Bioacoustic Patterns as Accurate Identifiers of Queen Presence in Honey Bee Hives
Abstract
The queen bee is responsible for her colony's growth, renewal and organizational stability. To know weather a honey queen is inside the hive, the beekeeper has to open it, which stresses the bees, destroys part of the nest and causes bee workers' death. Classifying the queen's presence through the colony audio is a non-invasive inspection method that can keep the colony well-being. However, bioacoustic patterns generate a considerable volume of data. Incremental classifiers with a daily recording ratio could garantee efficiency while reducing the bottleneck. Here we evaluate the performance of three classifiers: Hoeffding Tree, Random Forest and Naive Bayes. Naive Bayes showed the best results with 10 windows of 1s /day, response time of 0.93s and average accuracy of 97%.
Keywords:
bioacoustics, honey bees, incremental classifiers, audio processing, machine learning
References
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Braga, A. R., Gomes, D. G., Rogers, R., Hassler, E. E., Freitas, B. M., and Cazier, J. A. (2020). A method for mining combined data from in-hive sensors, weather and apiary inspections to forecast the health status of honey bee colonies. Computers and Electronics in Agriculture, 169:105161.
Dineva, K., Atanasova, T., et al. (2018). Osemn process for working over data acquired by iot devices mounted in beehives. Curr. Trends Nat. Sci, 7(13):47–53.
Giannakopoulos, T. (2015). pyaudioanalysis: An open-source python library for audio signal analysis. PLOS ONE, 10(12):1–17.
Hadjur, H., Ammar, D., and Lefévre, L. (2022). Toward an intelligent and efficient beehive: A survey of precision beekeeping systems and services. Computers and Electronics in Agriculture, 192:106604.
Howard, D., Duran, O., Hunter, G., and Stebel, K. (2013). Signal processing the acoustics of honeybees (apis mellifera) to identify the ”queenless”state in hives. Proceedings of the Institute of Acoustics, 35:290–297.
Meikle, W. G., Weiss, M., Maes, P. W., Fitz, W., Snyder, L. A., Sheehan, T., Mott, B. M., and Anderson, K. E. (2017). Internal hive temperature as a means of monitoring honey bee colony health in a migratory beekeeping operation before and during winter. Apidologie, 48(5):666–680.
Peng, R., Ardekani, I., and Sharifzadeh, H. (2020). An acoustic signal processing system for identification of queen-less beehives. In 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pages 57–63.
Rodrigues, I., Melo, D., Freitas, B., and G. Gomes, D. (2021). Detecção de anomalias em padrões acústicos, de temperatura e umidade sazonais para abelhas melíferas (apis mellifera l.). In Anais do XII Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais, pages 69–78, Porto Alegre, RS, Brasil. SBC.
Silva, D., Ícaro Rodrigues, Braga, A., Nobre, J., Freitas, B., and Gomes, D. (2020). An autonomic, adaptive and high-precision statistical model to determine bee colonies well-being scenarios. In Anais do XI Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais, pages 31–40, Porto Alegre, RS, Brasil. SBC.
Wilk, J. T., Bak, B., Artiemjew, P., Wilde, J., and Siuda, M. (2021). Classifying the biological status of honeybee workers using gas sensors. Sensors, 21(1).
Winston, M. L. (1991). The biology of the honey bee. harvard university press.
Wobbrock, J. O., Findlater, L., Gergle, D., and Higgins, J. J. (2011). The aligned rank transform for nonparametric factorial analyses using only anova procedures. In Proceedings of the SIGCHI conference on human factors in computing systems, pages 143–146.
Zgank, A. (2018). Acoustic monitoring and classification of bee swarm activity using mfcc feature extraction and hmm acoustic modeling. In 2018 ELEKTRO, pages 1–4.
Published
2022-07-31
How to Cite
RODRIGUES, Ícaro de Lima; MELO, Davyd B. de; SILVA, Daniel de Amaral da; RYBARCZYK, Yves; GOMES, Danielo G..
Bioacoustic Patterns as Accurate Identifiers of Queen Presence in Honey Bee Hives. In: WORKSHOP ON COMPUTING APPLIED TO THE MANAGEMENT OF THE ENVIRONMENT AND NATURAL RESOURCES (WCAMA), 13. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 11-20.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2022.222913.
