ArticlePlant phenomics (Washington, D.C.)2024
3D Morphological Feature Quantification and Analysis of Corn Leaves.
Article in Plant phenomics (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Integrating 3D phenotyping and functional-structural plant models for crop ideotype breeding.Nature communications · 2026Review
- 3D point cloud driven organ semantic segmentation to assess maize structural responses along the planting-density gradient.Plant phenomics (Washington, D.C.) · 2026Article
- Quantification of lettuce leaf DUS test traits and phenotypic fingerprint construction for variety identification.Plant phenomics (Washington, D.C.) · 2026Article
- F2DMAS: a smartphone video-based 3D phenotyping workflow for potted plants in complex backgrounds.Frontiers in plant science · 2026Article
- Two-dimensional semantic morphological feature extraction and atlas construction of maize ear leaves.Frontiers in plant science · 2025Article
- Combining multispectral and high-resolution 3D imaging for leaf vein segmentation and density measurement.Frontiers in plant science · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
Marked variations in the 3-dimensional (3D) shape of corn leaves can be discerned as a function of various influences, including genetics, environmental factors, and the management of cultivation processes. However, the causes of these variations remain unclear, primarily due to the absence of quantitative methods to describe the 3D spatial morphology of leaves. To address this issue, this study acquired 3D digitized data of ear-position leaves from 478 corn inbred lines during the grain-filling stage. We propose quantitative calculation methods for 13 3D leaf shape features, such as the leaf length, 3D leaf area, leaf inclination angle, blade-included angle, blade self-twisting, blade planarity, and margin amplitude. Correlation analysis, cluster analysis, and heritability analysis were conducted among the 13 leaf traits. Leaf morphology differences among subpopulations of the inbred lines were also analyzed. The results revealed that the 3D leaf traits are capable of revealing the morphological differences among different leaf surfaces, and the genetic analysis revealed that 84.62% of the 3D phenotypic traits of ear-position leaves had a heritability greater than 0.3. However, the majority of 3D leaf shape traits were strongly affected by environmental conditions. Overall, this study quantitatively investigated 3D leaf shape in corn, providing a reliable basis for further research on the genetic regulation of corn leaf morphology and advancing the understanding of the complex interplay among crop genetics, phenotypes, and the environment.
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Registered trials
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