Overview of the SOY3DSEG workflow. (IMAGE)
Caption
Overview of the SOY3DSEG workflow. (A) Dynamic Multi-Stage Sampling Strategy (DMSS): For each soybean point cloud spanning growth stages V1–R7, the algorithm computes bounding-box volume and point-cloud density, derives the coefficient of variation, and adaptively sets a two-stage down-sampling ratio to generate a structurally faithful yet lightweight point set.(B) SOY3DSEG point-cloud segmentation network: An encoder–decoder architecture first extracts multi-scale geometric features with a geometry-aware Transformer and Transition Down layers, then restores resolution through Transition Up and feature-fusion blocks, finally classifying individual points into stem and leaf categories.(C) Cross-crop transfer validation: The trained model is applied—without additional fine-tuning—to maize and tomato datasets, producing accurate organ-level segmentations and demonstrating strong cross-species generalization.
Credit
Plant Phenomics
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CC BY