High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography
Traditional seed and fruit phenotyping are mainly accomplished by manual measurement or extraction of morphological properties from two-dimensional images. These methods are not only in low-throughput but also unable to collect their three-dimensional (3D) characteristics and internal morphology. X-...
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doaj-843413fb734b4b9a828910687aee41312020-11-25T04:07:17ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2020-11-011110.3389/fpls.2020.601475601475High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed TomographyWeizhen Liu0Chang Liu1Jingyi Jin2Dongye Li3Yongping Fu4Xiaohui Yuan5Xiaohui Yuan6School of Computer Science and Technology, Wuhan University of Technology, Wuhan, ChinaSchool of Computer Science and Technology, Wuhan University of Technology, Wuhan, ChinaWuhan Gooalgene Technology Co., Ltd., Wuhan, ChinaWuhan Gooalgene Technology Co., Ltd., Wuhan, ChinaEngineering Research Centre of Chinese Ministry of Education for Edible and Medicinal Fungi, Jilin Agricultural University, Changchun, ChinaSchool of Computer Science and Technology, Wuhan University of Technology, Wuhan, ChinaEngineering Research Centre of Chinese Ministry of Education for Edible and Medicinal Fungi, Jilin Agricultural University, Changchun, ChinaTraditional seed and fruit phenotyping are mainly accomplished by manual measurement or extraction of morphological properties from two-dimensional images. These methods are not only in low-throughput but also unable to collect their three-dimensional (3D) characteristics and internal morphology. X-ray computed tomography (CT) scanning, which provides a convenient means of non-destructively recording the external and internal 3D structures of seeds and fruits, offers a potential to overcome these limitations. However, the current CT equipment cannot be adopted to scan seeds and fruits with high throughput. And there is no specialized software for automatic extraction of phenotypes from CT images. Here, we introduced a high-throughput image acquisition approach by mounting a specially designed seed-fruit container onto the scanning bed. The corresponding 3D image analysis software, 3DPheno-Seed&Fruit, was created for automatic segmentation and rapid quantification of eight morphological phenotypes of internal and external compartments of seeds and fruits. 3DPheno-Seed&Fruit is a graphical user interface design and user-friendly software with an excellent phenotype result visualization function. We described the software in detail and benchmarked it based upon CT image analyses in seeds of soybean, wheat, peanut, pine nut, pistachio nut and dwarf Russian almond fruit. R2 values between the extracted and manual measurements of seed length, width, thickness, and radius ranged from 0.80 to 0.96 for soybean and wheat. High correlations were found between the 2D (length, width, thickness, and radius) and 3D (volume and surface area) phenotypes for soybean. Overall, our methods provide robust and novel tools for phenotyping the morphological seed and fruit traits of various plant species, which could benefit crop breeding and functional genomics.https://www.frontiersin.org/articles/10.3389/fpls.2020.601475/fullcomputed tomographyseed and fruitmorphological trait3D image processinghigh-throughput phenotyping |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Weizhen Liu Chang Liu Jingyi Jin Dongye Li Yongping Fu Xiaohui Yuan Xiaohui Yuan |
spellingShingle |
Weizhen Liu Chang Liu Jingyi Jin Dongye Li Yongping Fu Xiaohui Yuan Xiaohui Yuan High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography Frontiers in Plant Science computed tomography seed and fruit morphological trait 3D image processing high-throughput phenotyping |
author_facet |
Weizhen Liu Chang Liu Jingyi Jin Dongye Li Yongping Fu Xiaohui Yuan Xiaohui Yuan |
author_sort |
Weizhen Liu |
title |
High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography |
title_short |
High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography |
title_full |
High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography |
title_fullStr |
High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography |
title_full_unstemmed |
High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography |
title_sort |
high-throughput phenotyping of morphological seed and fruit characteristics using x-ray computed tomography |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Plant Science |
issn |
1664-462X |
publishDate |
2020-11-01 |
description |
Traditional seed and fruit phenotyping are mainly accomplished by manual measurement or extraction of morphological properties from two-dimensional images. These methods are not only in low-throughput but also unable to collect their three-dimensional (3D) characteristics and internal morphology. X-ray computed tomography (CT) scanning, which provides a convenient means of non-destructively recording the external and internal 3D structures of seeds and fruits, offers a potential to overcome these limitations. However, the current CT equipment cannot be adopted to scan seeds and fruits with high throughput. And there is no specialized software for automatic extraction of phenotypes from CT images. Here, we introduced a high-throughput image acquisition approach by mounting a specially designed seed-fruit container onto the scanning bed. The corresponding 3D image analysis software, 3DPheno-Seed&Fruit, was created for automatic segmentation and rapid quantification of eight morphological phenotypes of internal and external compartments of seeds and fruits. 3DPheno-Seed&Fruit is a graphical user interface design and user-friendly software with an excellent phenotype result visualization function. We described the software in detail and benchmarked it based upon CT image analyses in seeds of soybean, wheat, peanut, pine nut, pistachio nut and dwarf Russian almond fruit. R2 values between the extracted and manual measurements of seed length, width, thickness, and radius ranged from 0.80 to 0.96 for soybean and wheat. High correlations were found between the 2D (length, width, thickness, and radius) and 3D (volume and surface area) phenotypes for soybean. Overall, our methods provide robust and novel tools for phenotyping the morphological seed and fruit traits of various plant species, which could benefit crop breeding and functional genomics. |
topic |
computed tomography seed and fruit morphological trait 3D image processing high-throughput phenotyping |
url |
https://www.frontiersin.org/articles/10.3389/fpls.2020.601475/full |
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