TY - JOUR
T1 - A Low-Cost Automated System for High-Throughput Phenotyping of Single Oat Seeds
AU - Clohessy, James W.
AU - Pauli, Duke
AU - Kreher, Kevin M.
AU - Buckler, Edward S.
AU - Armstrong, Paul R.
AU - Wu, Tingting
AU - Hoekenga, Owen A.
AU - Jannink, Jean Luc
AU - Sorrells, Mark E.
AU - Gore, Michael A.
N1 - Funding Information:
This research was supported by PepsiCo Global R&D, the USDA–ARS, USDA‐NIFA‐AFRI 2011‐68002‐30029 (Triticeae‐CAP), USDA‐NIFA‐AFRI 2017‐67007‐25939 (Wheat‐CAP), USDA‐NIFA‐AFRI 2017‐67007‐26502, Cotton Incorporated, and Hatch Project 149‐447.
Funding Information:
This research was supported by PepsiCo Global R&D, the USDA?ARS, USDA-NIFA-AFRI 2011-68002-30029 (Triticeae-CAP), USDA-NIFA-AFRI 2017-67007-25939 (Wheat-CAP), USDA-NIFA-AFRI 2017-67007-26502, Cotton Incorporated, and Hatch Project 149-447. We especially thank Matthew Ming, Nicholas Kaczmar, Fang Liu, Han Rongkui, Matt Baseggio, and Yujie Meng for providing excellent technical expertise in the collection of phenotypic data through the SSA and manual measurements.
Publisher Copyright:
Copyright © American Society of Agronomy and Crop Science Society of America
PY - 2018/1
Y1 - 2018/1
N2 - Core Ideas: A single-seed analyzer system was modified to provide low-cost seed imaging. The system throughput allows rapid, nondestructive phenotyping of single seeds. Accuracy of seed shape and size measurements were similar to those obtained manually. Repeatability of morphometric seed traits was higher than that of seed color traits. Efforts focused on the genetic improvement of seed morphometric and color traits would greatly benefit from efficient and reliable quantitative phenotypic assessment in a nondestructive manner. Although several seed phenotyping systems exist, none of them combine the cost effectiveness, identity preservation, throughput, and accuracy needed for implementation in plant breeding. We integrated an image analysis component into a single-seed analyzer (SSA) system that also captures near-infrared reflectance (NIR) and weight data. Through the development and utilization of an open-source computational image analysis pipeline, image data acquired by two cameras mounted on the SSA machine were automatically processed to derive estimates of length, width, height, volume, and color for 96 individual dehulled groats (seeds) of five oat (Avena sativa L.) genotypes replicated across days. With the exception of color, the four traits were found to be strongly correlated with and have repeatability comparable to manual measurements. The seed color values had moderately strong correlation with those measured by a colorimeter, but further improvements to the SSA system are needed to increase measurement accuracy. These results demonstrate that the SSA system has the potential to provide a low-cost solution for the rapid, accurate measurement of morphological traits on individual seeds of oat and potentially other crop species, allowing the screening of seeds from numerous genotypes in breeding programs.
AB - Core Ideas: A single-seed analyzer system was modified to provide low-cost seed imaging. The system throughput allows rapid, nondestructive phenotyping of single seeds. Accuracy of seed shape and size measurements were similar to those obtained manually. Repeatability of morphometric seed traits was higher than that of seed color traits. Efforts focused on the genetic improvement of seed morphometric and color traits would greatly benefit from efficient and reliable quantitative phenotypic assessment in a nondestructive manner. Although several seed phenotyping systems exist, none of them combine the cost effectiveness, identity preservation, throughput, and accuracy needed for implementation in plant breeding. We integrated an image analysis component into a single-seed analyzer (SSA) system that also captures near-infrared reflectance (NIR) and weight data. Through the development and utilization of an open-source computational image analysis pipeline, image data acquired by two cameras mounted on the SSA machine were automatically processed to derive estimates of length, width, height, volume, and color for 96 individual dehulled groats (seeds) of five oat (Avena sativa L.) genotypes replicated across days. With the exception of color, the four traits were found to be strongly correlated with and have repeatability comparable to manual measurements. The seed color values had moderately strong correlation with those measured by a colorimeter, but further improvements to the SSA system are needed to increase measurement accuracy. These results demonstrate that the SSA system has the potential to provide a low-cost solution for the rapid, accurate measurement of morphological traits on individual seeds of oat and potentially other crop species, allowing the screening of seeds from numerous genotypes in breeding programs.
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U2 - 10.2135/tppj2018.07.0005
DO - 10.2135/tppj2018.07.0005
M3 - Article
AN - SCOPUS:85074206677
SN - 2578-2703
VL - 1
SP - 1
EP - 13
JO - Plant Phenome Journal
JF - Plant Phenome Journal
IS - 1
ER -