Unsupervised Acoustic Detection of Queenless Hives in Honeybees (Apis mellifera ligustica)
Resumo
The absence of a queen bee directly compromises the survival of the colony. Accurately detecting this condition is essential for modern beekeeping, but traditional methods rely on invasive manual inspections. In this paper, we propose an automated and non-invasive approach based on unsupervised machine learning to identify queenless hives through bee sound analysis. Mel spectrograms extracted from real audio recordings of Apis mellifera ligustica were processed using a convolutional autoencoder. The resulting latent representations were clustered using HDBSCAN, and labels were applied only in the final validation step. The model achieved 98.78% accuracy, outperforming supervised approaches reported in the literature. To the best of our knowledge, no previous study has investigated the acoustic detection of queenless honeybee hives using unsupervised learning. Our results show the potential of unsupervised bioacoustic analysis and support advances in precision beekeeping within data-centric e-Science.
Palavras-chave:
precision beekeeping, machine learning, Hdbscan, neural networks, data science
Referências
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Calinski, T. and Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics - Theory and Methods, 3(1):1–27.
Davies, D. L. and Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2):224–227.
FAO (2018). Bees and other pollinators crucial to food security, biodiversity. Accessed: 2025-06-26.
Klein, A.-M., Vaissiere, B. E., Cane, J. H., Steffan-Dewenter, I., Cunningham, S. A., Kremen, C., and Tscharntke, T. (2007). Importance of pollinators in changing landscapes for world crops. Proceedings of the royal society B: biological sciences, 274(1608):303–313.
Michelsen, A., Kirchner, W. H., Andersen, B. B., and Lindauer, M. (1986). The tooting and quacking vibration signals of honeybee queens: a quantitative analysis. Journal of Comparative Physiology A, 158(5):605–611.
Nolasco, I., Terenzi, A., Cecchi, S., Orcioni, S., Bear, H. L., and Benetos, E. (2019a). Audio-based identification of beehive states. In ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8256– 8260. IEEE.
Nolasco, I., Terenzi, A., Cecchi, S., Orcioni, S., Bear, H. L., and Benetos, E. (2019b). Audio-based identification of beehive states: The dataset.
Orlowska, A., Fourer, D., Gavini, J.-P., and Cassou-Ribehart, D. (2022). Honey Bee Queen Presence Detection from Audio Field Recordings Using Summarized Spectrogram and Convolutional Neural Networks, page 83–92. Springer International Publishing.
Otesbelgue, A., de Lima Rodrigues, I., dos Santos, C. F., Gomes, D. G., and Blochtein, B. (2025). The missing queen: a non-invasive method to identify queenless stingless bee hives. Apidologie, 56(2).
Potts, S. G., Biesmeijer, J. C., Kremen, C., Neumann, P., Schweiger, O., and Kunin, W. E. (2010). Global pollinator declines: trends, impacts and drivers. Trends in Ecology & Evolution, 25(6):345–353.
Quaderi, S. J. S., Labonno, S. A., Mostafa, S., and Akhter, S. (2022). Identify the beehive sound using deep learning. arXiv preprint arXiv:2209.01374.
Rafael Braga, A., de Castro Rabelo, J., de Castro Callado, A., Rego da Rocha, A., M. Freitas, B., and G. Gomes, D. (2020). Beenotified! a notification system of physical quantities for beehives remote monitoring. Revista de Informática Teórica e Aplicada, 27(3):50–61.
Robles-Guerrero, A., Saucedo-Anaya, T., González-Ramérez, E., and Galván-Tejada, C. E. (2017). Frequency analysis of honey bee buzz for automatic recognition of health status: A preliminary study. Research in Computing Science, 142(1):89–98.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20:53–65.
Santos, I. R. d., Araújo, F. H. D. d., and Magalhães, D. M. V. (2025). Análise comparativa de modelos de classificação de áudio de colmeias de abelhas em dispositivos portáteis android com onnxruntime. Brazilian Journal of Development, 11(2):e78007.
Sánchez-Bayo, F. and Wyckhuys, K. (2019). Worldwide decline of the entomofauna: A review of its drivers. Biological Conservation, 232.
Uthoff, C., Homsi, M. N., and von Bergen, M. (2023). Acoustic and vibration monitoring of honeybee colonies for beekeeping-relevant aspects of presence of queen bee and swarming. Computers and Electronics in Agriculture, 205:107589.
Woods, E. F. (1956). Queen piping. Bee World, 37(10):185–195.
