A high‐resolution model of the grapevine leaf morphospace predicts synthetic leaves
Societal Impact Statement Grapevine leaves are emblematic of the strong visual associations people make with plants. Leaf shape is immediately recognizable at a glance, and therefore, this is used to distinguish grape varieties. In an era of computationally enabled machine learning‐derived represent...
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2025-01-01
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| Series: | Plants, People, Planet |
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| Online Access: | https://doi.org/10.1002/ppp3.10561 |
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| author | Daniel H. Chitwood Efrain Torres‐Lomas Ebi S. Hadi Wolfgang L. G. Peterson Mirjam F. Fischer Sydney E. Rogers Chuan He Michael G. F. Acierno Shintaro Azumaya Seth Wayne Benjamin Devendra Prasad Chalise Ellice E. Chess Alex J. Engelsma Qiuyi Fu Jirapa Jaikham Bridget M. Knight Nikita S. Kodjak Adazsofia Lengyel Brenda L. Muñoz Justin T. Patterson Sundara I. Rincon Francis L. Schumann Yujie Shi Charlie C. Smith Mallory K. St. Clair Carly S. Sweeney Patrick Whitaker James Wu Luis Diaz‐Garcia |
| author_facet | Daniel H. Chitwood Efrain Torres‐Lomas Ebi S. Hadi Wolfgang L. G. Peterson Mirjam F. Fischer Sydney E. Rogers Chuan He Michael G. F. Acierno Shintaro Azumaya Seth Wayne Benjamin Devendra Prasad Chalise Ellice E. Chess Alex J. Engelsma Qiuyi Fu Jirapa Jaikham Bridget M. Knight Nikita S. Kodjak Adazsofia Lengyel Brenda L. Muñoz Justin T. Patterson Sundara I. Rincon Francis L. Schumann Yujie Shi Charlie C. Smith Mallory K. St. Clair Carly S. Sweeney Patrick Whitaker James Wu Luis Diaz‐Garcia |
| author_sort | Daniel H. Chitwood |
| collection | DOAJ |
| description | Societal Impact Statement Grapevine leaves are emblematic of the strong visual associations people make with plants. Leaf shape is immediately recognizable at a glance, and therefore, this is used to distinguish grape varieties. In an era of computationally enabled machine learning‐derived representations of reality, we can revisit how we view and use the shapes and forms that plants display to understand our relationship with them. Using computational approaches combined with time‐honored methods, we can predict theoretical leaves that are possible, enabling us to understand the genetics, development, and environmental responses of plants in new ways. Summary Grapevine leaves are a model morphometric system. Sampling over 10,000 leaves using dozens of landmarks, the genetic, developmental, and environmental basis of leaf shape has been studied and a morphospace for the genus Vitis predicted. Yet, these representations of leaf shape fail to capture the exquisite features of leaves at high resolution. We measure the shapes of 139 grapevine leaves using 1672 pseudo‐landmarks derived from 90 homologous landmarks with Procrustean approaches. From hand traces of the vasculature and blade, we have derived a method to automatically detect landmarks and place pseudo‐landmarks that results in a high‐resolution representation of grapevine leaf shape. Using polynomial models, we create continuous representations of leaf development in 10 Vitis spp. We visualize a high‐resolution morphospace in which genetic and developmental sources of leaf shape variance are orthogonal to each other. Using classifiers, Vitis vinifera, Vitis spp., rootstock and dissected leaf varieties as well as developmental stages are accurately predicted. Theoretical eigenleaf representations sampled from across the morphospace that we call synthetic leaves can be classified using models. By predicting a high‐resolution morphospace and delimiting the boundaries of leaf shapes that can plausibly be produced within the genus Vitis, we can sample synthetic leaves with realistic qualities. From an ampelographic perspective, larger numbers of leaves sampled at lower resolution can be projected onto this high‐resolution space, or, synthetic leaves can be used to increase the robustness and accuracy of machine learning classifiers. |
| format | Article |
| id | doaj-art-985054801be4423eb8a6e896cc79abe9 |
| institution | OA Journals |
| issn | 2572-2611 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Plants, People, Planet |
| spelling | doaj-art-985054801be4423eb8a6e896cc79abe92025-08-20T01:54:38ZengWileyPlants, People, Planet2572-26112025-01-017113314610.1002/ppp3.10561A high‐resolution model of the grapevine leaf morphospace predicts synthetic leavesDaniel H. Chitwood0Efrain Torres‐Lomas1Ebi S. Hadi2Wolfgang L. G. Peterson3Mirjam F. Fischer4Sydney E. Rogers5Chuan He6Michael G. F. Acierno7Shintaro Azumaya8Seth Wayne Benjamin9Devendra Prasad Chalise10Ellice E. Chess11Alex J. Engelsma12Qiuyi Fu13Jirapa Jaikham14Bridget M. Knight15Nikita S. Kodjak16Adazsofia Lengyel17Brenda L. Muñoz18Justin T. Patterson19Sundara I. Rincon20Francis L. Schumann21Yujie Shi22Charlie C. Smith23Mallory K. St. Clair24Carly S. Sweeney25Patrick Whitaker26James Wu27Luis Diaz‐Garcia28Department of Horticulture Michigan State University East Lansing Michigan USADepartment of Viticulture and Enology University of California at Davis Davis California USADepartment of Viticulture and Enology University of California at Davis Davis California USADepartment of Viticulture and Enology University of California at Davis Davis California USADepartment of Viticulture and Enology University of California at Davis Davis California USADepartment of Viticulture and Enology University of California at Davis Davis California USADepartment of Viticulture and Enology University of California at Davis Davis California USADepartment of Viticulture and Enology University of California at Davis Davis