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

Abstract

The wheat grain three-dimensional (3D) phenotypic characters are of great significance for final yield and variety breeding, and the ventral sulcus traits are the important factors to the wheat flour yield. The wheat grain trait 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 obtained by destructive measurement. In this paper, an intelligent analysis method based on the structured light imaging has been proposed to extract the 3D wheat grain phenotypes and ventral sulcus traits. First, the 3D point cloud data of wheat grain were obtained by the structured light scanner, and then, the specified point cloud processing algorithms including single grain segmentation and ventral sulcus location have been designed; finally, 28 wheat grain 3D phenotypic characters and 4 ventral sulcus traits have been extracted. To evaluate the best experimental conditions, three-level orthogonal experiments, which include rotation angle, scanning angle, and 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 were 30°, 37°, black color individually. Additionally, the results also proved that the mean absolute percentage errors (MAPEs) of wheat grain length, width, thickness, and ventral sulcus depth were 1.83, 1.86, 2.19, and 4.81%. Moreover, the 500 wheat grains of five 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 2 of the models ranged from 0.77 to 0.83. Finally, the wheat grain phenotype extraction and grain weight prediction were integrated into the specialized software. Therefore, this method was demonstrated to be an efficient and effective way for wheat breeding research.

Plant phenotyping relevance

構造化光画像から小麦粒の3D表現型と腹溝形質を抽出する手法を開発し、精度評価・条件最適化・ソフトウェア統合まで行っており、表現型取得が中心である。

Code and data availability

本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。