이미지 기반 유전자원 디지털화: 한계와 극복

Image-Based Digitalization of Germplasm Stock: Overcoming its limitations

Image-based digitalization of germplasm stock holds significant promise for acceleratingplant breeding and crop improvement. This technology facilitates efficientgermplasm characterization, evaluation, and management through the capture and analysisof visual phenotypes. However, widespread adoption is hindered by challenges that includeimage quality control, data analysis complexity, and phenotypic representationlimitations. This study investigated these constraints and proposed strategies to addressthem. By managing technical challenges, refining phenotypic data extraction, and developingrobust data analysis pipelines, researchers can fully leverage image-based digitalizationto enhance germplasm utilization and contribute to sustainable agriculture.