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ShinyFruit: interactive fruit phenotyping software and its application in blackberry.

T Mason Chizk1, Jackie A Lee1, John R Clark1

  • 1Department of Horticulture, University of Arkansas, Fayetteville, AR, United States.

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|October 23, 2023
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Summary

ShinyFruit, an R-based web app, offers efficient image-based phenotyping for horticultural breeding. It accurately measures blackberry size and color, aiding in genomic selection and breeding strategies.

Keywords:
Rubus subgenus Rubusimage analysispostharvest qualityred cell regressionred drupelet reversionreddening

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Area of Science:

  • Horticultural Science
  • Plant Breeding
  • Bioinformatics

Background:

  • Horticultural plant breeding requires extensive phenotypic data for quality assessment.
  • High-throughput phenotyping pipelines are crucial for advanced breeding strategies like genome-wide association studies and genomic selection.

Purpose of the Study:

  • To introduce ShinyFruit, an R-based web application for image-based phenotyping of fruit and vegetable traits.
  • To evaluate ShinyFruit's accuracy in measuring blackberry size and red drupelet reversion (RDR) compared to traditional methods.

Main Methods:

  • Developed an R-based web application, ShinyFruit, for automated image analysis.
  • Compared ShinyFruit's measurements of blackberry length, width, and RDR with ImageJ and manual measurements.
  • Utilized a population of blackberry cultivars and breeding selections.

Main Results:

  • ShinyFruit showed strong positive correlations with manual measurements for blackberry length (r = 0.96).
  • High correlation was observed between ShinyFruit and ImageJ for RDR estimates (r = 0.96).
  • Weaker correlations were found for manual RDR estimation (r = 0.62–0.70); no genotypic differences in width were detected.

Conclusions:

  • ShinyFruit provides a viable, open-source solution for efficient phenotyping of size and color in blackberry fruit.
  • The application's adjustable settings enhance its utility for diverse fruits and vegetables.
  • Further studies may strengthen correlations by maximizing genotypic variance for traits like RDR.