Summary of Computer Vision Approaches For Automated Bee Counting Application, by Simon Bilik et al.
Computer Vision Approaches for Automated Bee Counting Application
by Simon Bilik, Ilona Janakova, Adam Ligocki, Dominik Ficek, Karel Horak
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper addresses a crucial challenge in bee colony health state monitoring: developing an efficient way to count incoming and outgoing bees. This task has numerous applications, such as analyzing trends related to colony health, blooming periods, or investigating the effects of agricultural spraying. The authors compare three methods for automated bee counting using two own datasets. Notably, a ResNet-50 convolutional neural network classifier outperforms the others, achieving 87% accuracy on the BUT1 dataset and 93% on the BUT2 dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand bees! It’s about creating a way to count how many bees come in and go out of their colonies. This is important because it can help scientists figure out if the bee colony is healthy, when flowers bloom, or what happens when farmers spray pesticides. The researchers tested three methods for counting bees using two special datasets. They found that a special kind of computer program called ResNet-50 works best, getting 87% right on one dataset and 93% right on another. |
Keywords
» Artificial intelligence » Neural network » Resnet
