CitrusFarm Dataset

CitrusFarm is a multimodal agricultural robotics dataset that provides both multispectral images and navigational sensor data for localization, mapping and crop monitoring tasks.

• It was collected by a wheeled mobile robot in the Agricultural Experimental Station at the University of California Riverside in the summer of 2023.
• It offers a total of nine sensing modalities, including stereo RGB, depth, monochrome, near-infrared and thermal images, as well as wheel odometry, LiDAR, IMU and GPS-RTK data.
• It comprises seven sequences collected from three citrus tree fields, featuring various tree species at different growth stages, distinctive planting patterns, as well as varying daylight conditions.
• It spans a total operation time of 1.7 hours, covers a total distance of 7.5 km, and constitutes 1.3 TB of data.

Authors: Hanzhe Teng, Yipeng Wang, Xiaoao Song and Konstantinos Karydis from ARCS Lab at UC Riverside.

Related Workshop Spotlight: Present and Future of Agricultural Robotics and Technologies: Academic and Industry Perspectives (IROS 2023)

Related Publications: H. Teng, Y. Wang, X. Song and K. Karydis, “Multimodal Dataset for Localization, Mapping and Crop Monitoring in Citrus Tree Farms”, In International Symposium on Visual Computing (ISVC 2023). [paper] [preprint]

@inproceedings{teng2023multimodal,
  title={Multimodal Dataset for Localization, Mapping and Crop Monitoring in Citrus Tree Farms},
  author={Teng, Hanzhe and Wang, Yipeng and Song, Xiaoao and Karydis, Konstantinos},
  booktitle={International Symposium on Visual Computing},
  pages={571--582},
  year={2023}
}

License

CitrusFarm dataset is released under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0). You are allowed to share and adapt under the condition that you give appropriate credit, indicate if changes were made, and distribute your contributions under the same license.

Acknowledgement

This work is supported in part by NSF, USDA-NIFA, ONR and the University of California UC-MRPI. Furthermore, we thank Dr. Peggy Mauk and the staff team at UCR’s Agricultural Experimental Station for their support in our work.