ArticleAoB PLANTS2024
Using high-throughput phenotype platform MVS-Pheno to reconstruct the 3D morphological structure of wheat.
Article in AoB PLANTS, 2024. 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.
- Quantifying wheat spike morphology by high resolution 3D surface scanning.Plant methods · 2026Article
- Deep learning-based 3D morphological segmentation and quantitative growth analysis of field-grown cabbage across the full cycle.Plant phenomics (Washington, D.C.) · 2026Article
- The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset.Plant phenomics (Washington, D.C.) · 2025Article
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Authors and funding
9 authors.
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Abstract
It is of great significance to study the plant morphological structure for improving crop yield and achieving efficient use of resources. Three dimensional (3D) information can more accurately describe the morphological and structural characteristics of crop plants. Automatic acquisition of 3D information is one of the key steps in plant morphological structure research. Taking wheat as the research object, we propose a point cloud data-driven 3D reconstruction method that achieves 3D structure reconstruction and plant morphology parameterization at the phytomer scale. Specifically, we use the MVS-Pheno platform to reconstruct the point cloud of wheat plants and segment organs through the deep learning algorithm. On this basis, we automatically reconstructed the 3D structure of leaves and tillers and extracted the morphological parameters of wheat. The results show that the semantic segmentation accuracy of organs is 95.2%, and the instance segmentation accuracy AP
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