A machine learning-based multiclass classification model for bee colony anomaly identification using an IoT-based audio monitoring system with an edge computing framework

Abstract

Approximately one-third of human food is derived from flowering plants, and 80% of those plants require honeybees for pollination. However, climate change is affecting bees as well as the beekeeping industry. Having continuous information on the condition of honey bee colonies would be helpful in studying new diseases such as colony collapse disorder, as well as in developing novel beekeeping tools to improve the health management of bee colonies. In this study, an audio monitoring system is established based on Internet of Thing (IoT) technology to record the sounds produced by bees. A colony status classification model coupled with a machine learning algorithm is used to analyze the sound data of bee colonies under various treatments to classify the colony conditions, such as queenless, virus-infected, and pesticide-contaminated. With an edge computing framework, the classification is done by the monitoring system, which greatly reduces data transmission delay and cloud computing burden. The field test results show that the accuracy andF1-score for the classification model for the beehive under various treatments reach 98.8% and 0.98, respectively.The proposed IoT-based audio monitoring system for beehives can assist beekeepers and users in managing bee colonies more effectively by providing automatic, accurate, and real-time monitoring information regarding the current colony conditions.