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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

Author

Liang, Andrew

Published

2024

Language

EN