A Low-Cost Automated System for High-Throughput Phenotyping of Single Oat Seeds

Abstract

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 ( 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.