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DOI: 10.14569/IJACSA.2023.01408112
PDF

A Novel Convolutional Neural Network Architecture for Pollen-Bearing Honeybee Recognition

Author 1: Thi-Nhung Le
Author 2: Thi-Minh-Thuy Le
Author 3: Thi-Thu-Hong Phan
Author 4: Huu-Du Nguyen
Author 5: Thi-Lan Le

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 8, 2023.

  • Abstract and Keywords
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Abstract: Monitoring the pollen foraging behavior of honey-bees is an important task that is beneficial to beekeepers, allowing them to understand the health status of their honeybee colonies. To perform this task, monitoring systems should have the ability to automatically recognize images of pollen-bearing honeybees extracted from videos recorded at the beehive entrance. In this paper, a novel convolutional neural network architecture is proposed for recognizing pollen-bearing and non-pollen-bearing honeybees from their images. The performance of the proposed model is illustrated based on a real dataset and the obtained results show that it performs better than some other state-of-the-art deep learning architectures like VGG16, VGG19, or Resnet50 in terms of both accuracy and execution time. Thus, the proposed model can be considered an effective algorithm for designing automatic honeybee colony monitoring systems.

Keywords: Pollen-bearing honeybee; image classification; convolutional neural network; honeybee monitoring system; Pollen dataset

Thi-Nhung Le, Thi-Minh-Thuy Le, Thi-Thu-Hong Phan, Huu-Du Nguyen and Thi-Lan Le, “A Novel Convolutional Neural Network Architecture for Pollen-Bearing Honeybee Recognition” International Journal of Advanced Computer Science and Applications(IJACSA), 14(8), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01408112

@article{Le2023,
title = {A Novel Convolutional Neural Network Architecture for Pollen-Bearing Honeybee Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01408112},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01408112},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {8},
author = {Thi-Nhung Le and Thi-Minh-Thuy Le and Thi-Thu-Hong Phan and Huu-Du Nguyen and Thi-Lan Le}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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