The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples

Halcro, Keith; McNabb, Kaitlin; Lockinger, Ashley; Socquet-Juglard, Didier; Bett, Kirstin E.; Noble, Scott D.

doi:10.1186/s13007-020-00591-8

Cited by 29 publications

(29 citation statements)

References 19 publications

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“…The speed at which the non-destructive OCT measurements can be taken makes seed coat thickness a viable phenotype to use in breeding or plant physiology studies having a large number of samples, a small number of individual seeds per sample, or both. While manual measurement with this technique is quite fast, incorporating OCT measurement into an automated individual seed-imaging system [9] would further accelerate the process.…”

Section: Discussionmentioning

confidence: 99%

Optical Coherence Tomography Applied to Non-Destructive Seed Coat Thickness Measurement

Nguyen

Bett

Noble

2021

Preprint

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Background: Seed coat thickness is a parameter of interest in lentil breeding and processing. Methods described in the literature are destructive and time consuming, limiting the usefulness of this seed characteristic in breeding or grading. In this study, a low-cost optical coherence tomography (OCT) system (OQ Labscope 2.0 from Lumedica, Inc., Durham, NC USA) was used to non-destructively measure lentil seed coat thicknesses. These measurements were compared to destructive measurements obtained via optical microscopy of cross-sectioned seeds. Results: Measurements from the two methods were comparable with consistent trends in thickness and population separability among the five lentil accessions used. The average microscope-based measurements for each accession were higher than those from the OCT, with a linear relationship between the two sets of measurements. The discrepancy in absolute thicknesses is attributed to differences in instrument calibration and in the definition of seed coat boundaries in the two methods. Based on data collected from a larger population, OCT measurements took a mean time of 30 seconds/seed, and a median time of 16 seconds/seed, with no sample preparation required. Conclusions: Optical coherence tomography is a viable method for acquiring comparative data on seed coat thickness of lentil seeds, and presumably other species with similar seed characteristics. The method is non-destructive, and fast enough to be practical for studying large breeding populations. Opportunities for greater efficiencies as part of an automated imaging system are being explored.

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Section: Discussionmentioning

confidence: 99%

Optical Coherence Tomography Applied to Non-Destructive Seed Coat Thickness Measurement

Nguyen

Bett

Noble

2021

Preprint

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“…Recently, deep learning has become a powerful technique used by some seed germination analysis software (Mahajan et al ., 2018; Nguyen et al ., 2018; Halcro et al ., 2020), in which it was applied to extract features, segment seeds, and classify germination status. Although DL is presently relatively easy to implement through P ython , the reasons we chose a combined CV and ML approach are as follows.…”

Section: Discussionmentioning

confidence: 99%

SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination

et al. 2020

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Efficient seed germination and establishment are important traits for field and glasshouse crops. Large-scale germination experiments are laborious and prone to observer errors, leading to the necessity for automated methods. We experimented with five crop species, including tomato, pepper, Brassica, barley, and maize, and concluded an approach for large-scale germination scoring. Here, we present the SeedGerm system, which combines cost-effective hardware and open-source software for seed germination experiments, automated seed imaging, and machine-learning based phenotypic analysis. The software can process multiple image series simultaneously and produce reliable analysis of germination-and establishment-related traits, in both comma-separated values (CSV) and processed images (PNG) formats. In this article, we describe the hardware and software design in detail. We also demonstrate that SeedGerm could match specialists' scoring of radicle emergence. Germination curves were produced based on seed-level germination timing and rates rather than a fitted curve. In particular, by scoring germination across a diverse panel of Brassica napus varieties, SeedGerm implicates a gene important in abscisic acid (ABA) signalling in seeds. We compared SeedGerm with existing methods and concluded that it could have wide utilities in large-scale seed phenotyping and testing, for both research and routine seed technology applications.

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“…The second protocol is for high-throughput seed phenotyping and was developed and tested in lentil at the University of Saskatchewan (Halcro et al, 2020). The imaging system captures top-and side-view images of single seeds as they pass underneath integrated RGB cameras.…”

Section: Lentil Seed Imagingmentioning

confidence: 99%

Intelligent Characterization of Lentil Genetic Resources: Evolutionary History, Genetic Diversity of Germplasm, and the Need for Well‐Represented Collections

et al. 2021

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The genetic and phenotypic characterization of crops allows us to elucidate their evolutionary and domestication history, the genetic basis of important traits, and the use of variation present in landraces and wild relatives to enhance resilience. In this context, we aim to provide an overview of the main genetic resources developed for lentil and their main outcomes, and to suggest protocols for continued work on this important crop. Lens culinaris is the third-mostimportant cool-season grain and its use is increasing as a quick-cooking, nutritious, plant-based source of protein. L. culinaris was domesticated in the Fertile Crescent, and six additional wild taxa (L. orientalis, L. tomentosus, L. odemensis, L. lamottei, L. ervoides, and L. nigricans) are recognized. Numerous genetic diversity studies have shown that wild relatives present high levels of genetic variation and provide a reservoir of alleles that can be used for breeding programs. Furthermore, the integration of genetics/genomics and breeding techniques has resulted in identification of quantitative trait loci and genes related to attributes of interest. Genetic maps, massive genotyping, marker-assisted selection, and genomic selection are some of the genetic resources generated and applied in lentil. In addition, despite its size (∼4 Gbp) and complexity, the L. culinaris genome has been assembled, allowing a deeper understanding of its architecture. Still, major knowledge gaps exist in lentil, and a deeper understanding and characterization of germplasm resources, including wild relatives, is critical to lentil breeding and improvement.

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The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples

Cited by 29 publications

References 19 publications

Optical Coherence Tomography Applied to Non-Destructive Seed Coat Thickness Measurement

Optical Coherence Tomography Applied to Non-Destructive Seed Coat Thickness Measurement

SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination

Intelligent Characterization of Lentil Genetic Resources: Evolutionary History, Genetic Diversity of Germplasm, and the Need for Well‐Represented Collections

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