# Computer Vision Approaches for Automated Bee Counting Application

arXiv: https://arxiv.org/abs/2406.08898 · HTML: https://mlsanity.com/paper/2406.08898/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks](https://mlsanity.com/paper/1812.11703/) (2018)
- [Machine Learning and Computer Vision Techniques in Continuous Beehive Monitoring Applications: A survey](https://mlsanity.com/paper/2208.00085/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Management](https://mlsanity.com/paper/2509.08822/) (2025)

## Abstract (arXiv)

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.