ArticlePloS one2021
Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- A Dataset of Temporally Consistent Instance Annotations for 4D Plant Phenotyping.Scientific data · 2026Article
- Dynamic sparse point voxel transformer for 3D point cloud instance segmentation of dormant apple trees.Plant phenomics (Washington, D.C.) · 2026Article
- Three-Dimensional Crop Phenotyping for Crop Protection: Reconstruction Routes, Decision Pathways, and Digital-Twin Maturity.Plants (Basel, Switzerland) · 2026Review
- DBGCN: Dual-branch Graph Convolutional Network for organ instance inference on sparsely labeled 3D plant data.Plant phenomics (Washington, D.C.) · 2026Article
- TomatoPGT: A 3D point cloud dataset of tomato plants for segmentation and plant-trait extraction.Data in brief · 2026Article
- Leveraging time-series point clouds for dynamic crop canopy monitoring: Quantifying phenotypic variability and assessing leaf-level photosynthetic contributions.Plant phenomics (Washington, D.C.) · 2026Article
- Fine-grained 3D rice phenotyping via multi-scale NeRF and multimodal segmentation.Plant phenomics (Washington, D.C.) · 2026Article
- Ground Mobile Robots for High-Throughput Plant Phenotyping: A Review from the Closed-Loop Perspective of Perception, Decision, and Action.Plants (Basel, Switzerland) · 2026Review
- Three-dimensional phenotyping: Technological advances and applications in genomics-assisted crop breeding.Plant communications · 2026Review
- SOY3DSEG: A high-precision universal point cloud segmentation model for soybean full growth period based on improved point transformer.Plant phenomics (Washington, D.C.) · 2026Article
- 3D-OGT: 3D organ growth tracking with minimum segmentation.Plant phenomics (Washington, D.C.) · 2026Article
- MaizeField3D: A curated 3D point cloud and procedural model dataset of field-grown maize from a diversity panel.Plant phenomics (Washington, D.C.) · 2026Article
- Pepper-4D: Spatiotemporal 3D Pepper Crop Dataset for Phenotyping.Plants (Basel, Switzerland) · 2026Article
- A distributed segmentation strategy developed for three-dimensional leaf trait quantification of tomato plants.Frontiers in plant science · 2026Article
- Deep learning for three-dimensional (3D) plant phenomics.Plant phenomics (Washington, D.C.) · 2025Review
- Annotated 3D Point Cloud Dataset of Broad-Leaf Legumes Captured by High-Throughput Phenotyping Platform.Scientific data · 2025Article
- Automatic 3D Plant Organ Instance Segmentation Method Based on PointNeXt and Quickshift+.Plant phenomics (Washington, D.C.) · 2025Article
- Generation of labeled leaf point clouds for plants trait estimation.Plant phenomics (Washington, D.C.) · 2025Article
- Predictive modeling, pattern recognition, and spatiotemporal representations of plant growth in simulated and controlled environments: A comprehensive review.Plant phenomics (Washington, D.C.) · 2025Review
- TomatoWUR: An annotated dataset of tomato plants to quantitatively evaluate segmentation, skeletonisation, and plant-trait extraction algorithms for 3D plant phenotyping.Data in brief · 2025Article
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Authors and funding
11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.
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