ArticleFrontiers in plant science2023
"Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Rapid modeling of 3D rice canopy structure considering vertical heterogeneity and analysis of spectral response.Plant phenomics (Washington, D.C.) · 2026Article
- Leveraging time-series point clouds for dynamic crop canopy monitoring: Quantifying phenotypic variability and assessing leaf-level photosynthetic contributions.Plant phenomics (Washington, D.C.) · 2026Article
- 3D reconstruction analysis of maize-soybean intercropping competition under water stress.Plant phenomics (Washington, D.C.) · 2026Article
- MaizeField3D: A curated 3D point cloud and procedural model dataset of field-grown maize from a diversity panel.Plant phenomics (Washington, D.C.) · 2026Article
- Phenological growth stages of Finger millet (Frontiers in plant science · 2026Article
- Genomic and phenomic prediction for soybean seed yield, protein, and oil.The plant genome · 2025Article
- Robust soybean seed yield estimation using high-throughput ground robot videos.Frontiers in plant science · 2025Article
- Multi-sensor and multi-temporal high-throughput phenotyping for monitoring and early detection of water-limiting stress in soybean.Plant phenome journal · 2024Article
- A 3D printed plant model for accurate and reliable 3D plant phenotyping.GigaScience · 2024Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in imaging hardware allow high throughput capture of the detailed three-dimensional (3D) structure of plant canopies. The point cloud data is typically post-processed to extract coarse-scale geometric features (like volume, surface area, height, etc.) for downstream analysis. We extend feature extraction from 3D point cloud data to various additional features, which we denote as 'canopy fingerprints'. This is motivated by the successful application of the fingerprint concept for molecular fingerprints in chemistry applications and acoustic fingerprints in sound engineering applications. We developed an end-to-end pipeline to generate canopy fingerprints of a three-dimensional point cloud of soybean [
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