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.