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Computer Science > Computer Vision and Pattern Recognition

arXiv:2406.08898 (cs)
[Submitted on 13 Jun 2024]

Title:Computer Vision Approaches for Automated Bee Counting Application

Authors:Simon Bilik, Ilona Janakova, Adam Ligocki, Dominik Ficek, Karel Horak
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Abstract: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.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2406.08898 [cs.CV]
  (or arXiv:2406.08898v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2406.08898

Submission history

From: Šimon Bilík [view email]
[v1] Thu, 13 Jun 2024 07:51:08 UTC (264 KB)
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