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3D Morphological Processing for Wheat Spike Phenotypes Using Computed Tomography Images

School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China
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Remote Sens. 2019, 11(9), 1110; https://doi.org/10.3390/rs11091110
Received: 6 April 2019 / Revised: 28 April 2019 / Accepted: 6 May 2019 / Published: 9 May 2019
(This article belongs to the Special Issue Advanced Imaging for Plant Phenotyping)

Abstract

Wheat is the main food crop today world-wide. In order to improve its yields, researchers are committed to understand the relationships between wheat genotypes and phenotypes. Compared to progressive technology of wheat gene section identification, wheat trait measurement is mostly done manually in a destructive, labor-intensive and time-consuming way. Therefore, this study will be greatly accelerated and promoted if we can automatically discover wheat phenotype in a nondestructive and fast manner. In this paper, we propose a novel pipeline based on 3D morphological processing to detect wheat spike grains and stem nodes from 3D X-ray micro computed tomography (CT) images. We also introduce a set of newly defined 3D phenotypes, including grain aspect ratio, porosity, Grain-to-Grain distance, and grain angle, which are very difficult to be manually measured. The analysis of the associations among these traits would be very helpful for wheat breeding. Experimental results show that our method is able to count grains more accurately than normal human performance. By analyzing the relationships between traits and environment conditions, we find that the Grain-to-Grain distance, aspect ratio and porosity are more likely affected by the genome than environment (only tested temperature and water conditions). We also find that close grains will inhibit grain volume growth and that the aspect ratio 3.5 may be the best for higher yield in wheat breeding. View Full-Text
Keywords: 3D morphological processing; 3D phenotypes; X-ray micro computed tomography; image analysis; grain and stem detection 3D morphological processing; 3D phenotypes; X-ray micro computed tomography; image analysis; grain and stem detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Xiong, B.; Wang, B.; Xiong, S.; Lin, C.; Yuan, X. 3D Morphological Processing for Wheat Spike Phenotypes Using Computed Tomography Images. Remote Sens. 2019, 11, 1110.

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