{"ID":2877915,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.18694","arxiv_id":"2508.18694","title":"AgriChrono: A Multi-modal Dataset Capturing Crop Growth and Lighting Variability with a Field Robot","abstract":"Advances in AI and Robotics have accelerated significant initiatives in agriculture, particularly in the areas of robot navigation and 3D digital twin creation. A significant bottleneck impeding this progress is the critical lack of \"in-the-wild\" datasets that capture the full complexities of real farmland, including non-rigid motion from wind, drastic illumination variance, and morphological changes resulting from growth. This data gap fundamentally limits research on robust AI models for autonomous field navigation and scene-level dynamic 3D reconstruction. In this paper, we present AgriChrono, a modular robotic data collection platform and multi-modal dataset designed to capture these dynamic farmland conditions. Our platform integrates multiple sensors, enabling remote, time-synchronized acquisition of RGB, Depth, LiDAR, IMU, and Pose data for efficient and repeatable long-term data collection in real-world agricultural environments. We successfully collected 18TB of data over one month, documenting the entire growth cycle of Canola under diverse illumination conditions. We benchmark state-of-the-art 3D reconstruction methods on AgriChrono, revealing the profound challenge of reconstructing high-fidelity, dynamic non-rigid scenes in such farmland settings. This benchmark validates AgriChrono as a critical asset for advancing model generalization, and its public release is expected to significantly accelerate research and development in precision agriculture. The code and dataset are publicly available at: https://github.com/StructuresComp/agri-chrono","short_abstract":"Advances in AI and Robotics have accelerated significant initiatives in agriculture, particularly in the areas of robot navigation and 3D digital twin creation. A significant bottleneck impeding this progress is the critical lack of \"in-the-wild\" datasets that capture the full complexities of real farmland, including n...","url_abs":"https://arxiv.org/abs/2508.18694","url_pdf":"https://arxiv.org/pdf/2508.18694v3","authors":"[\"Jaehwan Jeong\",\"Tuan-Anh Vu\",\"Mohammad Jony\",\"Shahab Ahmad\",\"Md. Mukhlesur Rahman\",\"Sangpil Kim\",\"M. Khalid Jawed\"]","published":"2025-08-26T05:39:47Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"eess.SY\"]","methods":"[]","has_code":false,"code_links":[{"ID":610428,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2877915,"paper_url":"https://arxiv.org/abs/2508.18694","paper_title":"AgriChrono: A Multi-modal Dataset Capturing Crop Growth and Lighting Variability with a Field Robot","repo_url":"https://github.com/StructuresComp/agri-chrono","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}