ApIsoT: An IoT Function Aggregation Mechanism for Detecting Varroa Infestation in Apis mellifera Species

Abstract

In recent years, the global reduction in populations of the Apis mellifera species has generated a worrying deterioration in the production of essential foods for human consumption. This phenomenon threatens food security, as it reduces the pollination of vital crops, negatively affecting the health and stability of ecosystems. The three main factors generating the loss of the bee population are industrial agriculture, climate changes, and infectious diseases, mainly those of parasitic origin, such as the Varroa destructor mite. This article proposes an IoT system that uses accessible, efficient, low-cost devices for beekeepers in developing countries to monitor hives based on temperature, humidity, CO2, and TVOC. The proposed solution incorporates nine-feature aggregation as a data preprocessing strategy to reduce redundancy and efficiently manage data storage on hardware with limited capabilities, which, combined with a machine learning model, improves mite detection. Finally, an evaluation of the energy consumption of the solution in each of its nodes, an analysis of the data traffic injected into the network, an assessment of the energy consumption of each implemented classification model, and, finally, a validation of the solution with experts is presented.