Abstract
Plant phenotyping concerns the study of plant traits resulted from their interaction with their environment. Computer vision (CV) techniques represent promising, non-invasive approaches for leaf segmentation, leaf counting, and tracking plant growth. This paper discusses an interesting aspect of the recent best-performing deep learning works: the fact that main contribution comes from novel data augmentation techniques. Experiments are set to highlight the significance of data augmentation practices for limited data sets with narrow distributions. This paper reviews the ingenious techniques to generate synthetic data to augment training and performs experiments to attest of their potential importance.
| Original language | English |
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| Title of host publication | 2020 Digital Image Computing |
| Subtitle of host publication | Techniques and Applications (DICTA) |
| Place of Publication | United States |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Number of pages | 8 |
| ISBN (Print) | 9781728191089 |
| DOIs | |
| Publication status | E-pub ahead of print - 20 Sept 2020 |
| Event | 2020 Digital Image Computing: Techniques and Applications: DICTA 2020 - Virtual, Australia Duration: 30 Nov 2020 → 02 Dec 2020 http://www.dicta2020.org/ |
Conference
| Conference | 2020 Digital Image Computing: Techniques and Applications |
|---|---|
| Country/Territory | Australia |
| Period | 30/11/20 → 02/12/20 |
| Other | The International Conference on Digital Image Computing: Techniques and Applications (DICTA) is the flagship Australian Conference on computer vision, image processing, pattern recognition, and related areas. |
| Internet address |