A 3D Shape-Measuring System for Assessing Strawberry Fruits

Kochi, Nobuo; Tanabata, Takanari; Hayashi, Atsushi; Isobe, Sachiko

doi:10.20965/ijat.2018.p0395

Cited by 17 publications

(19 citation statements)

References 21 publications

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“…We report for the first time a robust method to measure strawberry shape uniformity and apply this technique to generate genetic markers for uniformity traits. Several studies have attempted to quantify strawberry fruit shape using 2D images with neural networks 48 , 3D imaging 49 and by machine learning 15 . However, none of these studies investigated berry uniformity.…”

Section: Discussionmentioning

confidence: 99%

Defining strawberry shape uniformity using 3D imaging and genetic mapping

Li¹,

Cockerton²,

Johnson³

et al. 2020

Hortic Res

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Strawberry shape uniformity is a complex trait, influenced by multiple genetic and environmental components. To complicate matters further, the phenotypic assessment of strawberry uniformity is confounded by the difficulty of quantifying geometric parameters 'by eye' and variation between assessors. An in-depth genetic analysis of strawberry uniformity has not been undertaken to date, due to the lack of accurate and objective data. Nonetheless, uniformity remains one of the most important fruit quality selection criteria for the development of a new variety. In this study, a 3D-imaging approach was developed to characterise berry shape uniformity. We show that circularity of the maximum circumference had the closest predictive relationship with the manual uniformity score. Combining five or six automated metrics provided the best predictive model, indicating that human assessment of uniformity is highly complex. Furthermore, visual assessment of strawberry fruit quality in a multi-parental QTL mapping population has allowed the identification of genetic components controlling uniformity. A "regular shape" QTL was identified and found to be associated with three uniformity metrics. The QTL was present across a wide array of germplasm, indicating a potential candidate for marker-assisted breeding, while the potential to implement genomic selection is explored. A greater understanding of berry uniformity has been achieved through the study of the relative impact of automated metrics on human perceived uniformity. Furthermore, the comprehensive definition of strawberry shape uniformity using 3D imaging tools has allowed precision phenotyping, which has improved the accuracy of trait quantification and unlocked the ability to accurately select for uniform berries.

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Section: Discussionmentioning

confidence: 99%

Defining strawberry shape uniformity using 3D imaging and genetic mapping

Li¹,

Cockerton²,

Johnson³

et al. 2020

Hortic Res

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show abstract

“…The new Fourier descriptor proposed by Uesaka (1984) can be applied to open curves, and is valuable for quantification of partial shape. In addition, He et al (2017) and Kochi et al (2018) measured strawberry fruit three-dimensionally. 3D measurement provides more information about the shape of strawberry such as volume, surface condition, attitude of sepal.…”

Section: Discussionmentioning

confidence: 99%

Strawberry fruit shape: quantification by image analysis and QTL detection by genome-wide association analysis

et al. 2021

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Fruit shape of cultivated strawberry ( Fragaria × ananassa Duch.) is an important breeding target. To detect genomic regions associated with this trait, its quantitative evaluation is needed. Previously we created a multi-parent advanced-generation inter-cross (MAGIC) strawberry population derived from six founder parents. In this study, we used this population to quantify fruit shape. Elliptic Fourier descriptors (EFDs) were generated from 2 969 two-dimensional binarized fruit images, and principal component (PC) scores were calculated on the basis of the EFD coefficients. PC1–PC3 explained 96% of variation in shape and thus adequately quantified it. In genome-wide association study, the PC scores were used as phenotypes. Genome wide association study using mixed linear models revealed 2 quantitative trait loci (QTLs) for fruit shape. Our results provide a novel and effective method to analyze strawberry fruit morphology; the detected QTLs and presented method can support marker-assisted selection in practical breeding programs to improve fruit shape.

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“…We report for the first time a robust method to measure strawberry uniformity and apply this technique to generate genetic markers for uniformity traits. Several studies have attempted to quantify strawberry fruit shape using 2D images with neural networks 44 , 3D imaging 45 and by machine learning 15 . However, none of these studies investigated berry uniformity.…”

Section: Discussionmentioning

confidence: 99%

Defining Strawberry Uniformity using 3D Imaging and Genetic Mapping

Cockerton²,

Johnson³

et al. 2020

Preprint

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Strawberry uniformity is a complex trait, influenced by multiple genetic and environmental components. To complicate matters further, the phenotypic assessment of strawberry uniformity is confounded by the difficulty of quantifying geometric parameters ‘by eye’ and variation between assessors. An in-depth genetic analysis of strawberry uniformity has not been undertaken to date, due to the lack of accurate and objective data. Nonetheless, uniformity remains one of the most important fruit quality selection criteria for the development of a new variety. In this study, a 3D-imaging approach was developed to characterise berry uniformity. We show that circularity of the maximum circumference had the closest predictive relationship with the manual uniformity score. Combining five or six automated metrics provided the best predictive model, indicating that human assessment of uniformity is highly complex. Furthermore, visual assessment of strawberry fruit quality in a multi-parental QTL mapping population has allowed the identification of genetic components controlling uniformity. A “regular shape” QTL was identified and found to be associated with three uniformity metrics. The QTL was present across a wide array of germplasm, indicating a strong candidate for marker-assisted breeding. A greater understanding of berry uniformity has been achieved through the study of the relative impact of automated metrics on human perceived uniformity. Furthermore, the comprehensive definition of strawberry uniformity using 3D imaging tools has allowed precision phenotyping, which has improved the accuracy of trait quantification. This tool has allowed us to illustrate the use of advanced image analysis towards the breeding of greater uniformity in strawberry.

show abstract

A 3D Shape-Measuring System for Assessing Strawberry Fruits

Cited by 17 publications

References 21 publications

Defining strawberry shape uniformity using 3D imaging and genetic mapping

Defining strawberry shape uniformity using 3D imaging and genetic mapping

Strawberry fruit shape: quantification by image analysis and QTL detection by genome-wide association analysis

Defining Strawberry Uniformity using 3D Imaging and Genetic Mapping

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