Aquisição e Classificação da Intensidade da Colmeia usando Características Cepstrais
Abstract
The management of hives is fundamental for the maintenance of the beekeeping chain. However, constant revisions, especially during the honey harvest period, cause stress and contribute to swarm loss during the food shortage period. Therefore, this management needs to be fast, safe and non-invasive. This work combines the audio processing produced by the colonies associated with machine learning techniques to identify the intensity of the hive. The results suggest that the coefficients are effective in describing the hive intensity achieving an average accuracy above 97% for three different classifiers, which can help the beekeeper in decision making about which hives to use in honey collection.References
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Virtanen, T., Plumbley, M. D., and Ellis, D. (2018). Computational analysis of sound scenes and events. Springer.
Wardhani, N. W. S., Rochayani, M. Y., Iriany, A., Sulistyono, A. D., and Lestantyo, P. (2019). Cross-validation metrics for evaluating classification performance on imbalanced data. In International conference on computer, control, informatics and its applications, pages 14–18. IEEE.
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Breiman, L. (2001). Random forests. Machine learning, 45:5–32.
Bromenshenk, J. J., Henderson, C. B., Seccomb, R. A., Rice, S. D., and Etter, R. T. (2009). Honey bee acoustic recording and analysis system for monitoring hive health. US Patent 7,549,907.
Cejrowski, T., Szymański, J., and Logofătu, D. (2020). Buzz-based recognition of the honeybee colony circadian rhythm. Computers and Electronics in Agriculture, 175:105586.
Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine learning, 20:273–297.
Gorroi, G., Freitas, L. P. V. d., and Assis, D. C. S. d. (2020). Apicultura: o manejo das abelhas do gênero apis. Cad. técn. Vet. Zoot., pages 9–36.
Haykin, S. (2001). Redes neurais: princípios e prática. Bookman Editora.
Heise, D., Miller, Z., Wallace, M., and Galen, C. (2020). Bumble bee traffic monitoring using acoustics. In 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pages 1–6. IEEE.
Kim, J., Oh, J., and Heo, T.-Y. (2021). Acoustic scene classification and visualization of beehive sounds using machine learning algorithms and grad-cam. Mathematical Problems in Engineering, 2021:1–13.
Kulyukin, V. (2021). Audio, image, video, and weather datasets for continuous electronic beehive monitoring. Applied Sciences, 11(10):4632.
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8):2674.
McFee, B., Raffel, C., Liang, D., Ellis, D. P., McVicar, M., Battenberg, E., and Nieto, O. (2015). librosa: Audio and music signal analysis in python. In Proceedings of the 14th python in science conference, volume 8, pages 18–25.
Oliveira Costa, R., Bezerra, A. H. A., Ferreira, A. C., Pereira, B. B. M., Pimenta, T. A., and de Andrade, A. B. A. (2016). Análise hierárquica dos problemas existentes na produção de mel do estado da paraíba. Revista Verde de Agroecologia e Desenvolvimento Sustentável, 11(2):24–28.
Ruvinga, S., Hunter, G. J., Duran, O., and Nebel, J.-C. (2021). Use of lstm networks to identify “queenlessness” in honeybee hives from audio signals. In 2021 17th International Conference on Intelligent Environments (IE), pages 1–4. IEEE.
Shaghaghi, N., Liang, L., Yabe, Y., Lama, S., Mayer, J., and Ferguson, P. (2019). Identifying beehive frames ready for harvesting. In 2019 IEEE Global Humanitarian Technology Conference (GHTC), pages 1–4. IEEE.
Sharif, M. Z., Wario, F., Di, N., Xue, R., and Liu, F. (2020). Soundscape indices: new features for classifying beehive audio samples. Sociobiology, 67(4):566–571.
Shostak, S. and Prodeus, A. (2019). Classification of the bee colony condition using spectral features. In 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T), pages 737–740. IEEE.
Soares, B. S., Luz, J. S., de Macêdo, V. F., e Silva, R. R. V., de Araújo, F. H. D., and Magalhães, D. M. V. (2022). Mfcc-based descriptor for bee queen presence detection. Expert Systems with Applications, 201:117104.
Vieira, F. R., Andrade, D. C., and Ribeiro, F. L. (2021). A polinização por abelhas sob a perspectiva da abordagem de serviços ecossistêmicos (ase). Revista Ibero-Americana de Ciências Ambientais, 12(4):544–560.
Virtanen, T., Plumbley, M. D., and Ellis, D. (2018). Computational analysis of sound scenes and events. Springer.
Wardhani, N. W. S., Rochayani, M. Y., Iriany, A., Sulistyono, A. D., and Lestantyo, P. (2019). Cross-validation metrics for evaluating classification performance on imbalanced data. In International conference on computer, control, informatics and its applications, pages 14–18. IEEE.
Zgank, A. (2021). Iot-based bee swarm activity acoustic classification using deep neural networks. Sensors, 21(3):676.
Published
2023-08-06
How to Cite
OLIVEIRA, Myllena C. de; PEREIRA, Fábia de M.; MOURA, Vanessa G. de; BRITO, Marcos A. G. B.; SANTOS, Breno R. dos; OLIVEIRA, Mayra C. de; MAGALHÃES, Deborah M. V..
Aquisição e Classificação da Intensidade da Colmeia usando Características Cepstrais. In: PROCEEDINGS OF BRAZILIAN SYMPOSIUM ON UBIQUITOUS AND PERVASIVE COMPUTING (SBCUP), 15. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 31-40.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2023.230536.
