ArticleScientific reports2022
Cereal grain 3D point cloud analysis method for shape extraction and filled/unfilled grain identification based on structured light imaging.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Large-Scale Rice Mutant Establishment and High-Throughput Mutant Manipulation Help Advance Rice Functional Genomics.Plants (Basel, Switzerland) · 2025Review
- Leveraging sensor technologies for seed phenotyping by genebanks.Frontiers in plant science · 2025Article
- Integrative multi-omics analysis reveals genetic and heterotic contributions to male fertility and yield in potato.Nature communications · 2024Article
- An Overview of High-Throughput Crop Phenotyping: Platform, Image Analysis, Data Mining, and Data Management.Methods in molecular biology (Clifton, N.J.) · 2024Review
- A three-dimensional vision measurement method based on double-line combined structured light.Scientific reports · 2023Article
- A Novel Method for Filled/Unfilled Grain Classification Based on Structured Light Imaging and Improved PointNet+.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
7 authors.
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Abstract
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.
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