ArticlePlant phenomics (Washington, D.C.)2024
SCAG: A Stratified, Clustered, and Growing-Based Algorithm for Soybean Branch Angle Extraction and Ideal Plant Architecture Evaluation.
Article in Plant phenomics (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Accurate 3D recording: Integrating ground-based LiDAR data and 3D segmentation network to extract 3D traits and analyze genetics in wheat populations.Plant phenomics (Washington, D.C.) · 2026Article
- Integrating 3D phenotyping and functional-structural plant models for crop ideotype breeding.Nature communications · 2026Review
- Beyond high-throughput: leveraging plant phenotyping to improve understanding and prediction of plant growth through process-based models.The New phytologist · 2026Review
- Branch architecture reconstruction and phenotypic trait analysis of poplar trees using low-cost UAV LiDAR point clouds.Plant phenomics (Washington, D.C.) · 2026Article
- Identification of quantitative trait loci qPL6 for petiole length in soybean.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Deep learning for three-dimensional (3D) plant phenomics.Plant phenomics (Washington, D.C.) · 2025Review
- Three-dimensional reconstruction of densely planted rice seedlings based on MultiView images.Plant phenomics (Washington, D.C.) · 2025Article
- Regulatory Mechanism of the GmMYB14 Transcription Factor on Auxin-Related Proteins in Soybean.International journal of molecular sciences · 2025Article
- AutoGP: An intelligent breeding platform for enhancing maize genomic selection.Plant communications · 2025Article
- A spontaneous keypoints connection algorithm for leafy plants skeletonization and phenotypes extraction.Frontiers in plant science · 2025Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Three-dimensional (3D) phenotyping is important for studying plant structure and function. Light detection and ranging (LiDAR) has gained prominence in 3D plant phenotyping due to its ability to collect 3D point clouds. However, organ-level branch detection remains challenging due to small targets, sparse points, and low signal-to-noise ratios. In addition, extracting biologically relevant angle traits is difficult. In this study, we developed a stratified, clustered, and growing-based algorithm (SCAG) for soybean branch detection and branch angle calculation from LiDAR data, which is heuristic, open-source, and expandable. SCAG achieved high branch detection accuracy (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.