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.

通过多视角无人机影像和 LiDAR 点云相结合的数据集，本研究提出了第一个多模态农作物生物量估计数据集，并通过评估现有方法的性能和探索新的潜在应用，如无人机图像下的 3D 农作物重建和新视角渲染，为农作物生物量量化的基准方法和视觉方法的发展提供了支持。