Image-based digitalization of germplasm stock holds significant promise for accelerating plant breeding and crop improvement. This technology facilitates efficient germplasm characterization, evaluation, and management through the capture and analysis of visual phenotypes. However, widespread adoption is hindered by challenges that include image quality control, data analysis complexity, and phenotypic representation limitations. This study investigated these constraints and proposed strategies to address them. By managing technical challenges, refining phenotypic data extraction, and developing robust data analysis pipelines, researchers can fully leverage image-based digitalization to enhance germplasm utilization and contribute to sustainable agriculture.

The breeding
objective
for crops changes constantly due to environmental changes, customer requirements, and farming convenience, among other factors. As breeding takes a long time, suitable mating candidates must be obtained from the beginning. In this study, we propose a breeder-friendly system and datasets. We investigated 11 traits that affect adaptation to the cultivation environment in Jeju Island using 27 buckwheat germplasms and selected good germplasms for breeding new cultivars. Data on the various traits of the target crop could assist breeders or researchers in identifying the traits of each genetic resource that will facilitate the development of new cultivars that suit the requirements of customers. Therefore, this study suggests establishing a breeder-friendly system and datasets in GenBank.