MMCBE: Multi-modality Dataset for Crop Biomass Estimation and Beyond
Xuesong Li Zeeshan Hayder Ali Zia Connor Cassidy Shiming Liu Warwick Stiller Eric Stone Warren Conaty Lars Petersson Vivien Rolland
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.