Zacepins, A., Brusbardis, V., Meitalovs, J., and Stalidzans, E. (2015). Challenges in the development of precision beekeeping. Biosystems Engineering, 130:60–71.
Brown, M. J., Dicks, L. V., Paxton, R. J., Baldock, K. C., Barron, A. B., Chauzat, M.-P., Freitas, B. M., Goulson, D., Jepsen, S., Kremen, C., et al. (2016). A horizon scan of future threats and opportunities for pollinators and pollination. PeerJ, 4:e2249.
Calinski, T. and Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics - Theory and Methods, 3(1):1–27.
Davies, D. L. and Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2):224–227.
FAO (2018). Bees and other pollinators crucial to food security, biodiversity. Accessed: 2025-06-26.
Klein, A.-M., Vaissiere, B. E., Cane, J. H., Steffan-Dewenter, I., Cunningham, S. A., Kremen, C., and Tscharntke, T. (2007). Importance of pollinators in changing landscapes for world crops. Proceedings of the royal society B: biological sciences, 274(1608):303–313.
Michelsen, A., Kirchner, W. H., Andersen, B. B., and Lindauer, M. (1986). The tooting and quacking vibration signals of honeybee queens: a quantitative analysis. Journal of Comparative Physiology A, 158(5):605–611.
Nolasco, I., Terenzi, A., Cecchi, S., Orcioni, S., Bear, H. L., and Benetos, E. (2019a). Audio-based identification of beehive states. In ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8256– 8260. IEEE.
Nolasco, I., Terenzi, A., Cecchi, S., Orcioni, S., Bear, H. L., and Benetos, E. (2019b). Audio-based identification of beehive states: The dataset.
Orlowska, A., Fourer, D., Gavini, J.-P., and Cassou-Ribehart, D. (2022). Honey Bee Queen Presence Detection from Audio Field Recordings Using Summarized Spectrogram and Convolutional Neural Networks, page 83–92. Springer International Publishing.
Otesbelgue, A., de Lima Rodrigues, I., dos Santos, C. F., Gomes, D. G., and Blochtein, B. (2025). The missing queen: a non-invasive method to identify queenless stingless bee hives. Apidologie, 56(2).
Potts, S. G., Biesmeijer, J. C., Kremen, C., Neumann, P., Schweiger, O., and Kunin, W. E. (2010). Global pollinator declines: trends, impacts and drivers. Trends in Ecology & Evolution, 25(6):345–353.
Quaderi, S. J. S., Labonno, S. A., Mostafa, S., and Akhter, S. (2022). Identify the beehive sound using deep learning. arXiv preprint arXiv:2209.01374.
Rafael Braga, A., de Castro Rabelo, J., de Castro Callado, A., Rego da Rocha, A., M. Freitas, B., and G. Gomes, D. (2020). Beenotified! a notification system of physical quantities for beehives remote monitoring. Revista de Informática Teórica e Aplicada, 27(3):50–61.
Robles-Guerrero, A., Saucedo-Anaya, T., González-Ramérez, E., and Galván-Tejada, C. E. (2017). Frequency analysis of honey bee buzz for automatic recognition of health status: A preliminary study. Research in Computing Science, 142(1):89–98.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20:53–65.
Santos, I. R. d., Araújo, F. H. D. d., and Magalhães, D. M. V. (2025). Análise comparativa de modelos de classificação de áudio de colmeias de abelhas em dispositivos portáteis android com onnxruntime. Brazilian Journal of Development, 11(2):e78007.
Sánchez-Bayo, F. and Wyckhuys, K. (2019). Worldwide decline of the entomofauna: A review of its drivers. Biological Conservation, 232.
Uthoff, C., Homsi, M. N., and von Bergen, M. (2023). Acoustic and vibration monitoring of honeybee colonies for beekeeping-relevant aspects of presence of queen bee and swarming. Computers and Electronics in Agriculture, 205:107589.
Woods, E. F. (1956). Queen piping. Bee World, 37(10):185–195.
Zacepins, A., Brusbardis, V., Meitalovs, J., and Stalidzans, E. (2015). Challenges in the development of precision beekeeping. Biosystems Engineering, 130:60–71.
Publicado
29/09/2025
Como Citar
M. CARVALHO JR., Cleiton; RODRIGUES, Ícaro de Lima; G. GOMES, Danielo.
Unsupervised Acoustic Detection of Queenless Hives in Honeybees (Apis mellifera ligustica). In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 19. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 49-56.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2025.248141.
