This paper is a proof of concept application of deep learning technologies towards honeybee colony monitoring. A goal was set to determine internal beehive temperature only through analysis of sound signals produced by the hive. Such a goal was not attempted before. Signals were acquired using an experimental monitoring station, which gathered data from both inside and outside the beehive, as well as recorded temperature inside the beehive. Features extracted from those signals were mel frequency cepstral coefficients and power spectral density. A deep learning convolutional network was employed in the analysis. All tested methods achieved satisfactory results and allowed sufficiently correct prediction of temperatures inside the beehive based on signals recorded by both an internal and an external microphone. Differences of results obtained using external and internal measurements were similar. This proof of concept serves as an indication of future research possibilities concerning automated acoustic monitoring of honeybee families. Such possibilities lie mainly within honeybee health monitoring to which goal this paper’s findings may be of use.
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