Summary of Energy-efficient Uncertainty-aware Biomass Composition Prediction at the Edge, by Muhammad Zawish et al.
Energy-Efficient Uncertainty-Aware Biomass Composition Prediction at the Edge
by Muhammad Zawish, Paul Albert, Flavio Esposito, Steven Davy, Lizy Abraham
First submitted to arxiv on: 17 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 proposed solution for estimating dry biomass composition from grass images in fields, leveraging filter pruning to reduce energy requirements and enabling deployment on edge devices like smartphones. The approach uses a hybrid model that combines a pruned network with an unpruned one, allowing for reduced energy consumption while maintaining high accuracy. Tested on the GrassClover and Irish clover datasets using an NVIDIA Jetson Nano edge device, the solution achieves an average 50% reduction in energy consumption with only a 4% loss in accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Farmers can estimate dry biomass composition from grass images without needing to analyze them manually or rely on expensive lab equipment. This helps farmers make better decisions about fertilization and sowing. A team proposed using deep learning algorithms, but these require lots of energy. To fix this, they pruned the networks to use less energy. However, the pruned models didn’t work well in real-world situations with blurry or angled images. So, they added a special “uncertainty” feature that helps them predict when their answers might be wrong. When that happens, they use an even more accurate model to get a better answer. This new approach works really well and uses less energy than before. |
Keywords
» Artificial intelligence » Deep learning » Pruning
