The determination of the developments of beehives via artificial neural networks

Abstract

Honeybees provide great benefits for people both with the foods they produce and as a pollinator. It is known that they pass the whole year with the foods they collect in spring and summer months. Beekeepers also benefit from the honey produced in these periods. Whether a beehive works adequately or not and its status of development can be understood through the observations by beekeepers. In this study, an Arduino-supported neural network model was developed in order to obtain information about the general situations of beehives. The three-input and three-output neural networks were embedded in a board after the training and testing stage. While temperature, humidity, and weight refer to inputs, good situation, stable situation, and bad situation represent outputs. The real-time model has an accuracy of 99.84%.