Two-dimensional semantic morphological feature extraction and atlas construction of maize ear leaves
Maize ear leaves have important roles in photosynthesis, nutrient partitioning and hormone regulation. The morphological and structural variations observed in maize ear leaves are numerous and contribute significantly to the yield. Nevertheless, research on the fine-scale morphology of maize leaves...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1520297/full |
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| author | Hongli Song Hongli Song Weiliang Wen Weiliang Wen Ying Zhang Ying Zhang Yanxin Zhao Xinyu Guo Xinyu Guo Chunjiang Zhao Chunjiang Zhao Chunjiang Zhao |
| author_facet | Hongli Song Hongli Song Weiliang Wen Weiliang Wen Ying Zhang Ying Zhang Yanxin Zhao Xinyu Guo Xinyu Guo Chunjiang Zhao Chunjiang Zhao Chunjiang Zhao |
| author_sort | Hongli Song |
| collection | DOAJ |
| description | Maize ear leaves have important roles in photosynthesis, nutrient partitioning and hormone regulation. The morphological and structural variations observed in maize ear leaves are numerous and contribute significantly to the yield. Nevertheless, research on the fine-scale morphology of maize leaves is less, particularly the quantitative methods to characterize the morphology of leaves in two-dimensional (2D) space is absent. This makes it challenging to accurately identify 2D leaf shape of their cultivars. Therefore, this study presents the methods of 2D semantic morphological feature extraction and atlas construction, with the ear leaf in silking stage of maize association analysis population serving as an example. A three-dimensional (3D) digitizer was employed to obtain data from 1,431 leaves belonging to 518 inbred lines. The data was then processed using mesh subdivision and planar parameterization to create 2D leaf models with area-preserving characteristics. Additionally, averaged 2D leaf models of all the inbred lines were constructed, and 29 2D leaf features were quantified. Based on this, 11 features were extracted as semantic features of 2D leaf shape through clustering and correlation analysis. A comprehensive 2D leaf shape indicator L2D based on the 11 semantic features was proposed, and a 2D leaf shape atlas was constructed in accordance with the L2D ordering. Inbred line identification of 2D leaf shape in maize was achieved using the atlas. The results of maize leaf inbred line identification can determine the probability that the corresponding true inbred line ranked within the top 10 of the predicted results is 0.706, within the top 20 is 0.810, and within the top 45 is 0.900. This enables the generation of the corresponding maize 2D leaf shape through the matching of semantic features. The methodology presented in this study offers novel insights into the construction of semantic models for the morphology of maize and the identification of cultivars. It also provides a theoretical and technical foundation for the generation and drawing the leaf shape based on semantic 2D morphological and structural features. |
| format | Article |
| id | doaj-art-813bdfe0806a45ea94ba8358a01a3224 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-813bdfe0806a45ea94ba8358a01a32242025-02-12T07:26:26ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-02-011610.3389/fpls.2025.15202971520297Two-dimensional semantic morphological feature extraction and atlas construction of maize ear leavesHongli Song0Hongli Song1Weiliang Wen2Weiliang Wen3Ying Zhang4Ying Zhang5Yanxin Zhao6Xinyu Guo7Xinyu Guo8Chunjiang Zhao9Chunjiang Zhao10Chunjiang Zhao11College of Information Engineering, Northwest A&F University, Yangling, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, ChinaBeijing Key Laboratory of Maize DNA (DeoxyriboNucleic Acid) Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, ChinaCollege of Information Engineering, Northwest A&F University, Yangling, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaBeijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, ChinaMaize ear leaves have important roles in photosynthesis, nutrient partitioning and hormone regulation. The morphological and structural variations observed in maize ear leaves are numerous and contribute significantly to the yield. Nevertheless, research on the fine-scale morphology of maize leaves is less, particularly the quantitative methods to characterize the morphology of leaves in two-dimensional (2D) space is absent. This makes it challenging to accurately identify 2D leaf shape of their cultivars. Therefore, this study presents the methods of 2D semantic morphological feature extraction and atlas construction, with the ear leaf in silking stage of maize association analysis population serving as an example. A three-dimensional (3D) digitizer was employed to obtain data from 1,431 leaves belonging to 518 inbred lines. The data was then processed using mesh subdivision and planar parameterization to create 2D leaf models with area-preserving characteristics. Additionally, averaged 2D leaf models of all the inbred lines were constructed, and 29 2D leaf features were quantified. Based on this, 11 features were extracted as semantic features of 2D leaf shape through clustering and correlation analysis. A comprehensive 2D leaf shape indicator L2D based on the 11 semantic features was proposed, and a 2D leaf shape atlas was constructed in accordance with the L2D ordering. Inbred line identification of 2D leaf shape in maize was achieved using the atlas. The results of maize leaf inbred line identification can determine the probability that the corresponding true inbred line ranked within the top 10 of the predicted results is 0.706, within the top 20 is 0.810, and within the top 45 is 0.900. This enables the generation of the corresponding maize 2D leaf shape through the matching of semantic features. The methodology presented in this study offers novel insights into the construction of semantic models for the morphology of maize and the identification of cultivars. It also provides a theoretical and technical foundation for the generation and drawing the leaf shape based on semantic 2D morphological and structural features.https://www.frontiersin.org/articles/10.3389/fpls.2025.1520297/fullmaizetwo-dimensionalleaf shapephenotypingsemantic features |
| spellingShingle | Hongli Song Hongli Song Weiliang Wen Weiliang Wen Ying Zhang Ying Zhang Yanxin Zhao Xinyu Guo Xinyu Guo Chunjiang Zhao Chunjiang Zhao Chunjiang Zhao Two-dimensional semantic morphological feature extraction and atlas construction of maize ear leaves Frontiers in Plant Science maize two-dimensional leaf shape phenotyping semantic features |
| title | Two-dimensional semantic morphological feature extraction and atlas construction of maize ear leaves |
| title_full | Two-dimensional semantic morphological feature extraction and atlas construction of maize ear leaves |
| title_fullStr | Two-dimensional semantic morphological feature extraction and atlas construction of maize ear leaves |
| title_full_unstemmed | Two-dimensional semantic morphological feature extraction and atlas construction of maize ear leaves |
| title_short | Two-dimensional semantic morphological feature extraction and atlas construction of maize ear leaves |
| title_sort | two dimensional semantic morphological feature extraction and atlas construction of maize ear leaves |
| topic | maize two-dimensional leaf shape phenotyping semantic features |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1520297/full |
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