Computer Vision Approaches for Automated Bee Counting Application

TL;DR

This paper evaluates computer vision methods for automated bee counting, demonstrating that a ResNet-50 classifier achieves high accuracy in two datasets, aiding bee colony health monitoring and environmental studies.

Contribution

It compares three bee counting methods and identifies ResNet-50 as the most accurate approach for automated bee detection.

Findings

ResNet-50 achieved 87% accuracy on BUT1 dataset.

ResNet-50 achieved 93% accuracy on BUT2 dataset.

The approach supports monitoring bee colony health and environmental impacts.

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.