Developing non-invasive 3D quantificational imaging for intelligent coconut analysis system with X-ray
Abstract Background As one of the largest drupes in the world, the coconut has a special multilayered structure and a seed development process that is not yet fully understood. On the one hand, the special structure of the coconut pericarp prevents the development of external damage to the coconut f...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-03-01
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| Series: | Plant Methods |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13007-023-01002-4 |
| _version_ | 1797864550335250432 |
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| author | Yu Zhang Qianfan Liu Jing Chen Chengxu Sun Shenghuang Lin Hongxing Cao Zhaolin Xiao Mengxing Huang |
| author_facet | Yu Zhang Qianfan Liu Jing Chen Chengxu Sun Shenghuang Lin Hongxing Cao Zhaolin Xiao Mengxing Huang |
| author_sort | Yu Zhang |
| collection | DOAJ |
| description | Abstract Background As one of the largest drupes in the world, the coconut has a special multilayered structure and a seed development process that is not yet fully understood. On the one hand, the special structure of the coconut pericarp prevents the development of external damage to the coconut fruit, and on the other hand, the thickness of the coconut shell makes it difficult to observe the development of bacteria inside it. In addition, coconut takes about 1 year to progress from pollination to maturity. During the long development process, coconut development is vulnerable to natural disasters, cold waves, typhoons, etc. Therefore, nondestructive observation of the internal development process remains a highly important and challenging task. In this study, We proposed an intelligent system for building a three-dimensional (3D) quantitative imaging model of coconut fruit using Computed Tomography (CT) images. Cross-sectional images of coconut fruit were obtained by spiral CT scanning. Then a point cloud model was built by extracting 3D coordinate data and RGB values. The point cloud model was denoised using the cluster denoising method. Finally, a 3D quantitative model of a coconut fruit was established. Results The innovations of this work are as follows. 1) Using CT scans, we obtained a total of 37,950 non-destructive internal growth change maps of various types of coconuts to establish a coconut data set called “CCID”, which provides powerful graphical data support for coconut research. 2) Based on this data set, we built a coconut intelligence system. By inputting a batch of coconut images into a 3D point cloud map, the internal structure information can be ascertained, the entire contour can be drawn and rendered according to need, and the long diameter, short diameter and volume of the required structure can be obtained. We maintained quantitative observation on a batch of local Hainan coconuts for more than 3 months. With 40 coconuts as test cases, the high accuracy of the model generated by the system is proven. The system has a good application value and broad popularization prospects in the cultivation and optimization of coconut fruit. Conclusion The evaluation results show that the 3D quantitative imaging model has high accuracy in capturing the internal development process of coconut fruits. The system can effectively assist growers in internal developmental observations and in structural data acquisition from coconut, thus providing decision-making support for improving the cultivation conditions of coconuts. |
| first_indexed | 2024-04-09T22:54:46Z |
| format | Article |
| id | doaj.art-39d367c82f45400c89da5dfdfedaceca |
| institution | Directory Open Access Journal |
| issn | 1746-4811 |
| language | English |
| last_indexed | 2024-04-09T22:54:46Z |
| publishDate | 2023-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj.art-39d367c82f45400c89da5dfdfedaceca2023-03-22T11:20:03ZengBMCPlant Methods1746-48112023-03-0119111110.1186/s13007-023-01002-4Developing non-invasive 3D quantificational imaging for intelligent coconut analysis system with X-rayYu Zhang0Qianfan Liu1Jing Chen2Chengxu Sun3Shenghuang Lin4Hongxing Cao5Zhaolin Xiao6Mengxing Huang7School of Computer Science and Technology, Hainan UniversitySchool of Computer Science and Technology, Hainan UniversityRadiology department, Central South University Xiangya School of Medicine Affiliated Haikou HospitalCoconut Research Institute, Chinese Academy of Tropical Agricultural SciencesRadiology department, Central South University Xiangya School of Medicine Affiliated Haikou HospitalCoconut Research Institute, Chinese Academy of Tropical Agricultural SciencesSchool of Computer Science and Engineering, Xi’an University of TechnologySchool of Computer Science and Technology, Hainan UniversityAbstract Background As one of the largest drupes in the world, the coconut has a special multilayered structure and a seed development process that is not yet fully understood. On the one hand, the special structure of the coconut pericarp prevents the development of external damage to the coconut fruit, and on the other hand, the thickness of the coconut shell makes it difficult to observe the development of bacteria inside it. In addition, coconut takes about 1 year to progress from pollination to maturity. During the long development process, coconut development is vulnerable to natural disasters, cold waves, typhoons, etc. Therefore, nondestructive observation of the internal development process remains a highly important and challenging task. In this study, We proposed an intelligent system for building a three-dimensional (3D) quantitative imaging model of coconut fruit using Computed Tomography (CT) images. Cross-sectional images of coconut fruit were obtained by spiral CT scanning. Then a point cloud model was built by extracting 3D coordinate data and RGB values. The point cloud model was denoised using the cluster denoising method. Finally, a 3D quantitative model of a coconut fruit was established. Results The innovations of this work are as follows. 1) Using CT scans, we obtained a total of 37,950 non-destructive internal growth change maps of various types of coconuts to establish a coconut data set called “CCID”, which provides powerful graphical data support for coconut research. 2) Based on this data set, we built a coconut intelligence system. By inputting a batch of coconut images into a 3D point cloud map, the internal structure information can be ascertained, the entire contour can be drawn and rendered according to need, and the long diameter, short diameter and volume of the required structure can be obtained. We maintained quantitative observation on a batch of local Hainan coconuts for more than 3 months. With 40 coconuts as test cases, the high accuracy of the model generated by the system is proven. The system has a good application value and broad popularization prospects in the cultivation and optimization of coconut fruit. Conclusion The evaluation results show that the 3D quantitative imaging model has high accuracy in capturing the internal development process of coconut fruits. The system can effectively assist growers in internal developmental observations and in structural data acquisition from coconut, thus providing decision-making support for improving the cultivation conditions of coconuts.https://doi.org/10.1186/s13007-023-01002-4Intelligent coconut analysisNon-invasivePoint cloudQuantitative imaging model |
| spellingShingle | Yu Zhang Qianfan Liu Jing Chen Chengxu Sun Shenghuang Lin Hongxing Cao Zhaolin Xiao Mengxing Huang Developing non-invasive 3D quantificational imaging for intelligent coconut analysis system with X-ray Plant Methods Intelligent coconut analysis Non-invasive Point cloud Quantitative imaging model |
| title | Developing non-invasive 3D quantificational imaging for intelligent coconut analysis system with X-ray |
| title_full | Developing non-invasive 3D quantificational imaging for intelligent coconut analysis system with X-ray |
| title_fullStr | Developing non-invasive 3D quantificational imaging for intelligent coconut analysis system with X-ray |
| title_full_unstemmed | Developing non-invasive 3D quantificational imaging for intelligent coconut analysis system with X-ray |
| title_short | Developing non-invasive 3D quantificational imaging for intelligent coconut analysis system with X-ray |
| title_sort | developing non invasive 3d quantificational imaging for intelligent coconut analysis system with x ray |
| topic | Intelligent coconut analysis Non-invasive Point cloud Quantitative imaging model |
| url | https://doi.org/10.1186/s13007-023-01002-4 |
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