AgriChrono: A Multi-modal Dataset Capturing Crop Growth and Lighting Variability with a Field Robot

TL;DR

AgriChrono introduces a comprehensive multi-modal dataset captured by a robotic platform, addressing the lack of real-world farmland data to improve AI models for autonomous navigation and 3D reconstruction in agriculture.

Contribution

This paper presents AgriChrono, a modular robotic platform and dataset capturing dynamic farmland conditions, including lighting, growth, and non-rigid motion, to advance AI research in precision agriculture.

Findings

Benchmarking reveals challenges in reconstructing dynamic non-rigid scenes.

Dataset covers a full crop growth cycle with diverse illumination conditions.

Public dataset and code available to accelerate agricultural AI research.

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