图像识别技术在杏品种资源叶片形态评估与多样性研究中的应用

Abstract:

To explore the genetic diversity of apricot leaf morphology and facilitate the collection of apricot germplasm resources and variety identification， this study investigated 28 leaf phenotypic traits across 142 germplasm resources. Leaf morphological characteristics were rapidly evaluated using picture recognition technology. The results of phenotypic trait diversity analysis showed the coefficients of variation （CV） of the 13 qualitative and 8 quantitative traits were higher than 10%， indicating rich genetic diversity on leaf morphology in the collection. Correlation analysis revealed highly significant positive correlations among leaf area， leaf perimeter， leaf length， and leaf width； highly significant negative correlations between leaf base shape， leaf base angle， and leaf length/width； and significant negative correlations between leaf roundness and leaf tip length. Principal component analysis indicated that the first principal component， consisting of leaf area， leaf circumference， leaf length， leaf width， and petiole length， mainly reflected traits related to leaf blade size. The second principal component reflected traits related to leaf blade color， while the third and fourth principal components reflected traits related to leaf blade shape and blade sawtooth height， respectively. Cluster analysis broadly classified these samples into eight taxa， including large-leafed-cuneate， small-leafed-light green， large-leafed-dark green， small-leafed-dark green， small-leafed-rounded， large-leafed-cordate， large-leafed-truncate， and mixed taxa， and the Ⅷ taxon was divided into six subgroups. Collectively， this study suggested three qualitative traits， leaf tip length， leaf margin sawtooth depth， and widest position of the leaf， to identify different apricot varieties， thus providing a reference for future evaluations of leaf morphology and variety recognition of apricot germplasm resources in China.

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