1College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
2Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China
3College of Information Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
4College of Land Science and Technology, China Agricultural University, Beijing 100091, China
5College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
| Received 15 Apr 2023 |
Accepted 04 Nov 2023 |
Published 22 Nov 2023 |
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.
