{"ID":2852919,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.17783","arxiv_id":"2510.17783","title":"Botany-Bot: Digital Twin Monitoring of Occluded and Underleaf Plant Structures with Gaussian Splats","abstract":"Commercial plant phenotyping systems using fixed cameras cannot perceive many plant details due to leaf occlusion. In this paper, we present Botany-Bot, a system for building detailed \"annotated digital twins\" of living plants using two stereo cameras, a digital turntable inside a lightbox, an industrial robot arm, and 3D segmentated Gaussian Splat models. We also present robot algorithms for manipulating leaves to take high-resolution indexable images of occluded details such as stem buds and the underside/topside of leaves. Results from experiments suggest that Botany-Bot can segment leaves with 90.8% accuracy, detect leaves with 86.2% accuracy, lift/push leaves with 77.9% accuracy, and take detailed overside/underside images with 77.3% accuracy. Code, videos, and datasets are available at https://berkeleyautomation.github.io/Botany-Bot/.","short_abstract":"Commercial plant phenotyping systems using fixed cameras cannot perceive many plant details due to leaf occlusion. In this paper, we present Botany-Bot, a system for building detailed \"annotated digital twins\" of living plants using two stereo cameras, a digital turntable inside a lightbox, an industrial robot arm, and...","url_abs":"https://arxiv.org/abs/2510.17783","url_pdf":"https://arxiv.org/pdf/2510.17783v1","authors":"[\"Simeon Adebola\",\"Chung Min Kim\",\"Justin Kerr\",\"Shuangyu Xie\",\"Prithvi Akella\",\"Jose Luis Susa Rincon\",\"Eugen Solowjow\",\"Ken Goldberg\"]","published":"2025-10-20T17:42:20Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CV\"]","methods":"[]","has_code":false}