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

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.

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

“…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.…”

“…By combining them along the z-direction, the 3D images were easily gained and would be directly used for all the following image analyses. Comparing to the methods based on 2D image processing ( Hughes et al, 2017 ), this strategy is more precise to identify seeds/fruits from the background noise and the tightly connected interlayers of container because it simultaneously took all voxels from all slices into consideration ( Xiong et al, 2019 ). Before segmentation of seeds/fruits, we conducted a series of 3D image preprocessing, consisting of intensity standardization and removal of holder and background.…”

“…The intensity of CT images might vary across sample sets when scanning at different time and using different scanning parameters. To make sure all the images with consistent parameters, a linear stretch mapping method which was adopted in Xiong et al (2019) was utilized to automatically standardize the intensity of CT images of all sets. The equation for linear stretch mapping is as follows:…”

“…But it has since been applied to a broad range of fields, like material, earth, natural, and animal sciences ( Cnudde et al, 2006 ; Schambach et al, 2010 ). Recent improvements in scanning quality, resolution, and speed allowed it to be adopted to visualize and quantify complex plant traits ( Dhondt et al, 2010 ; Hughes et al, 2017 ; Xiong et al, 2019 ; Li et al, 2020 ; Yang et al, 2020 ). 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.…”

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

“…Strange automatically estimated the morphometry of a wheat grain from computed tomography [ 26 ]. Xiong et al developed a 3D morphological method to complete the processing of computed tomography images of wheat spikes [ 27 ]. Hughes et al achieved the nondestructive and high-content analysis of wheat grain traits using X-ray microcomputed tomography [ 28 ].…”