Precise 3D geometric phenotyping and phenotype interaction network construction of maize kernels
Accurate identification of maize kernel morphology is crucial for breeding and quality improvement. Traditional manual methods are limited in dealing with complex structures and cannot fully capture kernel characteristics from a phenome perspective. To address this, our study aims to develop a high-...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1438594/full |
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| _version_ | 1850152394958045184 |
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| author | Shuaihao Zhao Shuaihao Zhao Guanmin Huang Guanmin Huang Si Yang Si Yang Chuanyu Wang Chuanyu Wang Juan Wang Juan Wang Yanxin Zhao Minxiao Duan Ying Zhang Ying Zhang Xinyu Guo Xinyu Guo |
| author_facet | Shuaihao Zhao Shuaihao Zhao Guanmin Huang Guanmin Huang Si Yang Si Yang Chuanyu Wang Chuanyu Wang Juan Wang Juan Wang Yanxin Zhao Minxiao Duan Ying Zhang Ying Zhang Xinyu Guo Xinyu Guo |
| author_sort | Shuaihao Zhao |
| collection | DOAJ |
| description | Accurate identification of maize kernel morphology is crucial for breeding and quality improvement. Traditional manual methods are limited in dealing with complex structures and cannot fully capture kernel characteristics from a phenome perspective. To address this, our study aims to develop a high-throughput 3D phenotypic analysis method for maize kernels using Micro-CT-based point cloud data, thereby enhancing both accuracy and efficiency. We introduced new phenotypic indicators and developed a kernel phenome interaction network to better characterize the diversity and variability of kernel traits. Using a natural population of maize, high-resolution 2D slice data from Micro-CT scans were converted into 3D point cloud models for detailed analysis. This process led to the proposal of five new indicators, such as the endosperm density uniformity index (ENDUI) and endosperm integrity index (ENII), and the construction of their corresponding phenome interaction network. The study identified 27 3D morphological feature parameters, significantly improving the accuracy of kernel phenotypic analysis. These new indicators enable a more comprehensive evaluation of trait differences between subgroups. Results show that ENDUI and ENII are central to the phenome interaction networks, revealing synergistic relationships and environmental adaptation strategies during kernel growth. Additionally, it was found that length traits significantly impact the volumes of the embryo and endosperm, with linear regression coefficients of 0.599 and 0.502, respectively. This study not only advances maize kernel morphology research but also offers a novel method for phenotypic analysis. By enriching the phenotypic diversity of maize kernels, it contributes to breeding programs and grain processing improvements, ultimately enhancing the quality, and utilization value of maize kernels. |
| format | Article |
| id | doaj-art-6a2cbf9e30a042a79d67b94a635d7b3f |
| institution | OA Journals |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-6a2cbf9e30a042a79d67b94a635d7b3f2025-08-20T02:25:59ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-04-011610.3389/fpls.2025.14385941438594Precise 3D geometric phenotyping and phenotype interaction network construction of maize kernelsShuaihao Zhao0Shuaihao Zhao1Guanmin Huang2Guanmin Huang3Si Yang4Si Yang5Chuanyu Wang6Chuanyu Wang7Juan Wang8Juan Wang9Yanxin Zhao10Minxiao Duan11Ying Zhang12Ying Zhang13Xinyu Guo14Xinyu Guo15Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaBeijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaBeijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaBeijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaBeijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaBeijing Key Laboratory of Digital Plant, China 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, 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 Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaBeijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, ChinaAccurate identification of maize kernel morphology is crucial for breeding and quality improvement. Traditional manual methods are limited in dealing with complex structures and cannot fully capture kernel characteristics from a phenome perspective. To address this, our study aims to develop a high-throughput 3D phenotypic analysis method for maize kernels using Micro-CT-based point cloud data, thereby enhancing both accuracy and efficiency. We introduced new phenotypic indicators and developed a kernel phenome interaction network to better characterize the diversity and variability of kernel traits. Using a natural population of maize, high-resolution 2D slice data from Micro-CT scans were converted into 3D point cloud models for detailed analysis. This process led to the proposal of five new indicators, such as the endosperm density uniformity index (ENDUI) and endosperm integrity index (ENII), and the construction of their corresponding phenome interaction network. The study identified 27 3D morphological feature parameters, significantly improving the accuracy of kernel phenotypic analysis. These new indicators enable a more comprehensive evaluation of trait differences between subgroups. Results show that ENDUI and ENII are central to the phenome interaction networks, revealing synergistic relationships and environmental adaptation strategies during kernel growth. Additionally, it was found that length traits significantly impact the volumes of the embryo and endosperm, with linear regression coefficients of 0.599 and 0.502, respectively. This study not only advances maize kernel morphology research but also offers a novel method for phenotypic analysis. By enriching the phenotypic diversity of maize kernels, it contributes to breeding programs and grain processing improvements, ultimately enhancing the quality, and utilization value of maize kernels.https://www.frontiersin.org/articles/10.3389/fpls.2025.1438594/fullmaize kernelsplant phenomics3D point cloud modelphenotypic analysisphenome interaction network |
| spellingShingle | Shuaihao Zhao Shuaihao Zhao Guanmin Huang Guanmin Huang Si Yang Si Yang Chuanyu Wang Chuanyu Wang Juan Wang Juan Wang Yanxin Zhao Minxiao Duan Ying Zhang Ying Zhang Xinyu Guo Xinyu Guo Precise 3D geometric phenotyping and phenotype interaction network construction of maize kernels Frontiers in Plant Science maize kernels plant phenomics 3D point cloud model phenotypic analysis phenome interaction network |
| title | Precise 3D geometric phenotyping and phenotype interaction network construction of maize kernels |
| title_full | Precise 3D geometric phenotyping and phenotype interaction network construction of maize kernels |
| title_fullStr | Precise 3D geometric phenotyping and phenotype interaction network construction of maize kernels |
| title_full_unstemmed | Precise 3D geometric phenotyping and phenotype interaction network construction of maize kernels |
| title_short | Precise 3D geometric phenotyping and phenotype interaction network construction of maize kernels |
| title_sort | precise 3d geometric phenotyping and phenotype interaction network construction of maize kernels |
| topic | maize kernels plant phenomics 3D point cloud model phenotypic analysis phenome interaction network |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1438594/full |
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