  Honey bees pollinate about one-third of the world's food supply, but bee
colonies have alarmingly declined by nearly 40% over the past decade due to
several factors, including pesticides and pests. Traditional methods for
monitoring beehives, such as human inspection, are subjective, disruptive, and
time-consuming. To overcome these limitations, artificial intelligence has been
used to assess beehive health. However, previous studies have lacked an
end-to-end solution and primarily relied on data from a single source, either
bee images or sounds. This study introduces a comprehensive system consisting
of bee object detection and health evaluation. Additionally, it utilized a
combination of visual and audio signals to analyze bee behaviors. An
Attention-based Multimodal Neural Network (AMNN) was developed to adaptively
focus on key features from each type of signal for accurate bee health
assessment. The AMNN achieved an overall accuracy of 92.61%, surpassing eight
existing single-signal Convolutional Neural Networks and Recurrent Neural
Networks. It outperformed the best image-based model by 32.51% and the top
sound-based model by 13.98% while maintaining efficient processing times.
Furthermore, it improved prediction robustness, attaining an F1-score higher
than 90% across all four evaluated health conditions. The study also shows that
audio signals are more reliable than images for assessing bee health. By
seamlessly integrating AMNN with image and sound data in a comprehensive bee
health monitoring system, this approach provides a more efficient and
non-invasive solution for the early detection of bee diseases and the
preservation of bee colonies.
