Data

Synthetic Arabidopsis Dataset

Commonwealth Scientific and Industrial Research Organisation
Ward, Daniel ; Moghadam, Peyman
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/5c36957c0af41&rft.title=Synthetic Arabidopsis Dataset&rft.identifier=https://doi.org/10.25919/5c36957c0af41&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=We introduce a synthetic dataset of 10,000 top down images of Arabidopsis plants. Leaf instance segmentation labels for each image are also presented. This dataset was designed to accompany the real dataset provided with the Leaf Segmentation Challenge of the Computer Vision Problems in Plant Phenotyping. Furthermore, we release a leaf instance segmentation pre-trained model based on the Mask-RCNN architecture.&rft.creator=Ward, Daniel &rft.creator=Moghadam, Peyman &rft.date=2019&rft.edition=v4&rft.relation=https://arxiv.org/abs/1807.10931&rft_rights=CSIRO Data Licence https://research.csiro.au/dap/licences/csiro-data-licence/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2018.&rft_subject=instance segmentation&rft_subject=leaf annotation&rft_subject=Arabidopsis&rft_subject=synthetic&rft_subject=Deep Learning: Data61&rft_subject=CSIRO&rft_subject=Machine Learning&rft_subject=Deep Learning&rft_subject=Leaf segmentation&rft_subject=CVPPP&rft_subject=Agricultural systems analysis and modelling&rft_subject=Agriculture, land and farm management&rft_subject=AGRICULTURAL, VETERINARY AND FOOD SCIENCES&rft_subject=Assistive robots and technology&rft_subject=Control engineering, mechatronics and robotics&rft_subject=ENGINEERING&rft_subject=Artificial intelligence not elsewhere classified&rft_subject=Artificial intelligence&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft_subject=Computer vision&rft_subject=Computer vision and multimedia computation&rft.type=dataset&rft.language=English Access the data
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We introduce a synthetic dataset of 10,000 top down images of Arabidopsis plants. Leaf instance segmentation labels for each image are also presented. This dataset was designed to accompany the real dataset provided with the Leaf Segmentation Challenge of the Computer Vision Problems in Plant Phenotyping. Furthermore, we release a leaf instance segmentation pre-trained model based on the Mask-RCNN architecture.

Available: 2019-01-10

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Related Publications

  • Associated with D. Ward, P. Moghadam, and Nicolas Hudson, "Deep Leaf Segmentation Using Synthetic Data", British Machine Vision Conference (BMVC) Workshop on Computer Vision Problems in Plant Pheonotyping (CVPPP 2018)
    url : arxiv.org/abs/1807.10931

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Subjects
Agricultural, Veterinary and Food Sciences | Agricultural Systems Analysis and Modelling | Agriculture, Land and Farm Management | Arabidopsis | Artificial Intelligence | Artificial Intelligence Not Elsewhere Classified | Assistive Robots and Technology | CSIRO | CVPPP | Computer Vision | Computer Vision and Multimedia Computation | Control Engineering, Mechatronics and Robotics | Deep Learning | Deep Learning: Data61 | Engineering | Information and Computing Sciences | Leaf segmentation | Machine Learning | instance segmentation | leaf annotation | synthetic |

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ACN 633 798 857