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.

通过联合国际协作努力，我们建立了一个大型、多样化且标记完整的数据集，全球小麦头部检测（GWHD）数据集。该数据集包含4,700张高分辨率RGB图像和190,000个标记的小麦头部，旨在开发和基准化小麦头部检测的方法，并通过提供图像采集指南、关联最小元数据以遵守公平原则和一致标注方法来帮助开发新的头部检测数据集。

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

全球小麦穗检测（GWHD）数据集：一个由高分辨率RGB标记图像组成的大型多样化数据集，用于开发和基准测试小麦穗检测方法