Simplifying PlantCV workflows with multiple objects

Schuhl, Haley; Peery, J David; Gutiérrez, Jorge; Gehan, Malia; Fahlgren, Noah

doi:10.22541/au.166758437.76129704/v1

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Automated annotation of multiplexed images has previously been implemented in the PlantCV framework, relying on subject detection and assuming a true grid (plantcv.roi.auto grid) (Schuhl et al, 2022). Although the reported strategy is robust to some missing subjects, in AlGrow we have preferred to detect internal references to ensure consistent annotation.…”

Section: Discussionmentioning

confidence: 99%

AlGrow: a graphical interface for easy, fast and accurate area and growth analysis of heterogeneously colored targets

McHale,

Sulpice

2024

Preprint

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Image analysis is widely used in plant biology to determine growth rates and other phenotypic characters, with segmentation into foreground and background being a primary challenge. Statistical clustering and learning approaches can reduce the need for user input into this process, though these are computationally demanding, can generalise poorly and are not intuitive to end users. As such, simple strategies that rely on the definition of a range of target colors are still frequently adopted. These are limited by the geometries in color space that are implicit to their definition; i.e. thresholds define cuboid volumes and selected colors with a radius define spheroid volumes. A more comprehensive specification of target color is a hull, in color space, enclosing the set of colors in the image foreground. We developed AlGrow, a software tool that allows users to easily define hulls by clicking on the source image or a three-dimensional projection of its colors. We implemented convex hulls and then alpha-hulls, i.e. a limit applied to hull edge length, to support concave surfaces and disjoint color volumes. AlGrow also provides automated annotation by detecting internal circular markers, such as pot margins, and applies relative indexes to support movement. Analysis of publicly available Arabidopsis image series and metadata demonstrated effective automated annotation and mean Dice coefficients of >0.95 following training on only the first and last images in each series. AlGrow provides both graphical and command line interfaces and is released free and open-source with compiled binaries for the major operating systems.

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Section: Discussionmentioning

confidence: 99%

AlGrow: a graphical interface for easy, fast and accurate area and growth analysis of heterogeneously colored targets

McHale,

Sulpice

2024

Preprint

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“…High throughput phenotyping technologies, which enable rapid and efficient measurement of physical traits in organisms, are increasingly being developed to bridge this gap (Herr et al, 2023). These include, but are not limited to, phone apps, automated lab equipment and greenhouses, RGB and hyperspectral imaging technologies, light detection and ranging (lidar) scanners, and ground penetrating radar (De Bei et al, 2016;Jimenez-Berni et al, 2018;Siebers et al, 2018;Bai et al, 2019;Ferguson et al, 2021;Jin et al, 2021;Manavalan et al, 2021;Montes et al, 2022;Schuhl et al, 2022). These technologies have advanced beyond mere data collection by facilitating a convergence of expertise from multiple disciplines, which enables the affordable and rapid transformation of raw data into biologically meaningful traits.…”

Section: Introductionmentioning

confidence: 99%

To have value, comparisons of high-throughput phenotyping methods need statistical tests of bias and variance

McGrath,

Siebers,

et al. 2024

Front. Plant Sci.

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The gap between genomics and phenomics is narrowing. The rate at which it is narrowing, however, is being slowed by improper statistical comparison of methods. Quantification using Pearson’s correlation coefficient (r) is commonly used to assess method quality, but it is an often misleading statistic for this purpose as it is unable to provide information about the relative quality of two methods. Using r can both erroneously discount methods that are inherently more precise and validate methods that are less accurate. These errors occur because of logical flaws inherent in the use of r when comparing methods, not as a problem of limited sample size or the unavoidable possibility of a type I error. A popular alternative to using r is to measure the limits of agreement (LOA). However both r and LOA fail to identify which instrument is more or less variable than the other and can lead to incorrect conclusions about method quality. An alternative approach, comparing variances of methods, requires repeated measurements of the same subject, but avoids incorrect conclusions. Variance comparison is arguably the most important component of method validation and, thus, when repeated measurements are possible, variance comparison provides considerable value to these studies. Statistical tests to compare variances presented here are well established, easy to interpret and ubiquitously available. The widespread use of r has potentially led to numerous incorrect conclusions about method quality, hampering development, and the approach described here would be useful to advance high throughput phenotyping methods but can also extend into any branch of science. The adoption of the statistical techniques outlined in this paper will help speed the adoption of new high throughput phenotyping techniques by indicating when one should reject a new method, outright replace an old method or conditionally use a new method.

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HopBox: An image analysis pipeline to characterize hop cone morphology

Altendorf,

Heineck,

Wakholi

et al. 2023

The Plant Phenome Journal

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Hop cone morphology can influence picking and drying ability, and color can impact consumer preference and may be indicative of quality. However, these characteristics are not generally evaluated in hop breeding programs due to the tedious nature of trait quantification and the extensive variation among cones within a genotype. We developed the HopBox, which is a simply constructed light box with a camera mount, and a publicly available image processing pipeline that identifies hop cones within color‐corrected images, reads a QR code within the image, and outputs data on hop cone length, width, area, perimeter, openness, weight, color, and density. The trained model was applied to images of 500 cones each from 15 replicated advanced hop genotypes from the USDA‐ARS breeding program in Prosser, Washington. Analysis of variance revealed significant (p < 0.001) differences between genotypes for all traits measured, enabling breeders to discriminate between genotypes for selection purposes. Broad sense heritability for all traits ranged from 0.23 to 0.59. A random sampling of hop cones from the complete dataset revealed that imaging only 5–10 cones adequately captured genotypic variation and provided acceptable rank correlations (rs > 0.75); however, increasing the sample size to 30 provided optimal precision. Instructions for constructing a HopBox and the code for the analysis pipeline are publicly available online and have wide applicability for hop breeding and research.

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Simplifying PlantCV workflows with multiple objects

Cited by 4 publications

References 5 publications

AlGrow: a graphical interface for easy, fast and accurate area and growth analysis of heterogeneously colored targets

AlGrow: a graphical interface for easy, fast and accurate area and growth analysis of heterogeneously colored targets

To have value, comparisons of high-throughput phenotyping methods need statistical tests of bias and variance

HopBox: An image analysis pipeline to characterize hop cone morphology

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