Two-dimensional semantic morphological feature extraction and atlas construction of maize ear leaves

• DOI: 10.3389/fpls.2025.1520297
• Publication Date: 2025-02-12T07:29:56Z
• Authors: Hongli Song; Weiliang Wen; Ying Zhang; Yanxin Zhao; Xinyu Guo; Chunjiang Zhao
• Keywords: Inbred strain

Maize ear leaves have important roles in photosynthesis, nutrient partitioning and hormone regulation. The morphological structural variations observed maize are numerous contribute significantly to the yield. Nevertheless, research on fine-scale morphology of is less, particularly quantitative methods characterize two-dimensional (2D) space absent. This makes it challenging accurately identify 2D leaf shape their cultivars. Therefore, this study presents semantic feature extraction atlas construction, with silking stage association analysis population serving as an example. A three-dimensional (3D) digitizer was employed obtain data from 1,431 belonging 518 inbred lines. then processed using mesh subdivision planar parameterization create models area-preserving characteristics. Additionally, averaged all lines were constructed, 29 features quantified. Based this, 11 extracted through clustering correlation analysis. comprehensive indicator L 2 D based proposed, a constructed accordance ordering. Inbred line identification achieved atlas. results can determine probability that corresponding true ranked within top 10 predicted 0.706, 20 0.810, 45 0.900. enables generation matching features. methodology presented offers novel insights into construction for It also provides theoretical technical foundation drawing