Predictive Hive Health Management using IoT and Linear Regression for Beekeeping and Pollinator Conservation

Abstract

Bee numbers have declined worldwide, raising worries about pollinator conservation and its effects on agriculture and ecosystems. The research develops a predictive hive health monitoring system using Internet of Things (IoT) technologies and linear regression to promote sustainable beekeeping and pollinator conservation. The system uses beehive IoT sensors to collect real-time data on environmental parameters, hive conditions, and bee behavior. The method uses linear regression to forecast hive conditions and possible difficulties, including disease outbreaks, insect infestations, and environmental stresses. Beekeepers may use constant monitoring and analysis to detect problems and take action to keep their colonies healthy and productive. The system's predictive capabilities enable preventative interventions to reduce colony collapse disorder and other threats, preserving pollinator populations. The proposed system gives beekeeper real-time input and decision assistance, enabling proactive hive management. IoT technology also improves system scalability and accessibility, allowing remote beehive monitoring and administration across several sites.