Summary of Active Learning For Efficient Annotation in Precision Agriculture: a Use-case on Crop-weed Semantic Segmentation, by Bart M. Van Marrewijk et al.
Active learning for efficient annotation in precision agriculture: a use-case on crop-weed semantic segmentation
by Bart M. van Marrewijk, Charbel Dandjinou, Dan Jeric Arcega Rustia, Nicolas Franco Gonzalez, Boubacar Diallo, Jérôme Dias, Paul Melki, Pieter M. Blok
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach to optimizing deep learning models for semantic segmentation tasks is proposed, leveraging active learning to reduce annotation effort. The authors explore three acquisition functions – Bayesian Active Learning by Disagreement (BALD), stochastic-based BALD (PowerBALD), and Random – on two agricultural datasets: Sugarbeet and Corn-Weed. These datasets contain three semantic classes: background, crop, and weed. Results show that active learning, particularly PowerBALD, outperforms random sampling on both datasets. However, due to high image redundancy and class imbalance (89% of pixels belong to the background class), further research is needed to apply active learning effectively in agricultural settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Active learning can help reduce the time and cost required for annotating images for deep learning models. The study focuses on using this approach for semantic segmentation, which requires labeling every pixel. Three methods – BALD, PowerBALD, and Random – were tested on two agricultural datasets with three classes: background, crop, and weed. The results show that active learning can improve performance, but more work is needed to make it effective in agriculture where images can be very similar and some classes are much larger than others. |
Keywords
» Artificial intelligence » Active learning » Deep learning » Semantic segmentation
