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Use of machine vision to decipher the genetic basis of potato tuber characteristics in a tetraploid biparental linkage mapping population
  • +9
  • Max Feldman,
  • Jaebum Park,
  • Max J Feldman,
  • Nathan Miller,
  • Collins Wakholi,
  • Katelyn Greene,
  • Arash Abbasi,
  • Devin Rippner,
  • Duroy Navarre,
  • Cari Schmitz-Carley,
  • Laura Shannon,
  • Rich Novy
Max Feldman

Corresponding Author:max.feldman@usda.gov

Author Profile
Jaebum Park
Small Grains and Potato Germplasm Research Unit USDA -Agricultural Research Service Aberdeen
Max J Feldman
Temperate Tree Fruit and Vegetable Research Unit USDA -Agricultural Research Service Prosser
Nathan Miller
Department of Botany, University of Wisconsin-Madison Madison
Collins Wakholi
Horticultural Crops Production and Genetic Improvement Research Unit USDA -Agricultural Research Service Prosser
Katelyn Greene
Temperate Tree Fruit and Vegetable Research Unit USDA -Agricultural Research Service Prosser
Arash Abbasi
The Beacom College of Computer and Cyber Sciences, Dakota State University Madison
Devin Rippner
Horticultural Crops Production and Genetic Improvement Research Unit USDA -Agricultural Research Service Prosser
Duroy Navarre
Temperate Tree Fruit and Vegetable Research Unit USDA -Agricultural Research Service Prosser
Cari Schmitz-Carley
Aardevo B.V. Boise
Laura Shannon
Department of Horticultural Sciences, University of Minnesota Minneapolis-St
Rich Novy
Small Grains and Potato Germplasm Research Unit USDA -Agricultural Research Service Aberdeen

Abstract

ORCiD: [ORCiD of presenting author] Jaebum Park [0000-0001-6459-909X] AND/OR Max Feldman [0000-0002-5415-4326]
Tuber size and shape, colorimetric characteristics of tuber skin and flesh, and tuber defect susceptibility are all factors that influence the adoption of potato cultivars. Despite the importance of these characteristics, our understanding of their inheritance is limited by our inability to precisely measure these features on the scale needed to evaluate breeding populations. To alleviate this bottleneck, we have developed a low-cost, semi-automated workflow to capture data and quantify each of these characteristics using machine vision. This workflow was applied to assess the phenotypic variation present within 189 F1 progeny of the A08241 breeding population and map the genetic basis of tuber characteristics. Several medium-to-large effect, quantitative trait loci (QTL) were found to be associated with different measurements of tuber shape. These results indicate that quantitative measurements acquired using machine vision methods are reliable, heritable, and can be used to map and select upon multiple traits simultaneously in structured potato breeding populations.
24 Oct 2022Submitted to NAPPN 2023 Abstracts
29 Oct 2022Published in NAPPN 2023 Abstracts