Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods
A - Papers appearing in refereed journals
David, E., Serouart, M., Smith, D., Madec, S., Velumani, K., Liu, S., Wang, X., Pinto Espinosa, F., Shafiee, S., Tahir, I. S. A., Tsujimoto, H., Nasuda, S., Zheng, B., Kichgessner, N., Aasen, H., Hund, A., Sadeghi-Tehran, P., Nagasawa, K., Ishikawa, G., Dandrifosse, S., Carlier, A., Mercatoris, B., Kuroki, K., Wang, H., Ishii, M., Badhon, M. A., Pozniak, C., LeBauer, D. S., Lilimo, M., Poland, J., Chapman, S., De Solan, B., Baret, F., Stavness, I. and Guo, W. 2021. Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods. Plant Phenomics. 2021, p. 9846158. https://doi.org/10.34133/2021/9846158
| Authors | David, E., Serouart, M., Smith, D., Madec, S., Velumani, K., Liu, S., Wang, X., Pinto Espinosa, F., Shafiee, S., Tahir, I. S. A., Tsujimoto, H., Nasuda, S., Zheng, B., Kichgessner, N., Aasen, H., Hund, A., Sadeghi-Tehran, P., Nagasawa, K., Ishikawa, G., Dandrifosse, S., Carlier, A., Mercatoris, B., Kuroki, K., Wang, H., Ishii, M., Badhon, M. A., Pozniak, C., LeBauer, D. S., Lilimo, M., Poland, J., Chapman, S., De Solan, B., Baret, F., Stavness, I. and Guo, W. |
|---|---|
| Abstract | The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version. |
| Year of Publication | 2021 |
| Journal | Plant Phenomics |
| Journal citation | 2021, p. 9846158 |
| Digital Object Identifier (DOI) | https://doi.org/10.34133/2021/9846158 |
| Open access | Published as ‘gold’ (paid) open access |
| Funder | Biotechnology and Biological Sciences Research Council |
| Funder project or code | Designing Future Wheat - WP1 - Increased efficiency and sustainability |
| Publisher's version | |
| Accepted author manuscript | |
| Output status | Published |
| Publication dates | |
| Online | 22 Sep 2021 |
| Publication process dates | |
| Accepted | 11 Aug 2021 |
| Publisher | American Association for the Advancement of Science (AAAS) |
| Other file | |
| ISSN | 2643-6515 |
Related outputs
Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods
A - Papers appearing in refereed journals
Okyere, F., Cudjoe, D., Sadeghi-Tehran, P., Virlet, N., Riche, A. B., Castle, M., Greche, L., Simms, D., Mhada, M., Mohareb, F. and Hawkesford, M. J. 2023. Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods. Frontiers in Plant Science. 14, p. 1209500. https://doi.org/10.3389/fpls.2023.1209500
Machine Learning Methods for Automatic Segmentation of Images of Field and Glasshouse Based Plants for High Throughput Phenotyping
A - Papers appearing in refereed journals
Okyere, F., Cudjoe, D., Sadeghi-Tehran, P., Virlet, N., Riche, A. B., Castle, M., Greche, L., Mohareb, F., Simms, D. M., Mhada, M. and Hawkesford, M. J. 2023. Machine Learning Methods for Automatic Segmentation of Images of Field and Glasshouse Based Plants for High Throughput Phenotyping. Plants - Basel. 12 (10), p. 2035. https://doi.org/10.3390/plants12102035
Using proximal sensing parameters linked to the photosynthetic capacity to assess the nutritional status and yield potential in quinoa
A - Papers appearing in refereed journals
Cudjoe, D., Okyere, F., Virlet, N., Castle, M., Buchner, P. H., Parmar, S., Sadeghi-Tehran, P., Riche, A. B., Sohail, Q., Mhada, M., Ghanem, M., Waine, T. W., Mohareb, F. and Hawkesford, M. J. 2023. Using proximal sensing parameters linked to the photosynthetic capacity to assess the nutritional status and yield potential in quinoa. Acta Horticulturae. 1360, pp. 373-379. https://doi.org/10.17660/ActaHortic.2023.1360.45
A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery
A - Papers appearing in refereed journals
Sadeghi-Tehran, P., Virlet, N. and Hawkesford, M. J. 2021. A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery. Remote Sensing. 13 (5), p. 898. https://doi.org/10.3390/rs13050898
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
A - Papers appearing in refereed journals
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. and Guo, W. 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. Plant Phenomics. 2020, p. 3521852. https://doi.org/10.34133/2020/3521852
Time-intensive geoelectrical monitoring under winter wheat
A - Papers appearing in refereed journals
Blanchy, G., Virlet, N., Sadeghi-Tehran, P., Watts, C. W., Hawkesford, M. J., Whalley, W. R. and Binley, A. 2020. Time-intensive geoelectrical monitoring under winter wheat . Near Surface Geophysics. https://doi.org/10.1002/nsg.12107
Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform
A - Papers appearing in refereed journals
Lyra, D. H., Virlet, N., Sadeghi-Tehran, P., Hassall, K. L., Wingen, L. U., Orford, S., Griffiths, S., Hawkesford, M. J. and Slavov, G. 2020. Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform . Journal of Experimental Botany. p. erz545. https://doi.org/10.1093/jxb/erz545
DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks
A - Papers appearing in refereed journals
Sadeghi-Tehran, P., Virlet, N., Ampe, E. M., Reyns, P. and Hawkesford, M. J. 2019. DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks. Frontiers in Plant Science. 10, p. 1176. https://doi.org/10.3389/fpls.2019.01176
Scalable Database Indexing and Fast Image Retrieval Based on Deep Learning and Hierarchically Nested Structure Applied to Remote Sensing and Plant Biology
A - Papers appearing in refereed journals
Sadeghi-Tehran, P., Angelov, P., Virlet, N. and Hawkesford, M. J. 2019. Scalable Database Indexing and Fast Image Retrieval Based on Deep Learning and Hierarchically Nested Structure Applied to Remote Sensing and Plant Biology. Journal of Imaging. 5 (3), p. 33. https://doi.org/10.3390/jimaging5030033
Automated method to determine two critical growth stages of wheat: heading and flowering
A - Papers appearing in refereed journals
Sadeghi-Tehran, P., Sabermanesh, K., Virlet, N. and Hawkesford, M. J. 2017. Automated method to determine two critical growth stages of wheat: heading and flowering. Frontiers in Plant Science. 8 (252). https://doi.org/10.3389/fpls.2017.00252
Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping
A - Papers appearing in refereed journals
Sadeghi-Tehran, P., Virlet, N., Sabermanesh, K. and Hawkesford, M. J. 2017. Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping. Plant Methods. 13 (103), pp. 1-16. https://doi.org/10.1186/s13007-017-0253-8
Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring
A - Papers appearing in refereed journals
Virlet, N., Sabermanesh, K., Sadeghi-Tehran, P. and Hawkesford, M. J. 2016. Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring. Functional Plant Biology. 44 (1), pp. 143-153. https://doi.org/10.1071/FP16163
Permalink - https://repository.rothamsted.ac.uk/item/9870z/global-wheat-head-detection-2021-an-improved-dataset-for-benchmarking-wheat-head-detection-methods