|CLOHESSY, JAMES - Cornell University|
|PAULI, DUKE - Cornell University|
|KREHER, KEVIN - Cornell University|
|BUCKLER V, EDWARD - Cornell University|
|WU, TINGTING - Northwest Agricultural & Forestry University|
|HOEKENGA, OWEN - Cayuga Genetics Consulting Group, Llc|
|SORRELLS, MARK - Cornell University|
|GORE, MICHAEL - Cornell University|
Submitted to: The Plant Phenome Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/9/2018
Publication Date: 12/6/2018
Citation: Clohessy, J.W., Pauli, D., Kreher, K.M., Buckler IV, E.S., Armstrong, P.R., Wu, T., Hoekenga, O.A., Jannink, J., Sorrells, M.E., Gore, M.A. 2018. A low-cost automated system for high-throughput phenotyping of single oat seeds. The Plant Phenome Journal. 1(1):1-13. https://doi.org/10.2135/tppj2018.07.0005.
Interpretive Summary: Efforts focused on the genetic improvement of seed dimensional and color traits would greatly benefit from efficient and reliable measurement systems. Color is an important attribute as it is important for market class and consumer acceptance. Physical size and shape is important in milling operations affecting final product size and yield. Several seed systems exist but none combine the cost-effectiveness, high-throughput, and accuracy needed for plant breeding programs. A two camera imaging system was integrated into a single-seed analyzer (SSA) system that also measures weight by electronic balance and seed composition using near-infrared reflectance (NIR). Seed length, width, height, volume, and color for five contrasting oat genotypes were measured with the cameras. The image-based measurements, except for color, compared favorably with manual measurements. These results demonstrate that the SSA system has the potential to provide a low-cost solution for the rapid, accurate measurement of physical traits on individual seeds of oat and other small grains, allowing for better screening of seeds under development in breeding programs.
Technical Abstract: Efforts focused on the genetic improvement of seed morphometric and color traits would greatly benefit from efficient and reliable quantitative phenotypic assessment. Although several seed phenotyping systems exist, none of them combine the cost-effectiveness, throughput, and accuracy needed for implementation in plant breeding programs. 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 seed length, width, height, volume, and color for 96 individual seeds of five contrasting oat genotypes replicated over six days. With the exception of color, the image-based traits, as well as seed weight were found to be strongly correlated with and have repeatabilities 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 other small grains, allowing for the screening of seeds from numerous genotypes in breeding programs.