Healthy and Anomalous Beehives Classification Model using Convolutional Neural Networks

Abstract

One of the main problems in chilean beekeeping is the late diseases diagnosis that affects beehives. In this work, convolutional neuronal networks are used to create a system that detect beehives health by classifying the sound they emit represented by spectrograms. A dataset is made from audio registers recorded in Chile. From this data, two models for beehives classification are elaborated with different architectures. The model implemented through Transfer Learning obtains a high percentage of accuracy (0.9303 in validation) at classifying recordings according to their health condition, which is comparable to other related publications about Machine Learning applied in beekeeping.