A Dataset of Temporally Consistent Instance Annotations for 4D Plant Phenotyping.

Spatio-temporal 4D plant phenotyping requires high-quality datasets with precise and temporally consistent annotations. However, such datasets are currently limited due to the substantial effort required for data acquisition and manual annotation. To address this limitation, we present Sugar4D, a publicly available 4D plant phenotyping dataset of sugar beet acquired using a terrestrial LiDAR scanner. The dataset comprises 768 point clouds from 48 individual plants representing twelve genotypes, recorded semiweekly across 16 consecutive time points during the growing season. All point clouds are provided with temporally consistent, pointwise instance annotations at the organ level, enabling the tracking of individual leaves over time. Sugar4D includes 6778 annotated leaves corresponding to 675 unique leaf instances. In addition to the annotated point cloud data, we provide 58 plant- and five leaf-related morphological parameters for each plant and leaf at each time point, validated using a 3D-printed plant reference model and invasive manual reference measurements. Sugar4D supports the development and evaluation of methods for plant instance segmentation, temporal registration, organ tracking, and spatio-temporal morphological analysis.