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arXiv Computer Vision By Ritwesh A. Kumar, Som Tripathi, Peja Matthews, Srikar Reddy, Talukder Zaki Jubery, Patrick Schnable, Adarsh Krishnamurthy, Baskar Ganapathysubramanian

Automated Maize Ear Phenotyping Using 3D Reconstructions

Read the original on arXiv Computer Vision →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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