Summary of Importance Of Realism in Procedurally-generated Synthetic Images For Deep Learning: Case Studies in Maize and Canola, by Nazifa Azam Khan et al.
Importance of realism in procedurally-generated synthetic images for deep learning: case studies in maize and canola
by Nazifa Azam Khan, Mikolaj Cieslak, Ian McQuillan
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers investigate the potential of procedurally generated plant simulations to augment or replace real-world images in training artificial neural networks for phenotyping tasks. By using Lindenmayer systems (L-systems) to create visually realistic simulations of crops like maize and canola, they demonstrate that synthetic images can improve prediction accuracy when combined with real images. The authors also explore the impact of realism levels on performance and show how calibrated L-systems can be fine-tuned using neural network predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Plants are hard to identify because there are so many different kinds! Scientists use special computers called artificial neural networks to help them recognize features in plants, but these computers need lots of pictures to learn. That’s a problem because taking and labeling those pictures is very time-consuming and expensive. A new way to make fake plant pictures using a system called Lindenmayer systems (L-systems) can help solve this problem. By combining real and fake pictures, scientists might be able to improve how well their computers recognize plants. This paper looks at whether making these fake pictures more realistic helps or hurts the computer’s ability to identify plants. |
Keywords
* Artificial intelligence * Neural network
