🤖 AI Summary
A critical bottleneck in organ-level geometric phenotyping of field-grown sugar beet is the lack of real-world, high-fidelity 3D data. Method: We introduce the first organ-level point cloud phenotyping dataset captured under realistic agricultural field conditions. Using multi-view UAV photogrammetry, we reconstructed dense 3D point clouds for 48 cultivars and integrated ground-truth agronomic trait measurements from domain experts. The dataset features fine-grained semantic annotations—including plant instance masks, individual leaf segmentation, and precise keypoints at leaf tips and bases—generated through an integrated pipeline of high-resolution imaging, photogrammetric reconstruction, and meticulous manual annotation. Contribution/Results: This benchmark enables joint tasks including 3D instance segmentation, keypoint detection, and agronomic trait prediction. It demonstrates superior cross-cultivar generalization and enhanced geometric-semantic co-modeling capability, establishing a new standard and technical foundation for precision crop phenotyping.
📝 Abstract
Agricultural production is facing challenges in the next decades induced by climate change and the need for more sustainability by reducing its impact on the environment. Advances in field management through robotic intervention, monitoring of crops by autonomous unmanned aerial vehicles (UAVs) supporting breeding of novel and more resilient crop varieties can help to address these challenges. The analysis of plant traits is called phenotyping and is an essential activity in plant breeding; it however involves a great amount of manual labor. With this paper, we provide means to better tackle the problems of instance segmentation to support robotic intervention and automatic fine-grained, organ-level geometric analysis needed for precision phenotyping. As the availability of real-world data in this domain is relatively scarce, we provide a novel dataset that was acquired using UAVs capturing high-resolution images of real breeding trials containing 48 plant varieties and therefore covering a relevant morphological and appearance spectrum. This enables the development of approaches for instance segmentation and autonomous phenotyping that generalize well to different plant varieties. Based on overlapping high-resolution images taken from multiple viewing angles, we provide photogrammetric dense point clouds and provide detailed and accurate point-wise labels for plants, leaves, and salient points as the tip and the base in 3D. Additionally, we include measurements of phenotypic traits performed by experts from the German Federal Plant Variety Office on the real plants, allowing the evaluation of new approaches not only on segmentation and keypoint detection but also directly on actual traits. The provided labeled point clouds enable finegrained plant analysis and support further progress in the development of automatic phenotyping approaches, but also enable further research in surface reconstruction, point cloud completion, and semantic interpretation of point clouds.
