Using machine learning for audio-based identification of beehive states

Researchers at Università Politecnica delle Marche, Queen Mary University of London and the Alan Turing Institute have recently collaborated on a research project aimed at identifying beehive states using machine learning. Their study, pre-published on arXiv, investigated the use of both support vector machines (SVMs) and convolutional neural networks (CNNs) for beehive state recognition, using audio data. The data used in this study was collected as part of the NU-Hive project, a research endeavor that led to the development of a system to monitor the condition of beehives by exploiting the sounds they emit. The researchers trained machine learning algorithms to analyze this audio data and identify the states of different beehives. "Our research is motivated by the decline in honeybee colonies over recent years in Europe and the rest of the world," Stefania Cecchi, a researcher who carried out the study, told TechXplore.