Wheat3D PartNet: Annotated dataset for 3D wheat part segmentation

High precision 3D data is becoming crucial for accurate feature extraction. Acquiring 3D data from plants with different growing patterns and thier growth under different environmental conditions is still a challenging task. The utilization of deep learning techniques can overcome some of these challenges, but these techniques often demand good quality training data for 3D point cloud analysis. One of the main challenges in plant phenotyping is the general lack of annotated 3D datasets available to the research community. Constructing such datasets is particularly difficult due to the complexity of capturing high-quality data that accurately represent the intricate structures and diverse morphologies of plants. The development of robust data sets is critical to advance plant phenotyping, allowing precise quantification of plant traits, and addressing challenges in modern agriculture. However, the lack of high-quality, annotated datasets for complex plant structures, such as wheat, hinders the development of effective methodologies. To address this, we introduce Wheat3D PartNet, a comprehensive repository of 1303 3D point cloud models of wheat (Triticum L.), comprising three cultivars: Paragon, Gladius, and Apogee. The 3D point clouds are reconstructed from RGB images of real plants that were acquired from multiple viewpoints and represent different plant structures at different growth rates. Wheat3D PartNet samples are manually labeled into two parts i.e., ears (wheat spikes) and non-ears (leaves and stems) and that captured in drought and watered conditions. Wheat3D PartNet is designed to support segmentation-based trait quantification tasks such as spike counting, spike length estimation, and stress detection—facilitating more precise yield prediction and enabling early agronomic intervention. Extensive experiments using several state-of-the-art 3D deep learning models validate the dataset’s utility and challenge level. The methodology behind Wheat3D PartNet is extensible to other crops, including rice and potato, and is expected to significantly boost the research, understanding, and measurements of plants of interest. • Wheat3D PartNet: The first annotated 3D point cloud dataset for wheat plant phenotyping. • Provides high-resolution, manually labeled segmentation of wheat ears (spikes) and non-ear (leaves & stems). • Benchmarks deep learning models (3DGTN, GAPointNet, PointNet++) for wheat trait segmentation. • Enables automated, high-throughput phenotyping for crop breeding and precision agriculture. • Serves as an open-access benchmark dataset for plant science and machine learning communities.