Summary of Shape and Style Gan-based Multispectral Data Augmentation For Crop/weed Segmentation in Precision Farming, by Mulham Fawakherji et al.
Shape and Style GAN-based Multispectral Data Augmentation for Crop/Weed Segmentation in Precision Farming
by Mulham Fawakherji, Vincenzo Suriani, Daniele Nardi, Domenico Daniele Bloisi
First submitted to arxiv on: 19 Jul 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 The paper presents a method for augmenting training data in precision farming using Generative Adversarial Networks (GANs). Traditional data augmentation techniques can be costly and challenging due to the need to collect information during different growing stages. The proposed approach uses two GANs to create artificial images by replacing patches containing objects of interest with new ones, considering both foreground (crop samples) and background (soil) patches. Experimental results on publicly available datasets demonstrate the effectiveness of the method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using special computer programs called GANs to make more training data for farmers who use computers to help them grow crops better. Right now, it’s hard and expensive to get this data, but these GANs can create fake images that look like real ones, which helps the computers learn faster. |
Keywords
* Artificial intelligence * Data augmentation * Precision
