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arXiv Computer Vision By Roberta Hunt, August Easton-Calabria, James Crall

BeeWhere: Segmenting Bumble Bee Colonies to Quantify Behavioral Effects

Read the original on arXiv Computer Vision →

BeeWhere is an AI-assisted workflow that merges ArUco fiducial detections with deep‑learnt instance segmentations to analyze bumble bee colonies. Using high‑resolution images of Bombus impatiens microcolonies, the system annotates thousands of bee instances, pollen balls, nest structures, and chamber boundaries, and trains YOLO models for behavioral metrics such as nearest‑neighbor distance and spatial occupancy. In a validation study, BeeWhere outperformed tag‑based tracking, especially under occlusion, and revealed pesticide‑induced changes in bee spatial organization that tag‑based methods missed.

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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