Use of LSTM Networks to Identify “Queenlessness” in Honeybee Hives from Audio Signals

Abstract

Honeybees are of vital importance to both agriculture and ecology, but honeybee populations have been in serious decline over recent years. The queen bee is of crucial importance to the success of a colony. In this paper, we contribute to addressing these problems by employing Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP) Neural Networks and Logistic Regression approaches applied to audio data recorded from “queen-absent” and “queen-present” hives to provide a method of prompt detection of a hive lacking a queen. The initial results-particularly from the LSTM - are highly encouraging.