IOT-Based Intelligent Beehive Monitoring System using Acoustic and Environmental Analytics
Abstract
In the article, an IoT-based intelligent beehive monitoring system is developed by integrating acoustic signal processing with environmental monitoring for non-invasive, continuous monitoring of the health and behavior of the beehive population. An ESP32 microcontroller is used to collect the acoustic signal from the beehive and environmental parameters such as microclimate, send the signals to the cloud server for feature extraction, and perform the required signal processing for feature extraction, such as FFT, MFCC, etc., and send the results to the cloud server for monitoring the beehive population and send alerts in case of any issues such as swarming, queen loss, disease-related stress, etc., in the beehive population. The results obtained by testing the proposed system in experimental beehives indicate that the proposed system is capable of monitoring the beehive population by identifying the acoustic signals and microclimate changes that occur in the beehive population before the actual issues arise, thereby reducing the need for invasive monitoring of the beehive population.
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