| Plant disease detection using hyperspectral imaging P Moghadam, D Ward, E Goan, S Jayawardena, P Sikka, E Hernandez 2017 International Conference on Digital Image Computing: Techniques and …, 2017 | 135 | 2017 |
| Deep leaf segmentation using synthetic data D Ward, P Moghadam, N Hudson arXiv preprint arXiv:1807.10931, 2018 | 133 | 2018 |
| Learning arbitrary-goal fabric folding with one hour of real robot experience R Lee, D Ward, V Dasagi, A Cosgun, J Leitner, P Corke Conference on Robot Learning, 2317-2327, 2021 | 60 | 2021 |
| Temporally coherent embeddings for self-supervised video representation learning J Knights, B Harwood, D Ward, A Vanderkop, O Mackenzie-Ross, ... 2020 25th International Conference on Pattern Recognition (ICPR), 8914-8921, 2021 | 40 | 2021 |
| Scalable learning for bridging the species gap in image-based plant phenotyping D Ward, P Moghadam Computer Vision and Image Understanding 197, 103009, 2020 | 27 | 2020 |
| Synthetic arabidopsis dataset D Ward, P Moghadam CSIRO. Data Collection., 2018 | 4 | 2018 |
| Using synthetic data to boost automated image-based plant phenotyping D Ward, P Moghadam, N Hudson | | |
Peyman MoghadamPrincipal Research Scientist, CSIRO | Professor (A), QUTVerified email at csiro.au
