Computer Vision Approaches for Automated Bee Counting Application
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913390191968256 |
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| author | Bilik, Simon Janakova, Ilona Ligocki, Adam Ficek, Dominik Horak, Karel |
| author_facet | Bilik, Simon Janakova, Ilona Ligocki, Adam Ficek, Dominik Horak, Karel |
| contents | Many application from the bee colony health state monitoring could be efficiently solved using a computer vision techniques. One of such challenges is an efficient way for counting the number of incoming and outcoming bees, which could be used to further analyse many trends, such as the bee colony health state, blooming periods, or for investigating the effects of agricultural spraying. In this paper, we compare three methods for the automated bee counting over two own datasets. The best performing method is based on the ResNet-50 convolutional neural network classifier, which achieved accuracy of 87% over the BUT1 dataset and the accuracy of 93% over the BUT2 dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08898 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Computer Vision Approaches for Automated Bee Counting Application Bilik, Simon Janakova, Ilona Ligocki, Adam Ficek, Dominik Horak, Karel Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Many application from the bee colony health state monitoring could be efficiently solved using a computer vision techniques. One of such challenges is an efficient way for counting the number of incoming and outcoming bees, which could be used to further analyse many trends, such as the bee colony health state, blooming periods, or for investigating the effects of agricultural spraying. In this paper, we compare three methods for the automated bee counting over two own datasets. The best performing method is based on the ResNet-50 convolutional neural network classifier, which achieved accuracy of 87% over the BUT1 dataset and the accuracy of 93% over the BUT2 dataset. |
| title | Computer Vision Approaches for Automated Bee Counting Application |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2406.08898 |