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- W4386394487 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." @default.
- W4386394487 created "2023-09-03" @default.
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- W4386394487 date "2023-01-01" @default.
- W4386394487 modified "2023-09-27" @default.
- W4386394487 title "A Novel Convolutional Neural Network Architecture for Pollen-Bearing Honeybee Recognition" @default.
- W4386394487 doi "https://doi.org/10.14569/ijacsa.2023.01408112" @default.
- W4386394487 hasPublicationYear "2023" @default.
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