TubAR: an R Package for Quantifying Tuber Shape and Skin Traits from Images

Michael D. Miller, Cari A. Schmitz Carley, Rachel A. Figueroa, Max J. Feldman, Darrin Haagenson, Laura M. Shannon

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Potato market value is heavily affected by tuber quality traits such as shape, color, and skinning. Despite this, potato breeders often rely on subjective scales that fail to precisely define phenotypes. Individual human evaluators and the environments in which ratings are taken can bias visual quality ratings. Collecting quality trait data using machine vision allows for precise measurements that will remain reliable between evaluators and breeding programs. Here we present TubAR (Tuber Analysis in R), an image analysis program designed to collect data for multiple tuber quality traits at low cost to breeders. To assess the efficacy of TubAR in comparison to visual scales, red-skinned potatoes were evaluated using both methods. Broad sense heritability was consistently higher for skinning, roundness, and length to width ratio using TubAR. TubAR collects essential data on fresh market potato breeding populations while maintaining efficiency by measuring multiple traits through one phenotyping protocol.

Original languageEnglish (US)
Pages (from-to)52-62
Number of pages11
JournalAmerican Journal of Potato Research
Volume100
Issue number1
DOIs
StatePublished - Feb 2023

Bibliographical note

Funding Information:
This work was funded by USDA-ARS 58-3060-0-012 AMD 1, USDA-NIFA 2016-34141-25707, USDA-NIFA 2019-34141-30284, and the Minnesota Department of Agriculture.

Publisher Copyright:
© 2022, The Author(s).

Keywords

  • Image analysis
  • Phenotyping
  • Quality traits
  • Red
  • Skin color
  • Skinning

Fingerprint

Dive into the research topics of 'TubAR: an R Package for Quantifying Tuber Shape and Skin Traits from Images'. Together they form a unique fingerprint.

Cite this