AUTHOR=Huang Chenglong , Qin Zhijie , Hua Xiangdong , Zhang Zhongfu , Xiao Wenli , Liang Xiuying , Song Peng , Yang Wanneng 

TITLE=An Intelligent Analysis Method for 3D Wheat Grain and Ventral Sulcus Traits Based on Structured Light Imaging

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2022.840908

DOI=10.3389/fpls.2022.840908

ISSN=1664-462X

ABSTRACT=The wheat grain 3D phenotypic characters are of great significance for final yield and variety breeding, and the ventral sulcus traits is an important factor to the wheat flour yield. The wheat grain traits measurements are necessary, however the traditional measurement method is still manual, which is inefficient, subjective and labor intensive, moreover the ventral sulcus traits can only be only be obtained by destructive measurement. In this paper, an intelligent analysis method based on structured light imaging has been proposed to extract the 3D wheat grain phenotypes and ventral sulcus traits. Firstly, the three-dimensional point cloud data of wheat grain were obtained by structured light scanner, and then the specified point cloud processing algorithms including single grain segmentation, ventral sulcus location have been designed, finally 28 wheat grain 3D phenotypic characters and 4 ventral sulcus traits have been extracted. In order to evaluate the best experimental conditions, three levels orthogonal experiments including rotation angle, scanning angle, stage color factors, were carried out on 125 grains of 5 wheat varieties, and the results demonstrated that optimum conditions of rotation angle, scanning angle and stage color was 30°, 37°, black color individually. And the results also proved that the mean absolute percentage errors of wheat grain length, width, thickness, ventral sulcus depth and were 1.83%, 1.86%, 2.19% and 4.81%. Moreover, the 500 wheat grains of 5 varieties were used to construct and validate the wheat grain weight model by 32 phenotypic traits, and the cross-validation results showed that the R square of the models ranged from 0.77 to 0.83. Finally, the wheat grain phenotype extraction and grain weight prediction were integrated into a specialized software. Therefore, this method was demonstrated to be an efficient and effective way for wheat breeding research.