The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
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
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Prior work on wheat head detection has shown that this variation can significantly degrade model performance on regions unseen during training (David et al., 2020).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as David et al., 2020
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Since their 2016 publication, numerous disciplines are striving to apply FAIR Principles, including genomics, agriculture (Arnaud et al., 2020; David et al., 2020), climate science, and medicine.”
From Sysrev: A FAIR Platform for Data Curation and Systematic Evidence Review (2021) · cites this paper as David et al., 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
Khanam, Z., Achari, V. P. S., Boukhennoufa, I., et al. (2024) · Durham Research Online (Durham University) · doi:10.4230/oasics.ng-res.2024.2 · Cited by 14,048
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2844175 · Cited by 3,457
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., et al. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01316-z · Cited by 2,683
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00982 · Cited by 2,194
A State-of-the-Art Survey on Deep Learning Theory and Architectures
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S. K. E., Sidike, P., Nasrin, M. S., Hasan, M., Essen, B. C. V., Awwal, A. A. S., & Asari, V. K. (2019) · Electronics · doi:10.3390/electronics8030292 · Cited by 1,722
Why are wheat yields stagnating in Europe? A comprehensive data analysis for France
Brisson, N., Gate, P., Gouache, D., Charmet, G., Oury, F., & Huard, F. (2010) · Field Crops Research · doi:10.1016/j.fcr.2010.07.012 · Cited by 768
Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives
Singh, A. K., Ganapathysubramanian, B., Sarkar, S., & Singh, A. (2018) · Trends in Plant Science · doi:10.1016/j.tplants.2018.07.004 · Cited by 670
Drone-Based Object Counting by Spatially Regularized Regional Proposal Network
Hsieh, M.-R., Lin, Y.-L., & Hsu, W. H. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.446 · Cited by 547
Milioto, A., Lottes, P., & Stachniss, C. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460962 · Cited by 537
Plant phenotyping: from bean weighing to image analysis
Walter, A., Liebisch, F., & Hund, A. (2015) · Plant Methods · doi:10.1186/s13007-015-0056-8 · Cited by 449
Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks
Ubbens, J. R., & Stavness, I. (2017) · Frontiers in Plant Science · doi:10.3389/fpls.2017.01190 · Cited by 387
PKLot—A robust dataset for parking lot classification
de Almeida, P. R. L., Oliveira, L. S., Britto, A. S., Silva, E. J., & Koerich, A. L. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.02.009 · Cited by 312
Ear density estimation from high resolution RGB imagery using deep learning technique
Madec, S., Jin, X., Lu, H., De Solan, B., Liu, S., Duyme, F., Heritier, E., & Baret, F. (2019) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2018.10.013 · Cited by 312
Sa, I., Popović, M., Khanna, R., Chen, Z., Lottes, P., Liebisch, F., Nieto, J., Stachniss, C., Walter, A., & Siegwart, R. (2018) · Remote Sensing · doi:10.3390/rs10091423 · Cited by 305
Subcategory-Aware Convolutional Neural Networks for Object Proposals and Detection
Yu, X., Choi, W., Lin, Y., & Savarese, S. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.108 · Cited by 303
Crain, J. L., Mondal, S., Rutkoski, J. E., Singh, R. P., & Poland, J. (2018) · The Plant Genome · doi:10.3835/plantgenome2017.05.0043 · Cited by 284
Detection and analysis of wheat spikes using Convolutional Neural Networks
Hasan, M. M., Chopin, J. P., Laga, H., & Miklavcic, S. J. (2018) · Plant Methods · doi:10.1186/s13007-018-0366-8 · Cited by 275
Reynolds, M. P., Chapman, S. C., Crespo‐Herrera, L., Molero, G., Mondal, S., Pequeno, D. N. L., Pinto, F., Piñera‐Chávez, F. J., Poland, J., Rivera‐Amado, C., et al. (2020) · Plant Science · doi:10.1016/j.plantsci.2019.110396 · Cited by 267
The use of plant models in deep learning: an application to leaf counting in rosette plants
Ubbens, J. R., Cieslak, M., Prusinkiewicz, P., & Stavness, I. (2018) · Plant Methods · doi:10.1186/s13007-018-0273-z · Cited by 258
Precise Detection in Densely Packed Scenes
Goldman, E., Herzig, R., Eisenschtat, A., Goldberger, J., & Hassner, T. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00537 · Cited by 257
From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer
Xiong, H., Lü, H., Liu, C., Liu, L., Cao, Z., & Shen, C. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00845 · Cited by 205
MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
Hani, N., Roy, P., & Isler, V. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2965061 · Cited by 196
A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting
Ghosal, S., Zheng, B., Chapman, S. C., Potgieter, A. B., Jordan, D. R., Wang, X. M., Singh, A. K., Singh, A., Hirafuji, M., Ninomiya, S., et al. (2019) · Plant Phenomics · doi:10.34133/2019/1525874 · Cited by 191
Deep Learning for Multi-task Plant Phenotyping
Pound, M. P., Atkinson, J. A., Wells, D. M., Pridmore, T., & French, A. P. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.241 · Cited by 187
Yield trends, variability and stagnation analysis of major crops in France over more than a century
Schauberger, B., Ari, T. B., Makowski, D., Kato, T., Kato, H., & Ciais, P. (2018) · Scientific Reports · doi:10.1038/s41598-018-35351-1 · Cited by 167
Sadeghi‐Tehran, P., Virlet, N., Ampe, E. M., Reyns, P., & Hawkesford, M. J. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.01176 · Cited by 165
