# 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

arXiv: https://arxiv.org/abs/2005.02162 · HTML: https://mlsanity.com/paper/2005.02162/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2020.

## Built on by (most cited first, 30 of 42)

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- [Noisy Annotation Refinement for Object Detection](https://mlsanity.com/paper/2110.10456/) (2021)
- [WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting](https://mlsanity.com/paper/2103.09408/) (2021)
- [Attribute-Modulated Generative Meta Learning for Zero-Shot Classification](https://mlsanity.com/paper/2104.10857/) (2021)
- [Semi-supervised dry herbage mass estimation using automatic data and synthetic images](https://mlsanity.com/paper/2110.13719/) (2021)
- [Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods](https://mlsanity.com/paper/2105.07660/) (2021)
- [DT/MARS-CycleGAN: Improved Object Detection for MARS Phenotyping Robot](https://mlsanity.com/paper/2310.12787/) (2023)
- [Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review](https://mlsanity.com/paper/2204.04707/) (2022)
- [Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation](https://mlsanity.com/paper/2204.08271/) (2022)
- [Empirical Study on Optimizer Selection for Out-of-Distribution Generalization](https://mlsanity.com/paper/2211.08583/) (2022)
- [UNICBench: UNIfied Counting Benchmark for MLLM](https://mlsanity.com/paper/2603.00595/) (2026)
- [SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts](https://mlsanity.com/paper/2108.12992/) (2021)
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- [High-throughput 3D shape completion of potato tubers on a harvester](https://mlsanity.com/paper/2407.21341/) (2024)
- [Local-Global Attention: An Adaptive Mechanism for Multi-Scale Feature Integration](https://mlsanity.com/paper/2411.09604/) (2024)
- [Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated Images for the Development and Benchmarking of Rice Segmentation Algorithms](https://mlsanity.com/paper/2504.02880/) (2025)
- [Advancing Wheat Crop Analysis: A Survey of Deep Learning Approaches Using Hyperspectral Imaging](https://mlsanity.com/paper/2505.00805/) (2025)
- [TasselNetV4: A vision foundation model for cross-scene, cross-scale, and cross-species plant counting](https://mlsanity.com/paper/2509.20857/) (2025)
- [Plant Taxonomy Meets Plant Counting: A Fine-Grained, Taxonomic Dataset for Counting Hundreds of Plant Species](https://mlsanity.com/paper/2603.21229/) (2026)
- [Global Wheat Challenge 2020: Analysis of the competition design and winning models](https://mlsanity.com/paper/2105.06182/) (2021)
- [Estimates of maize plant density from UAV RGB images using Faster-RCNN detection model: impact of the spatial resolution](https://mlsanity.com/paper/2105.11857/) (2021)
- [Multi-growth stage plant recognition: a case study of Palmer amaranth (Amaranthus palmeri) in cotton (Gossypium hirsutum)](https://mlsanity.com/paper/2307.15816/) (2023)
- [First Place Solution to the MLCAS 2025 GWFSS Challenge: The Devil is in the Detail and Minority](https://mlsanity.com/paper/2508.17305/) (2025)

## Abstract (arXiv)

Detection of wheat heads is an important task allowing to estimate pertinent traits including head population density and head characteristics such as sanitary state, size, maturity stage and the presence of awns. Several studies developed methods for wheat head detection from high-resolution RGB imagery. They are based on computer vision and machine learning and are generally calibrated and validated on limited datasets. However, variability in observational conditions, genotypic differences, development stages, head orientation represents a challenge in 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, the Global Wheat Head detection (GWHD) dataset. It contains 4,700 high-resolution RGB images and 190,000 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 is publicly available at http://www.global-wheat.com/ and aimed at developing and benchmarking methods for wheat head detection.