Identify The Beehive Sound Using Deep Learning
This work addresses the decline of honeybees, which pollinate 75% of flowering plants, by providing a monitoring tool, but it is incremental as it applies standard deep learning methods to a specific domain.
The paper tackled the problem of detecting changes in beehive sounds using acoustic classification to monitor honeybee populations, achieving classification of bee sounds from non-beehive noises with verification on combined recorded sounds (25-75% noises).
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 Naïve Bayes, with the deep learning techniques. The techniques are also verified on the combined recorded sounds (25-75% noises).