Identify The Beehive Sound Using Deep Learning

Flowers play an essential role in removing the duller from the environment. The life cycle of the flowering plants involves pollination, fertilization, flowering, seed-formation, dispersion, and germination. Honeybees pollinate approximately 75% of all flowering plants. Environmental pollution, clim...

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Bibliographic Details
Published in:arXiv.org 2022-09
Main Authors: Shah Jafor Sadeek Quaderi, Sadia Afrin Labonno, Mostafa, Sadia, Akhter, Shamim
Format: Article
Language:English
Subjects:
Acoustics
Artificial neural networks
Bees
Comparative studies
Decision trees
Deep learning
Flowering
Germination
Machine learning
Neural networks
Plants (botany)
Recurrent neural networks
Support vector machines
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Summary:Flowers play an essential role in removing the duller from the environment. The life cycle of the flowering plants involves pollination, fertilization, flowering, seed-formation, dispersion, and germination. Honeybees pollinate approximately 75% of all flowering plants. Environmental pollution, climate change, natural landscape demolition, and so on, threaten the natural habitats, thus continuously reducing the number of honeybees. As a result, several researchers are attempting to resolve this issue. Applying acoustic classification to recordings of beehive sounds may be a way of detecting changes within them. In this research, we use deep learning techniques, namely Sequential Neural Network, Convolutional Neural Network, and Recurrent Neural Network, on the recorded sounds to classify bee sounds from the nonbeehive noises. In addition, we perform a comparative study among some popular non-deep learning techniques, namely Support Vector Machine, Decision Tree, Random Forest, and Na\"ive Bayes, with the deep learning techniques. The techniques are also verified on the combined recorded sounds (25-75% noises).
ISSN:2331-8422