AI-enabled Low-Cost 3D Maize Ear Morphometry Platform at Breeding Scale
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at 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.
Maize kernel traits such as row number, kernels per row, and kernel size vary largely for genetic reasons and are consistently associated with regions of the genome that influence yield. Manual measur...
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