An Autonomic, Adaptive and High-Precision Statistical Model to Determine Bee Colonies Well-Being Scenarios

  • Daniel da Silva UFC
  • Ícaro Rodrigues UFC
  • Antonio Braga UFC
  • Juvêncio Nobre UFC
  • Breno Freitas UFC
  • Danielo Gomes UFC

Resumo


Honey bees, important pollinators, are threatened by a variety of pests, pathogens and extreme climatic events, such as the winter period. This paper proposes a two-stages model that seeks to define and predict evolutionary scenarios for improving the bee colonies’ well-being. The used dataset has data from both internal and external beehive sensors, and on-site inspection of beekeepers from six apiaries between the years 2016-2018. In the first stage, three evolutionary scenarios were obtained (pessimistic, conservative and optimistic) through the clustering technique. In the second one, aiming to classify these scenarios, an elastic net penalty logistic regression model was obtained with an accuracy of ~99.5%.

Palavras-chave: Honey bees, clustering techniques, logistic regression model

Referências

Abou-Shaara, H., Owayss, A., Ibrahim, Y., and Basuny, N. (2017). A review of impacts of temperature and relative humidity on various activities of honey bees. Insectes Sociaux, 64(4):455–463.

Barron, A. B. (2015). Death of the bee hive: understanding the failure of an insect society. Current Opinion in Insect Science, 10:45 – 50. Social Insects * Vectors andMedical and Veterinary Entomology.

Braga, A. R., Furtado, L., Bezerra, A. D., Freitas, B., Cazier, J., and Gomes, D. G. (2019). Applying the long-term memory algorithm to forecast thermoregulation capacity loss in honeybee colonies. In CSBC 2019 - 10º Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais (WCAMA), pages 1–14.

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. and Atanasova, T. (2018). Osemn process for working over data acquired by iot devices mounted in beehives. Current Trends in Natural Sciences, 7:47–53.

Friedman, J., Hastie, T., and Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, page 1–22.

Gil-Lebrero, S., Quiles-Latorre, F. J., Ortiz-López, M., Sánchez-Ruiz, V., Gámiz-López, V., and Luna- Rodríguez, J. J. (2017). Honey bee colonies remotemonitoring system. Sensors, 17(1).

Hoerl, A. E. and Kennard, R.W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12:55–67.

Huang, Z. (1998). Extensions to the k-means algorithm for clustering large data sets with categorical values. DataMin. Knowl. Discov., 2(3):283–304.

Jacobs, M., Cazier, J. A., Wilkes, J. T., Rogers, R., and Hassler, E. E. (2017). Building a business analytics platform for enhancing commercial beekeepers’ performance: Descriptive validation of a data framework forwidespread adoption by citizen scientists. In 23rd Americas Conference on Information Systems, AMCIS 2017, Boston, MA, USA, August 10-12, 2017, pages 611 – 620.

Kridi, D. S., de Carvalho, C. G. N., and Gomes, D. G. (2016). Application of wireless sensor networks for beehive monitoring and in-hive thermal patterns detection. Computers and Electronics in Agriculture, 127:221 – 235.

Meikle, W. G. and Holst, N. (2015). Application of continuous monitoring of honeybee colonies. Apidologie, 46(1):10–22.

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.

Sánchez-Bayo, F. and Wyckhuys, K. (2019). Worldwide decline of the entomofauna: A review of its drivers. Biological Conservation, 232.

Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society (Series B), 58:267–288.

Zacepins, A., Brusbardis, V., Meitalovs, J., and Stalidzans, E. (2015). Challenges in the development of precision beekeeping. Biosystems Engineering, 130:60 – 71.

Zogovic, N., Mladenovic, M., and Rasic, S. (2017). From primitive to cyber-physical beekeeping. In Zdravkovic,M., Konjovic, Z., Trajanovic,M. (Eds.) 7th International Conference on Information Society and Technology ICIST 2017 Proceedings Vol.1, pages 38–43.

Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B, 67:301–320.
Publicado
30/06/2020
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DA SILVA, Daniel; RODRIGUES, Ícaro; BRAGA, Antonio; NOBRE, Juvêncio; FREITAS, Breno; GOMES, Danielo. An Autonomic, Adaptive and High-Precision Statistical Model to Determine Bee Colonies Well-Being Scenarios. In: WORKSHOP DE COMPUTAÇÃO APLICADA À GESTÃO DO MEIO AMBIENTE E RECURSOS NATURAIS (WCAMA), 11. , 2020, Evento Online. Anais do XI Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais. Porto Alegre: Sociedade Brasileira de Computação, june 2020 . p. 31-40. ISSN 2595-6124. DOI: https://doi.org/10.5753/wcama.2020.11017.