Hugging Face Trending Papers
Automated Maize Ear Phenotyping Using 3D Reconstructions
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arXiv Computer Vision
Sep 3
Automated Maize Ear Phenotyping Using 3D Reconstructions
The paper presents a fully automated pipeline that extracts maize ear traits—such as kernel count, row number, and kernel size—from 3D point clouds generated by a video-to-point-cloud platform. The method processes raw video through COLMAP and NeRF, isolates the ear, calibrates the point cloud, aligns it, unwraps it into a 2D image, and applies Cellpose‑SAM for instance segmentation, achieving high accuracy (kernel count R² = 0.921, MAPE = 10.33 %) on a held‑out dataset. The resulting multi‑trait dataset, with genotype identities, is ready for phenotype‑to‑genotype association studies.
arXiv Computer Vision
Sep 1
AI-enabled Low-Cost 3D Maize Ear Morphometry Platform at Breeding Scale
arXiv:2608.30161v1 Announce Type: new Abstract: Maize ear geometry (length, width, curvature, and volume) is closely tied to yield and grain-filling outcomes, but existing high-throughput phenotyping...
arXiv Computer Vision
Aug 31
Denoising-Aware Temporal Point Cloud Completion for 3D Crop Architecture Recovery and Phenotypic Trait Extraction
The paper introduces SynthCrop4D, a synthetic dataset of temporally evolving plant point clouds that includes controllable noise, occlusion, and complete geometry for benchmarking reconstruction methods. It proposes a two‑stage pipeline combining spatial denoising with an Adaptive Temporal PoinTr model to recover missing regions from self‑occlusion, achieving significant improvements in reconstruction quality on both SynthCrop4D and the real Pheno4D dataset. The completed point clouds are further used to extract phenotypic traits such as plant height, canopy width, and convex hull volume, demonstrating the pipeline’s utility for high‑throughput crop phenotyping.
Hugging Face Trending Papers
Jul 2
The Turning Point of 3D Plant Phenotyping: 3D Foundation Models Enable Minute-to-Second Cross-Crop Reconstruction and Beyond
3D plant phenotyping is notoriously known to be procedure-complicated and of low throughput due to the extensive multi-view imaging, the fragile 3D reconstruction pipeline, and the additional cost from reconstructed geometry to phenotypic extraction. These limitations are further amplified in low-cost data acquisition, where smartphone videos or sparsely sampled multi-view images provide limited view overlap and self-occlusion.
Hugging Face Trending Papers
Jun 21
Curvature-aware 3D length estimation of greenhouse cucumbers using RGB-D imaging and cubic spline arc-length integration
Commercial greenhouse cucumber production is graded by fruit length, which drives harvest scheduling, labour allocation, and logistics. Manual measurement with thread or caliper is accurate but infeasible at commercial scale.
arXiv Computer Vision
Sep 14
EgoMaize: A First-Person Maize Instance Segmentation Benchmark under Severe Field Occlusion
arXiv:2609.12350v1 Announce Type: new Abstract: Close-range first-person field images are important for mobile maize phenotyping because many plant-level traits depend on in-canopy structures that ar...