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

Wei Wu, Yuanyuan Zhao, Hui Wang, Tianle Yang, Yanan Hu, Xiaochun Zhong (), Tao Liu (), Chengming Sun, Tan Sun and Shengping Liu ()
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Wei Wu: Key Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Yuanyuan Zhao: Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, China
Hui Wang: Lixiahe Institute of Agricultural Sciences of Jiangsu, Key Laboratory of Wheat Biology and Genetic Improvement for Low & Middle Yangtze Valley, Ministry of Agriculture and Rural Affairs, Yangzhou 225012, China
Tianle Yang: Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, China
Yanan Hu: Key Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Xiaochun Zhong: Key Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Tao Liu: Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, China
Chengming Sun: Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, China
Tan Sun: Key Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Shengping Liu: Key Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Agriculture, 2022, vol. 12, issue 11, 1-18

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 mm 3 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.

Keywords: wheat grain; binocular camera; 3D point cloud; high throughput; phenotype (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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