Spectral Vegetation Indices to Track Senescence Dynamics in Diverse Wheat Germplasm
Anderegg, J., Yu, K., Aasen, H., Walter, A., Liebisch, F., & Hund, A. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2019.01749 · Cited by 161
TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks
Xiong, H., Cao, Z., Lu, H., Madec, S., Liu, L., & Shen, C. (2019) · Plant Methods · doi:10.1186/s13007-019-0537-2 · Cited by 157
Measures for interoperability of phenotypic data: minimum information requirements and formatting
Ćwiek‐Kupczyńska, H., Altmann, T., Arend, D., Arnaud, E., Chen, D., Cornut, G., Fiorani, F., Frohmberg, W., Junker, A., Klukas, C., et al. (2016) · Plant Methods · doi:10.1186/s13007-016-0144-4 · Cited by 133
Weed Growth Stage Estimator Using Deep Convolutional Neural Networks
Teimouri, N., Dyrmann, M., Nielsen, P. R., Mathiassen, S. K., Somerville, G. J., & Jørgensen, R. N. (2018) · Sensors · doi:10.3390/s18051580 · Cited by 117
Guo, W., Zheng, B., Potgieter, A. B., Diot, J., Watanabe, K., Noshita, K., Jordan, D. R., Wang, X. M., Watson, J., Ninomiya, S., et al. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.01544 · Cited by 99
Applying innovations and new technologies for international collaborative wheat improvement
Reynolds, M. P., & Borlaug, N. E. (2006) · The Journal of Agricultural Science · doi:10.1017/s0021859606005879 · Cited by 92
An Easy-to-Setup 3D Phenotyping Platform for KOMATSUNA Dataset
Uchiyama, H., Sakurai, S., Mishima, M., Arita, D., Okayasu, T., Shimada, A., & Taniguchi, R. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.239 · Cited by 75
Annotated Image Datasets of Rosette Plants
Scharr, H., Minervini, M., Fischbach, A., & Tsaftaris, S. A. (2014) · Cited by 73
The GrassClover Image Dataset for Semantic and Hierarchical Species Understanding in Agriculture
Skovsen, S., Dyrmann, M., Mortensen, A. K., Laursen, M. S., Gislum, R., Eriksen, J., Farkhani, S., Karstoft, H., & Jørgensen, R. N. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00325 · Cited by 63
Applying FAIR Principles to Plant Phenotypic Data Management in GnpIS
Pommier, C., Michotey, C., Cornut, G., Roumet, P., Duchêne, É., Flores, R., Lebreton, A., Alaux, M., Durand, S., Kimmel, E., et al. (2019) · Plant Phenomics · doi:10.34133/2019/1671403 · Cited by 58
Jin, X., Madec, S., Dutartre, D., de Solan, B., Comar, A., & Baret, F. (2019) · Plant Phenomics · doi:10.34133/2019/4820305 · Cited by 51
Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large ImagesPreprint
Aich, S., & Stavness, I. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.11123 · Cited by 46
Non-invasive field phenotyping of cereal development
ETH Zurich, S., Hund, A., Kronenberg, L., ETH Zurich, S., Anderegg, J., ETH Zurich, S., Yu, K., KU Leuven, B., Walter, A., & ETH Zurich, S. (2019) · Burleigh Dodds series in agricultural science · doi:10.19103/as.2019.0051.13 · Cited by 26
Gibbs, J. A., Burgess, A. J., Pound, M. P., Pridmore, T., & Murchie, E. H. (2019) · PLANT PHYSIOLOGY · doi:10.1104/pp.19.00141 · Cited by 25
Flagged first, then by citations
Cited by
Sorted by citations
Review on Convolutional Neural Networks (CNN) in vegetation remote sensing
Kattenborn, T., Leitloff, J., Schiefer, F., & Hinz, S. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.12.010 · Cited by 1,966
Generalizing to unseen domains: A survey on domain generalization
Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., & Yu, P. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3178128 · Cited by 1,046
Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review
Lu, Y., Chen, D., Olaniyi, E. O., & Huang, Y. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107208 · Cited by 410
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518
Self-citationTowards the fully automated monitoring of ecological communities
Besson, M., Alison, J., Bjerge, K., Gorochowski, T. E., Høye, T. T., Jucker, T., Mann, H. M. R., & Clements, C. F. (2022) · Ecology Letters · doi:10.1111/ele.14123 · Cited by 304
Metadata
- Authors
- David, E., Madec, S., Sadeghi-Tehran, P., Aasen, H., Zheng, B., Liu, S., Kirchgessner, N., Ishikawa, G., Nagasawa, K., Badhon, M. A., Pozniak, C., de Solan, B., Hund, A., Chapman, S. C., Baret, F., Stavness, I., & Guo, W.
- Affiliations
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
- ETH Zurich (CH)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Nanjing Agricultural University (CN)
- National Agriculture and Food Research Organization (JP)
- University of Saskatchewan (CA)
- The University of Tokyo (JP)
- Corresponding author
- Stavness, Ian
- Guo, Wei
- ORCID
- Venue
- Plant Phenomics
- Publisher
- Elsevier BV
- Published
- 2020
- Volume
- 2020
- Pages
- 3521852
- ISSN
- 2097-0374 (ISSN-L)
- 2643-6515
- PMC
- PMC7706323
- OpenAlex
- W3064678530
- Cited by
- 277
- References
- 45
- Citations by year
- 2026: 39 · 2025: 46 · 2024: 44 · 2023: 42 · 2022: 54 · 2021: 43 · 2020: 9
- FWCI
- 23.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Plant Phenomics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Rothamsted Repository (Rothamsted Repository) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rothamsted Repository (Rothamsted Repository) (submittedVersion)
- HAL (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- FSOV “Plastix”
- CASDAR LITERAL
- ANR BREEDWHEAT
- ANR PHENOME
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Keywords
- wheat head detection, RGB images, head orientation, FAIR principles, image metadata, benchmarking, plant phenotyping
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
