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.

Pear (Pyrus spp.) is an economically important fruit tree that grows extensively worldwide. To facilitate the identification of agronomically important traits and provide new information for genetic and genomic research concerning this fruit tree, a high-density genetic linkage map of pear was constructed using 178 F₁ populations derived from a cross between ‘Manpungbae’ and ‘Oharabeni’. Single nucleotide polymorphisms (SNPs) detected by genotyping-by-sequencing (GBS) and simple sequence repeats (SSRs) developed from pears were analyzed to construct a genetic linkage map. SSR markers were used to locate the corresponding chromosome number for each linkage group (LG). A total of 1,807 GBS-SNPs and 41 SSRs were anchored to the integrated genetic linkage map. Seventeen LGs were identified, covering a genetic distance of 1,519.4 cM with an average marker density of 0.87 cM. The lengths of the LGs ranged from 70.9 cM (LG 14) to 160.4 cM (LG 15). Each LG had SSR markers from 1 to 5, except for LGs 7, 8, and 9. Our integrated genetic map of pear could be used as a basic frame map for comparative analysis of genomic structure between different pear research groups.