Research Article | Open Access

Volume 2020 |Article ID 3521852 | https://doi.org/10.34133/2020/3521852

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

Etienne DavidiD ,^1,2 Simon MadeciD ,^2,3 Pouria Sadeghi-TehraniD ,³ Helge AaseniD ,⁴ Bangyou ZhengiD ,⁵ Shouyang LiuiD ,^2,6 Norbert KirchgessneriD ,⁴ Goro IshikawaiD ,⁷ Koichi NagasawaiD ,⁸ Minhajul A. BadhoniD ,⁹ Curtis PozniakiD ,¹⁰ Benoit de SolaniD ,¹ Andreas HundiD ,⁴ Scott C. ChapmaniD ,^5,11 Frédéric BaretiD ,^2,6 Ian Stavness iD ,⁹ and Wei Guo iD ¹²

¹Arvalis, Institut du végétal, 3 Rue Joseph et Marie Hackin, 75116 Paris, France
²UMR1114 EMMAH, INRAE, Centre PACA, Bâtiment Climat, Domaine Saint-Paul, 228 Route de l’Aérodrome, CS 40509, 84914 Avignon Cedex, France
³Plant Sciences Department, Rothamsted Research, Harpenden, UK
⁴Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland
⁵CSIRO Agriculture and Food, Queensland Biosciences Precinct, 306 Carmody Road, St Lucia, 4067 QLD, Australia
⁶Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, China
⁷Institute of Crop Science, National Agriculture and Food Research Organization, Japan
⁸Hokkaido Agricultural Research Center, National Agriculture and Food Research Organization, Japan
⁹Department of Computer Science, University of Saskatchewan, Canada
¹⁰Department of Plant Sciences, University of Saskatchewan, Canada
¹¹School of Food and Agricultural Sciences, The University of Queensland, Gatton, 4343 QLD, Australia
¹²Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Midori-cho, Nishitokyo City, Tokyo, Japan

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.