Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography

Hu, Wenqing; Zhang, Can; Jiang, Yuqiang; Huang, Chenglong; Liu, Qian; Xiong, Lizhong; Yang, Wanneng; Chen, Fan

doi:10.34133/2020/3414926

Cited by 40 publications

(28 citation statements)

References 38 publications

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“…These results indicate that seed width and radius have greater effects on seed volume than seed thickness and length. The similar trends were also observed in rice ( Hu et al, 2020 ). Seed compactness and sphericity showed lower correlations with other size and shape-related features.…”

Section: Resultssupporting

confidence: 82%

“…Compared to 2D imaging methods ( Tanabata et al, 2012 ; Evgenii et al, 2017 ; Baek et al, 2020 ), our methods quantify 3D morphological traits, such as the surface area, volume, and sphericity. Compared to the other 3D image analysis pipelines designed for grain phenotyping ( Glidewell, 2006 ; Hughes et al, 2017 ; Xiong et al, 2019 ; Hu et al, 2020 ; Li et al, 2020 ), our methods provide an additional function of non-destructively measuring morphological phenotypes of seed and fruit internal compartments. The GUI design software, 3DPheno-Seed&Fruit, is quite user-friendly, which is easy to navigate and has the excellent visualization functions for displaying phenotyping results.…”

Section: Discussionmentioning

confidence: 99%

“…For example, Arendse et al (2016) used the X-ray CT to non-destructively quantify the external and internal morphological features (volumes of aril, peel, kernel, juice content, and air space) of pomegranate fruit. Hu et al (2020) scanned a rice spike and achieved an automatic segmentation of individual grain and high-throughput extraction of 22 spike and grain traits such as grain number, size, shape, and density. These examples demonstrated the feasibility of using CT images to measure plant traits.…”

Section: Introductionmentioning

confidence: 99%

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High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography

Liu

Jin³

et al. 2020

Front. Plant Sci.

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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. R 2 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.

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Section: Resultssupporting

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography

Liu

Jin³

et al. 2020

Front. Plant Sci.

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“…And automated, non-destructive phenotyping of rice tiller traits at a high spatial resolution and high-throughput for large-scale assessment of rice accessions is urgently needed (Di et al, 2019). Hu et al proposed an image analysis method to extract 3D grain traits based on X-ray CT (Weijuan et al, 2020). Su et al used X-ray CT to characterize the morphology of rice panicles.…”

Section: Introductionmentioning

confidence: 99%

Mask R‐CNN‐based feature extraction and three‐dimensional recognition of rice panicle CT images

Kong

Chen

2021

Plant Direct

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The rice panicle seed setting rate is extremely important for calculating rice yield and performing genetic analysis. Unlike machine vision, X‐ray computed tomography (CT) imaging is a nondestructive technique that provides direct information on the internal and external structure of rice panicles. However, occlusion and adhesion of panicles and grains in a CT image sequence make these objects difficult to identify, which in turn hinders accurate determination of the seed setting rate of rice panicles. Therefore, this paper proposes a method based on a mask region convolutional neural network (Mask R‐CNN) for feature extraction and three‐dimensional (3‐D) recognition of CT images of rice panicles. X‐ray CT feature characterization was combined with the Mask R‐CNN algorithm to perform feature extraction and classification of a panicle and grains in each layer of the CT sequence. The Euclidean distance between adjacent layers was minimized to extract the features of a 3‐D panicle and grains. The results were used to calculate the rice panicle seed setting rate. The proposed method was experimentally verified using eight sets of different rice panicles. The results showed that the proposed method can efficiently identify and count plump grains and blighted grains to achieve an accuracy above 99% for the seed setting rate.

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“…Several researches have paid attention to the analysis of water and starch distribution in the stem of woody plants (Mairhofer et al 2013). Recently, X-ray computed tomography started to be applied in large-scale assessment of grain and tiller architecture traits in rice (Wu et al 2019;Hu et al 2020). Hence, the once difficult 3D measurement of rice chalkiness and its spatial localization could be explored in vivo through the 3D micro-CT technology.…”

Section: Introductionmentioning

confidence: 99%

3D Visualization and Volume-Based Quantification of Rice Chalkiness In Vivo by Using High Resolution Micro-CT

Xiao

2020

Rice

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Background Rice quality research attracts attention worldwide. Rice chalkiness is one of the key indexes determining rice kernel quality. The traditional rice chalkiness measurement methods only use milled rice as materials and are mainly based on naked-eye observation or area-based two-dimensional (2D) image analysis and the results could not represent the three-dimensional (3D) characteristics of chalkiness in the rice kernel. These methods are neither in vivo thus are unable to analyze living rice seeds for high throughput screening of rice chalkiness phenotype. Results Here, we introduced a novel method for 3D visualization and accurate volume-based quantification of rice chalkiness in vivo by using X-ray microcomputed tomography (micro-CT). This approach not only develops a novel volume-based method to measure the 3D rice chalkiness index, but also provides a high throughput solution for rice chalkiness phenotype analysis by using living rice seeds. Conclusions Our method could be a new powerful tool for rice chalkiness measurement, especially for high throughput chalkiness phenotype screening using living rice seeds. This method could be used in chalkiness phenotype identification and screening, and would greatly promote the basic research in rice chalkiness regulation as well as the quality evaluation in rice production practice.

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Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography

Cited by 40 publications

References 38 publications

High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography

High-Throughput Phenotyping of Morphological Seed and Fruit Characteristics Using X-Ray Computed Tomography

Mask R‐CNN‐based feature extraction and three‐dimensional recognition of rice panicle CT images

3D Visualization and Volume-Based Quantification of Rice Chalkiness In Vivo by Using High Resolution Micro-CT

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