MMCBE: Multi-modality Dataset for Crop Biomass Prediction and Beyond
<|reference_start|>MMCBE: Multi-modality Dataset for Crop Biomass Prediction and Beyond: Crop biomass, a critical indicator of plant growth, health, and productivity, is invaluable for crop breeding programs and agronomic research. However, the accurate and scalable quantification of crop biomass remains inaccessible due to limitations in existing measurement methods. One of the obstacles impeding the advancement of current crop biomass prediction methodologies is the scarcity of publicly available datasets. Addressing this gap, we introduce a new dataset in this domain, i.e. Multi-modality dataset for crop biomass estimation (MMCBE). Comprising 216 sets of multi-view drone images, coupled with LiDAR point clouds, and hand-labelled ground truth, MMCBE represents the first multi-modality one in the field. This dataset aims to establish benchmark methods for crop biomass quantification and foster the development of vision-based approaches. We have rigorously evaluated state-of-the-art crop biomass estimation methods using MMCBE and ventured into additional potential applications, such as 3D crop reconstruction from drone imagery and novel-view rendering. With this publication, we are making our comprehensive dataset available to the broader community.<|reference_end|>
@article{li2024mmcbe:,
    title={MMCBE: Multi-modality Dataset for Crop Biomass Prediction and Beyond},
    author={Xuesong Li, Zeeshan Hayder, Ali Zia, Connor Cassidy, Shiming Liu,
  Warwick Stiller, Eric Stone, Warren Conaty, Lars Petersson, Vivien Rolland},
    journal={arXiv preprint arXiv:2404.11256},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11256},
    primaryClass={cs.CV}
}