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

Abstract

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.

Keywords: 3D image processing; computed tomography; high-throughput phenotyping; morphological trait; seed and fruit.

FIGURE 1

X-ray computed tomography image acquisition and processing pipeline for high-throughput phenotyping morphological characteristics of seeds/fruits. (A) Image acquisition and 3D reconstruction. (B) Image intensity equalization. (C) Container removal and object plant tissue segmentation. (D) Morphological phenotype extraction. (E) Internal component segmentation and phenotype extraction.

FIGURE 2

3DPheno-Seed&Fruit software interface with wheat data. (A) The main interface. (B) Example of histogram and correlation plots incorporated in the lower-middle of the interface.

FIGURE 3

The raw X-ray computed tomography images. (A) wheat seeds in the container with a diameter of 70 mm; (B) soybean seeds in the container with a diameter of 80 mm; (C) soybean seeds in the container with a diameter of 90 mm; (D) peanuts, pine nuts, and pistachio nuts in the container with a diameter of 90 mm.

FIGURE 4

Comparison between the 3DPheno-Seed&Fruit and manually measured phenotypes of seeds using the simple linear regression. (A) soybean; (B) wheat. Each point represents individual seeds. Coefficient of determination (R²) and number of observations (N) are shown in individual scatter plots.

FIGURE 5

Pairwise-correlation coefficients among eight morphological phenotypes of soybean seeds.