AgriChrono: A Multi-modal Dataset Capturing Crop Growth and Lighting Variability with a Field Robot
This provides a new dataset for researchers in precision agriculture to improve model robustness in real-world field scenarios, though it is incremental as it focuses on data collection rather than novel methods.
The paper tackles the problem of limited robustness in precision agriculture models due to static datasets by introducing AgriChrono, a multi-modal dataset collected with a field robot that captures dynamic real-world conditions like crop growth and lighting variability, and benchmarks show it highlights reconstruction challenges and advances model generalization.
Existing datasets for precision agriculture have primarily been collected in static or controlled environments such as indoor labs or greenhouses, often with limited sensor diversity and restricted temporal span. These conditions fail to reflect the dynamic nature of real farmland, including illumination changes, crop growth variation, and natural disturbances. As a result, models trained on such data often lack robustness and generalization when applied to real-world field scenarios. In this paper, we present AgriChrono, a novel robotic data collection platform and multi-modal dataset designed to capture the dynamic conditions of real-world agricultural environments. Our platform integrates multiple sensors and enables remote, time-synchronized acquisition of RGB, Depth, LiDAR, and IMU data, supporting efficient and repeatable long-term data collection across varying illumination and crop growth stages. We benchmark a range of state-of-the-art 3D reconstruction models on the AgriChrono dataset, highlighting the difficulty of reconstruction in real-world field environments and demonstrating its value as a research asset for advancing model generalization under dynamic conditions. The code and dataset are publicly available at: https://github.com/StructuresComp/agri-chrono