Based investigate of beehive sound to detect air pollutants by machine learning

Abstract As honey bees are extremely sensitive to a variety of chemicals and emit typical sound when exposed to environmental chemical, the sound of beehive may be explored as signal to monitor atmospheric pollutants. In the study, the beehives were exposed to the common air pollution chemicals of acetone, Trichloromethane, Glutaric dialdehyde and ethyl ether, and collect beehive sound data using a beehive sound acquisition device developed by ourselves. We found that the sound of honey bees has a certain relationship with the surrounding chemistry and pollution. Based on the features of Mel frequency cepstral coefficients (MFCCs) extracted from beehive sound data, we have builted the support vector machine (SVM) model provided the accuracy rate of 93.7% for classifying beehive sounds associated with different compounds. Our method outperformed other classification algorithms in terms of accuracy when applied to preprocessed teat data (93.7% average accuracy compared to the 83.8% achieved by KNN and the 83.6% achieved by RF). The results indicated that the beehive sound analysis can provide qualitative chemical information about the air surrounding the beehives. The study suggestd that monitoring the beehive sounds would become a promising way to monitor the air quality