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

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...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2023, Vol.14 (8)
Main Authors: Le, Thi-Nhung, Le, Thi-Minh-Thuy, Phan, Thi-Thu-Hong, Nguyen, Huu-Du, Le, Thi-Lan
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Artificial neural networks
Beekeeping
Bees
Classification
Computer architecture
Computer science
Datasets
Deep learning
Experiments
Foraging behavior
Fruits
Honey
Internet of Things
Kalman filters
Machine learning
Monitoring systems
Neural networks
Plant reproduction
Pollen
Support vector machines
System effectiveness
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Summary: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.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2023.01408112