a variety of traits of interest; such as root lengths, diameter classes, volumes, and surface areas of individual roots or entire root systems of many species, for example, Zea mays [48].…”

Section: Imaging Methods For Root Systemsmentioning

confidence: 99%

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

Dowd

McInturf

2021

Emerging Topics in Life Sciences

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A plants’ water and nutrients are primarily absorbed through roots, which in a natural setting is highly dependent on the 3-dimensional configuration of the root system, collectively known as root system architecture (RSA). RSA is difficult to study due to a variety of factors, accordingly, an arsenal of methods have been developed to address the challenges of both growing root systems for imaging, and the imaging methods themselves, although there is no ‘best’ method as each has its own spectrum of trade-offs. Here, we describe several methods for plant growth or imaging. Then, we introduce the adaptation and integration of three complementary methods, root mesocosms, photogrammetry, and electrical resistance tomography (ERT). Mesocosms can allow for unconstrained root growth, excavation and preservation of 3-dimensional RSA, and modularity that facilitates the use of a variety of sensors. The recovered root system can be digitally reconstructed through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. Lastly, we demonstrate how 3-dimensional water availability can be measured using ERT inside of root mesocosms.

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RhizoVision Crown: An Integrated Hardware and Software Platform for Root Crown Phenotyping

Cited by 92 publications

References 55 publications

Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry

Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry

Genome-Wide Association Study of Topsoil Root System Architecture in Field-Grown Soybean [Glycine max (L.) Merr.]

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

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