A scalable, low‐cost phenotyping strategy to assess tuber size, shape, and the colorimetric features of tuber skin and flesh in potato breeding populations
Abstract Tuber size, shape, colorimetric characteristics, and defect susceptibility are all factors that influence the acceptance of new potato cultivars. Despite the importance of these characteristics, our understanding of their inheritance is substantially limited by our inability to precisely me...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
|
| Series: | Plant Phenome Journal |
| Online Access: | https://doi.org/10.1002/ppj2.20099 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850120142035353600 |
|---|---|
| author | Max J. Feldman Jaebum Park Nathan Miller Collins Wakholi Katelyn Greene Arash Abbasi Devin A. Rippner Duroy Navarre Cari Schmitz Carley Laura M. Shannon Rich Novy |
| author_facet | Max J. Feldman Jaebum Park Nathan Miller Collins Wakholi Katelyn Greene Arash Abbasi Devin A. Rippner Duroy Navarre Cari Schmitz Carley Laura M. Shannon Rich Novy |
| author_sort | Max J. Feldman |
| collection | DOAJ |
| description | Abstract Tuber size, shape, colorimetric characteristics, and defect susceptibility are all factors that influence the acceptance of new potato cultivars. Despite the importance of these characteristics, our understanding of their inheritance is substantially limited by our inability to precisely measure these features quantitatively on the scale needed to evaluate breeding populations. To alleviate this bottleneck, we developed a low‐cost, semiautomated workflow to capture data and measure 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. Machine vision was applied to estimate linear and volumetric tuber size, assess tuber shape characteristics using aspect ratio and biomass profiles, and quantify tuber skin and flesh color; additionally, a deep learning mode was developed to classify the presence of hollow‐heart defect. Our results provide an example of quantitative measurements acquired using machine vision methods that are reliable, heritable, and capable of being used to understand and select multiple traits simultaneously in structured potato breeding populations. |
| format | Article |
| id | doaj-art-2df6cf6a55c242e6a341d5c0aad605c3 |
| institution | OA Journals |
| issn | 2578-2703 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Plant Phenome Journal |
| spelling | doaj-art-2df6cf6a55c242e6a341d5c0aad605c32025-08-20T02:35:26ZengWileyPlant Phenome Journal2578-27032024-12-0171n/an/a10.1002/ppj2.20099A scalable, low‐cost phenotyping strategy to assess tuber size, shape, and the colorimetric features of tuber skin and flesh in potato breeding populationsMax J. Feldman0Jaebum Park1Nathan Miller2Collins Wakholi3Katelyn Greene4Arash Abbasi5Devin A. Rippner6Duroy Navarre7Cari Schmitz Carley8Laura M. Shannon9Rich Novy10USDA‐ARS, Temperate Tree Fruit and Vegetable Research Unit Prosser Washington USAUSDA‐ARS, Small Grains and Potato Germplasm Research Unit Aberdeen Idaho USADepartment of Botany University of Wisconsin–Madison Madison Wisconsin USAUSDA‐ARS, Horticultural Crops Production and Genetic Improvement Research Unit Prosser Washington USAUSDA‐ARS, Temperate Tree Fruit and Vegetable Research Unit Prosser Washington USAThe Beacom College of Computer and Cyber Sciences Dakota State University Madison South Dakota USAUSDA‐ARS, Horticultural Crops Production and Genetic Improvement Research Unit Prosser Washington USAUSDA‐ARS, Temperate Tree Fruit and Vegetable Research Unit Prosser Washington USAAardevo North America, LLC Boise Idaho USADepartment of Horticultural Sciences University of Minnesota St. Paul Minnesota USAUSDA‐ARS, Small Grains and Potato Germplasm Research Unit Aberdeen Idaho USAAbstract Tuber size, shape, colorimetric characteristics, and defect susceptibility are all factors that influence the acceptance of new potato cultivars. Despite the importance of these characteristics, our understanding of their inheritance is substantially limited by our inability to precisely measure these features quantitatively on the scale needed to evaluate breeding populations. To alleviate this bottleneck, we developed a low‐cost, semiautomated workflow to capture data and measure 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. Machine vision was applied to estimate linear and volumetric tuber size, assess tuber shape characteristics using aspect ratio and biomass profiles, and quantify tuber skin and flesh color; additionally, a deep learning mode was developed to classify the presence of hollow‐heart defect. Our results provide an example of quantitative measurements acquired using machine vision methods that are reliable, heritable, and capable of being used to understand and select multiple traits simultaneously in structured potato breeding populations.https://doi.org/10.1002/ppj2.20099 |
| spellingShingle | Max J. Feldman Jaebum Park Nathan Miller Collins Wakholi Katelyn Greene Arash Abbasi Devin A. Rippner Duroy Navarre Cari Schmitz Carley Laura M. Shannon Rich Novy A scalable, low‐cost phenotyping strategy to assess tuber size, shape, and the colorimetric features of tuber skin and flesh in potato breeding populations Plant Phenome Journal |
| title | A scalable, low‐cost phenotyping strategy to assess tuber size, shape, and the colorimetric features of tuber skin and flesh in potato breeding populations |
| title_full | A scalable, low‐cost phenotyping strategy to assess tuber size, shape, and the colorimetric features of tuber skin and flesh in potato breeding populations |
| title_fullStr | A scalable, low‐cost phenotyping strategy to assess tuber size, shape, and the colorimetric features of tuber skin and flesh in potato breeding populations |
| title_full_unstemmed | A scalable, low‐cost phenotyping strategy to assess tuber size, shape, and the colorimetric features of tuber skin and flesh in potato breeding populations |
| title_short | A scalable, low‐cost phenotyping strategy to assess tuber size, shape, and the colorimetric features of tuber skin and flesh in potato breeding populations |
| title_sort | scalable low cost phenotyping strategy to assess tuber size shape and the colorimetric features of tuber skin and flesh in potato breeding populations |
| url | https://doi.org/10.1002/ppj2.20099 |
| work_keys_str_mv | AT maxjfeldman ascalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT jaebumpark ascalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT nathanmiller ascalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT collinswakholi ascalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT katelyngreene ascalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT arashabbasi ascalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT devinarippner ascalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT duroynavarre ascalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT carischmitzcarley ascalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT lauramshannon ascalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT richnovy ascalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT maxjfeldman scalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT jaebumpark scalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT nathanmiller scalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT collinswakholi scalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT katelyngreene scalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT arashabbasi scalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT devinarippner scalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT duroynavarre scalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT carischmitzcarley scalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT lauramshannon scalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations AT richnovy scalablelowcostphenotypingstrategytoassesstubersizeshapeandthecolorimetricfeaturesoftuberskinandfleshinpotatobreedingpopulations |