ArticleFrontiers in plant science2022
Estimation of Characteristic Parameters of Grape Clusters Based on Point Cloud Data.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Machine Learning-Based Prediction of Tribological Properties of Epoxy Composite Coating.Polymers · 2025Article
- Comprehensive metabolomics-based analysis of sugar composition and content in berries of 18 grape varieties.Frontiers in plant science · 2023Article
- Phenotyping of Silique Morphology in Oilseed Rape Using Skeletonization with Hierarchical Segmentation.Plant phenomics (Washington, D.C.) · 2023Article
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5 authors.
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Abstract
The measurement of grapevine phenotypic parameters is crucial to quantify crop traits. However, individual differences in grape bunches pose challenges in accurately measuring their characteristic parameters. Hence, this study explores a method for estimating grape feature parameters based on point cloud information: segment the grape point cloud by filtering and region growing algorithm, and register the complete grape point cloud model by the improved iterative closest point algorithm. After estimating model phenotypic size characteristics, the grape bunch surface was reconstructed using the Poisson algorithm. Through the comparative analysis with the existing four methods (geometric model, 3D convex hull, 3D alpha-shape, and voxel-based), the estimation results of the algorithm proposed in this study are the closest to the measured parameters. Experimental data show that the coefficient of determination (
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