Cereal grain 3D point cloud analysis method for shape extraction and filled/unfilled grain identification based on structured light imaging

被引:7
|
作者
Qin, Zhijie [1 ]
Zhang, Zhongfu [1 ]
Hua, Xiangdong [1 ]
Yang, Wanneng [2 ]
Liang, Xiuying [1 ]
Zhai, Ruifang [3 ]
Huang, Chenglong [1 ]
机构
[1] Huazhong Agr Univ, Coll Engn, Wuhan 430070, Peoples R China
[2] Huazhong Agr Univ, Natl Ctr Plant Gene Res Wuhan, Natl Key Lab Crop Genet Improvement, Wuhan 430070, Peoples R China
[3] Huazhong Agr Univ, Coll Informat, Wuhan 430070, Peoples R China
基金
中国国家自然科学基金;
关键词
FOOD SECURITY; RICE; EFFICIENCY; PHENOMICS;
D O I
10.1038/s41598-022-07221-4
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Cereals are the main food for mankind. The grain shape extraction and filled/unfilled grain recognition are meaningful for crop breeding and genetic analysis. The conventional measuring method is mainly manual, which is inefficient, labor-intensive and subjective. Therefore, a novel method was proposed to extract the phenotypic traits of cereal grains based on point clouds. First, a structured light scanner was used to obtain the grains point cloud data. Then, the single grain segmentation was accomplished by image preprocessing, plane fitting, region growth clustering. The length, width, thickness, surface area and volume was calculated by the specified analysis algorithms for grain point cloud. To demonstrate this method, experimental materials included rice, wheat and corn were tested. Compared with manual measurement results, the average measurement error of grain length, width and thickness was 2.07%, 0.97%, 1.13%, and the average measurement efficiency was about 9.6 s per grain. In addition, the grain identification model was conducted with 25 grain phenotypic traits, using 6 machine learning methods. The results showed that the best accuracy for filled/unfilled grain classification was 90.184%.The best accuracy for indica and japonica identification was 99.950%, while for different varieties identification was only 47.252%. Therefore, this method was proved to be an efficient and effective way for crop research.
引用
收藏
页数:16
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