ArticlePlant phenomics (Washington, D.C.)2020
Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods.
Article in Plant phenomics (Washington, D.C.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.
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72 citing papers in PubMed.
- Plant counting: origins, development, and future paths.Plant phenomics (Washington, D.C.) · 2026Review
- Rapid Classification and Deep Learning-Based Development Estimation of the Seeds ofPlants (Basel, Switzerland) · 2026Article
- Wheat spike and spikelet detection and counting from high-resolution digital imagery using YOLO with Oriented Bounding Boxes.Scientific reports · 2026Article
- High-throughput phenotyping of wheat ear surface area and ear density in the field.Plant phenomics (Washington, D.C.) · 2026Article
- Bidirectional state space modeling for lightweight and robust wheat head detection in complex agricultural environments.Scientific reports · 2026Article
- Leaf-DETR: Progressive adaptive network with lower matching cost for dense leaves detection.Plant phenomics (Washington, D.C.) · 2026Article
- Lightweight real-time detectors of apple-leaf diseases operating on embedded devices.PloS one · 2026Article
- LiteMS-YOLO: a lightweight framework for small target detection in complex wheat field environments.Frontiers in plant science · 2026Article
- Transfer learning models for wheat ear detection on multi-source dataset.Scientific reports · 2025Article
- Deep learning in plant phenotyping: the first ten years.Plant phenomics (Washington, D.C.) · 2025Review
- Deep learning for three-dimensional (3D) plant phenomics.Plant phenomics (Washington, D.C.) · 2025Review
- Low-rank adaptation for edge AI.Scientific reports · 2025Article
- The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset.Plant phenomics (Washington, D.C.) · 2025Article
- Global rice multiclass segmentation dataset (RiceSEG): comprehensive and diverse high-resolution RGB-annotated images for the development and benchmarking of rice segmentation algorithms.Plant phenomics (Washington, D.C.) · 2025Article
- A Detection Approach for Wheat Spike Recognition and Counting Based on UAV Images and Improved Faster R-CNN.Plants (Basel, Switzerland) · 2025Article
- RGB imaging and computer vision-based approaches for identifying spike number loci for wheat.Plant phenomics (Washington, D.C.) · 2025Article
- TPDNet: Triple phenotype deepen networks for monocular 3D object detection of melons and fruits in fields.Plant phenomics (Washington, D.C.) · 2025Article
- A scalable and efficient UAV-based pipeline and deep learning framework for phenotyping sorghum panicle morphology from point clouds.Plant phenomics (Washington, D.C.) · 2025Article
- LGWheatNet: A Lightweight Wheat Spike Detection Model Based on Multi-Scale Information Fusion.Plants (Basel, Switzerland) · 2025Article
- CVRP: A rice image dataset with high-quality annotations for image segmentation and plant phenomics research.Plant phenomics (Washington, D.C.) · 2025Article
12 more citing papers are in PubMed but not listed here.
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Authors and funding
17 authors.
Funding
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Abstract
The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these methods have generally been calibrated and validated on limited datasets. High variability in observational conditions, genotypic differences, development stages, and head orientation makes wheat head detection a challenge for computer vision. Further, possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex. Through a joint international collaborative effort, we have built a large, diverse, and well-labelled dataset of wheat images, called the Global Wheat Head Detection (GWHD) dataset. It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles, and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection.
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