California USADepartment of Viticulture and Enology University of California at Davis Davis California USADepartment of Horticulture Michigan State University East Lansing Michigan USADepartment of Plant, Soil, and Microbial Sciences Michigan State University East Lansing Michigan USADepartment of Land, Air, & Water Resources University of California at Davis Davis California USADepartment of Horticulture Michigan State University East Lansing Michigan USADepartment of Horticulture Michigan State University East Lansing Michigan USADepartment of Horticulture Michigan State University East Lansing Michigan USADepartment of Horticulture Michigan State University East Lansing Michigan USADepartment of Viticulture and Enology University of California at Davis Davis California USADepartment of Land, Air, & Water Resources University of California at Davis Davis California USADepartment of Viticulture and Enology University of California at Davis Davis California USADepartment of Viticulture and Enology University of California at Davis Davis California USADepartment of Viticulture and Enology University of California at Davis Davis California USADepartment of Viticulture and Enology University of California at Davis Davis California USADepartment of Viticulture and Enology University of California at Davis Davis California USADepartment of Horticulture Michigan State University East Lansing Michigan USADepartment of Horticulture Michigan State University East Lansing Michigan USADepartment of Viticulture and Enology University of California at Davis Davis California USADepartment of Viticulture and Enology University of California at Davis Davis California USADepartment of Viticulture and Enology University of California at Davis Davis California USADepartment of Viticulture and Enology University of California at Davis Davis California USASocietal Impact Statement Grapevine leaves are emblematic of the strong visual associations people make with plants. Leaf shape is immediately recognizable at a glance, and therefore, this is used to distinguish grape varieties. In an era of computationally enabled machine learning‐derived representations of reality, we can revisit how we view and use the shapes and forms that plants display to understand our relationship with them. Using computational approaches combined with time‐honored methods, we can predict theoretical leaves that are possible, enabling us to understand the genetics, development, and environmental responses of plants in new ways. Summary Grapevine leaves are a model morphometric system. Sampling over 10,000 leaves using dozens of landmarks, the genetic, developmental, and environmental basis of leaf shape has been studied and a morphospace for the genus Vitis predicted. Yet, these representations of leaf shape fail to capture the exquisite features of leaves at high resolution. We measure the shapes of 139 grapevine leaves using 1672 pseudo‐landmarks derived from 90 homologous landmarks with Procrustean approaches. From hand traces of the vasculature and blade, we have derived a method to automatically detect landmarks and place pseudo‐landmarks that results in a high‐resolution representation of grapevine leaf shape. Using polynomial models, we create continuous representations of leaf development in 10 Vitis spp. We visualize a high‐resolution morphospace in which genetic and developmental sources of leaf shape variance are orthogonal to each other. Using classifiers, Vitis vinifera, Vitis spp., rootstock and dissected leaf varieties as well as developmental stages are accurately predicted. Theoretical eigenleaf representations sampled from across the morphospace that we call synthetic leaves can be classified using models. By predicting a high‐resolution morphospace and delimiting the boundaries of leaf shapes that can plausibly be produced within the genus Vitis, we can sample synthetic leaves with realistic qualities. From an ampelographic perspective, larger numbers of leaves sampled at lower resolution can be projected onto this high‐resolution space, or, synthetic leaves can be used to increase the robustness and accuracy of machine learning classifiers.https://doi.org/10.1002/ppp3.10561ampelographygrapevineleaf developmentleaf shapemorphospaceVitis |
| spellingShingle | Daniel H. Chitwood Efrain Torres‐Lomas Ebi S. Hadi Wolfgang L. G. Peterson Mirjam F. Fischer Sydney E. Rogers Chuan He Michael G. F. Acierno Shintaro Azumaya Seth Wayne Benjamin Devendra Prasad Chalise Ellice E. Chess Alex J. Engelsma Qiuyi Fu Jirapa Jaikham Bridget M. Knight Nikita S. Kodjak Adazsofia Lengyel Brenda L. Muñoz Justin T. Patterson Sundara I. Rincon Francis L. Schumann Yujie Shi Charlie C. Smith Mallory K. St. Clair Carly S. Sweeney Patrick Whitaker James Wu Luis Diaz‐Garcia A high‐resolution model of the grapevine leaf morphospace predicts synthetic leaves Plants, People, Planet ampelography grapevine leaf development leaf shape morphospace Vitis |
| title | A high‐resolution model of the grapevine leaf morphospace predicts synthetic leaves |
| title_full | A high‐resolution model of the grapevine leaf morphospace predicts synthetic leaves |
| title_fullStr | A high‐resolution model of the grapevine leaf morphospace predicts synthetic leaves |
| title_full_unstemmed | A high‐resolution model of the grapevine leaf morphospace predicts synthetic leaves |
| title_short | A high‐resolution model of the grapevine leaf morphospace predicts synthetic leaves |
| title_sort | high resolution model of the grapevine leaf morphospace predicts synthetic leaves |
| topic | ampelography grapevine leaf development leaf shape morphospace Vitis |
| url | https://doi.org/10.1002/ppp3.10561 |
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