MMCBE: Multi-modality Dataset for Crop Biomass Prediction and Beyond
This provides a publicly available dataset to address data scarcity in crop biomass estimation for agronomic research and breeding programs, though it is incremental as it focuses on data creation rather than a new method.
The authors tackled the problem of accurate and scalable crop biomass prediction by introducing MMCBE, a new multi-modality dataset with 216 sets of drone images, LiDAR point clouds, and hand-labelled ground truth, which they used to evaluate state-of-the-art methods and explore applications like 3D reconstruction.
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.