A Pathway to Assess Genetic Variation of Wheat Germplasm by Multidimensional Traits with Digital Images

Tingting Wu; Peng Shen; Jianlong Dai; Yuntao Ma; Yi Feng

doi:10.34133/plantphenomics.0119

A Pathway to Assess Genetic Variation of Wheat Germplasm by Multidimensional Traits with Digital Images

Plant Phenomics. 2023 Nov 22:5:0119. doi: 10.34133/plantphenomics.0119. eCollection 2023.

Authors

Tingting Wu^{1

2}, Peng Shen³, Jianlong Dai¹, Yuntao Ma⁴, Yi Feng⁵

Affiliations

¹ College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China.
² Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China.
³ College of Information Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China.
⁴ College of Land Science and Technology, China Agricultural University, Beijing 100091, China.
⁵ College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China.

Abstract

In this paper, a new pathway was proposed to assess the germplasm genetic variation by multidimensional traits of wheat seeds generated from digital images. A machine vision platform was first established to reconstruct wheat germplasm 3D model from omnidirectional image sequences of wheat seeds. Then, multidimensional traits were conducted from the wheat germplasm 3D model, including seed length, width, thickness, surface area, volume, maximum projection area, roundness, and 2 new defined traits called cardioid-derived area and the index of adjustment (J index). To assess genetic variation of wheat germplasm, phenotypic coefficients of variation (PCVs), analysis of variance (ANOVA), clustering, and the defined genetic variation factor (GVF) were calculated using the extracted morphological traits of 15 wheat accessions comprising 13 offspring and 2 parents. The measurement accuracy of 3D reconstruction model is demonstrated by the correlation coefficient (R) and root mean square errors (RMSEs). Results of PCVs among all the traits show importance of multidimensional traits, as seed volume (22.4%), cardioid-derived area (16.97%), and maximum projection area (14.67%). ANOVA shows a highly significance difference among all accessions. The results of GVF innovatively reflect the connection between genotypic variance and phenotypic traits from parents to offspring. Our results confirmed that extracting multidimensional traits from digital images is a promising high-throughput and cost-efficient pathway that can be included as a valuable approach in genetic variation assessment, and it can provide useful information for genetic improvement, preservation, and evaluation of wheat germplasm.