WG-3D: A Low-Cost Platform for High-Throughput Acquisition of 3D Information on Wheat Grain

Abstract

The three-dimensional (3D) morphological information of wheat grains is an important parameter for discriminating seed health, wheat yield, and wheat quality. High-throughput acquisition of 3D indicators of wheat grains is of great importance for wheat cultivation management, genetic breeding, and economic value. Currently, the 3D morphology of wheat grains still relies on manual investigation, which is subjective, inefficient, and poorly reproducible. The existing 3D acquisition equipment is complicated to operate and expensive, which cannot meet the requirements of high-throughput phenotype acquisition. In this paper, an automatic, economical, and efficient method for the 3D morphometry of wheat grain is proposed. A line laser binocular camera was used to obtain high-quality point-cloud data. A wheat grain 3D model was constructed by point-cloud segmentation, finding, clustering, projection, and reconstruction. Based on this, 3D morphological indicators of wheat grains were calculated. The results show that the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the length were 0.2256 mm and 2.60%, the width, 0.2154 mm and 5.83%, the thickness, 0.2119 mm and 5.81%, and the volume, 1.7740 mm3 and 4.31%. The scanning time was around 12 s and the data processing time was around 3.18 s under a scanning speed of 25 mm/s. This method can achieve the high-throughput acquisition of the 3D information of wheat grains, and it provides a reference for in-depth study of the 3D morphological indicators of wheat and other grains.