A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping
Non-destructive plant stress phenotyping begins with traditional one-dimensional (1D) spectroscopy, followed by two-dimensional (2D) imaging, three-dimensional (3D) or even temporal-three-dimensional (T-3D), spectral-three-dimensional (S-3D), and temporal-spectral-three-dimensional (TS-3D) phenotypi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Plants |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2223-7747/12/8/1698 |
| _version_ | 1797603730270453760 |
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| author | Dapeng Ye Libin Wu Xiaobin Li Tolulope Opeyemi Atoba Wenhao Wu Haiyong Weng |
| author_facet | Dapeng Ye Libin Wu Xiaobin Li Tolulope Opeyemi Atoba Wenhao Wu Haiyong Weng |
| author_sort | Dapeng Ye |
| collection | DOAJ |
| description | Non-destructive plant stress phenotyping begins with traditional one-dimensional (1D) spectroscopy, followed by two-dimensional (2D) imaging, three-dimensional (3D) or even temporal-three-dimensional (T-3D), spectral-three-dimensional (S-3D), and temporal-spectral-three-dimensional (TS-3D) phenotyping, all of which are aimed at observing subtle changes in plants under stress. However, a comprehensive review that covers all these dimensional types of phenotyping, ordered in a spatial arrangement from 1D to 3D, as well as temporal and spectral dimensions, is lacking. In this review, we look back to the development of data-acquiring techniques for various dimensions of plant stress phenotyping (1D spectroscopy, 2D imaging, 3D phenotyping), as well as their corresponding data-analyzing pipelines (mathematical analysis, machine learning, or deep learning), and look forward to the trends and challenges of high-performance multi-dimension (integrated spatial, temporal, and spectral) phenotyping demands. We hope this article can serve as a reference for implementing various dimensions of non-destructive plant stress phenotyping. |
| first_indexed | 2024-03-11T04:36:00Z |
| format | Article |
| id | doaj.art-52bce445aca5461f869d518c1aea7820 |
| institution | Directory Open Access Journal |
| issn | 2223-7747 |
| language | English |
| last_indexed | 2024-03-11T04:36:00Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj.art-52bce445aca5461f869d518c1aea78202023-11-17T21:00:22ZengMDPI AGPlants2223-77472023-04-01128169810.3390/plants12081698A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress PhenotypingDapeng Ye0Libin Wu1Xiaobin Li2Tolulope Opeyemi Atoba3Wenhao Wu4Haiyong Weng5College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaNon-destructive plant stress phenotyping begins with traditional one-dimensional (1D) spectroscopy, followed by two-dimensional (2D) imaging, three-dimensional (3D) or even temporal-three-dimensional (T-3D), spectral-three-dimensional (S-3D), and temporal-spectral-three-dimensional (TS-3D) phenotyping, all of which are aimed at observing subtle changes in plants under stress. However, a comprehensive review that covers all these dimensional types of phenotyping, ordered in a spatial arrangement from 1D to 3D, as well as temporal and spectral dimensions, is lacking. In this review, we look back to the development of data-acquiring techniques for various dimensions of plant stress phenotyping (1D spectroscopy, 2D imaging, 3D phenotyping), as well as their corresponding data-analyzing pipelines (mathematical analysis, machine learning, or deep learning), and look forward to the trends and challenges of high-performance multi-dimension (integrated spatial, temporal, and spectral) phenotyping demands. We hope this article can serve as a reference for implementing various dimensions of non-destructive plant stress phenotyping.https://www.mdpi.com/2223-7747/12/8/1698plant stress phenotypingspectroscopyimagingmulti-dimensiontemporalspectral |
| spellingShingle | Dapeng Ye Libin Wu Xiaobin Li Tolulope Opeyemi Atoba Wenhao Wu Haiyong Weng A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping Plants plant stress phenotyping spectroscopy imaging multi-dimension temporal spectral |
| title | A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping |
| title_full | A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping |
| title_fullStr | A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping |
| title_full_unstemmed | A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping |
| title_short | A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping |
| title_sort | synthetic review of various dimensions of non destructive plant stress phenotyping |
| topic | plant stress phenotyping spectroscopy imaging multi-dimension temporal spectral |
| url | https://www.mdpi.com/2223-7747/12/8/1698 |
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