Automatic recognition and counting of raw water cyanobacteria from reservoirs in the Curitiba region
Abstract
Cyanobacteria are organisms that can occur in reservoirs and springs. Some species can produce harmful toxins by contact or ingestion and may even cause death. The law requires periodic analyzes of water intended for the use of the population to monitor and control their quality. The process of identification and counting of cyanobacteria cells is costly and manual. Artificial intelligence is active in problem solving, and convolutional neural networks are the state of the art in recognition of images and objects. It is proposed to develop an automatic method for identification and counting cyanobacteria cells. Tests have demonstrated the feasibility of the proposal as well as pointed improvements to be made.
References
Cetesb. (2019) “Manual de cianobactérias planctônicas:Legistação, orientações para o monitoramento e aspectos ambientais”, São Paulo, http://cetesb.sp.gov.br/laboratorios/wp-content/uploads/sites/24/2015/01/manual-cianobacterias-2013.pdf, Janeiro.
Copasa. (2019) Copasa Orienta – Cianobactérias: o perigo das algas azuis. Saneamento, tratamento e abastecimento de água. Programa Chuá: Educação Sanitária e Ambiental da Copasa, http://www.copasa.com.br/wps/wcm/connect/771d71f8-d24f-4cdb-abf8-e3461660d5f6/COPASA_Agua.pdf?MOD=AJPERES, Janeiro.
Gandola E., Antonioli M., Traficante A., Franceschini S., Scardi M., Congestri R. (2016) “ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning.” J. Microbiol. Methods. 124:48–56. http://dx.doi.org/10.1016/j.mimet.2016.03.007
Duy, T. N., et al. (2000) “Toxicology and risk assessment of freshwater cyanobacterial (blue-green algal) toxins in water.” In: Review of Environmental and Contamination and Toxicology, 163: 113-186. http://dx.doi.org/10.1007/978-1-4757-6429-1_3
Esteves, F. A. (1998) “Fundamentos de Limnologia”. Ed. Interciência, 2a ed., RJ, 602 p.
G1. (2016) “Tragédia da Hemodiálise que deixou quase 60 mortos completa 20 anos”, Recife, http://g1.globo.com/pe/caruaru-regiao/noticia/2016/02/tragedia-da-hemodialise-que-deixou-quase-60-mortos-completa-20-anos.html.
Lecun, Y., Bengio Y., Hinton. G. (2015) “Deep Learning”. Nature, v521, p436-444. http://dx.doi.org/10.1038/nature14539
Mosleh, M. A. A., Manssor, H., Malek, S., Milow, P., Salleh, A. (2012) “A preliminary study on automated freshwater algae recognition and classification system.” BMC Informatics 13 (Suppl 17), S25. http://dx.doi.org/10.1186/1471-2105-13-S17-S25
Zeder, M., S. Peter, T. Shabarova, J. Pernthaler. (2009) “A small population of planktonic Flavobacteria with disproportionally high growth during the spring phytoplankton bloom in a prealpine lake.” Environ. Microbiol. http://dx.doi.org/10.1111/j.1462-2920.2009.01994.x
Copasa. (2019) Copasa Orienta – Cianobactérias: o perigo das algas azuis. Saneamento, tratamento e abastecimento de água. Programa Chuá: Educação Sanitária e Ambiental da Copasa, http://www.copasa.com.br/wps/wcm/connect/771d71f8-d24f-4cdb-abf8-e3461660d5f6/COPASA_Agua.pdf?MOD=AJPERES, Janeiro.
Gandola E., Antonioli M., Traficante A., Franceschini S., Scardi M., Congestri R. (2016) “ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning.” J. Microbiol. Methods. 124:48–56. http://dx.doi.org/10.1016/j.mimet.2016.03.007
Duy, T. N., et al. (2000) “Toxicology and risk assessment of freshwater cyanobacterial (blue-green algal) toxins in water.” In: Review of Environmental and Contamination and Toxicology, 163: 113-186. http://dx.doi.org/10.1007/978-1-4757-6429-1_3
Esteves, F. A. (1998) “Fundamentos de Limnologia”. Ed. Interciência, 2a ed., RJ, 602 p.
G1. (2016) “Tragédia da Hemodiálise que deixou quase 60 mortos completa 20 anos”, Recife, http://g1.globo.com/pe/caruaru-regiao/noticia/2016/02/tragedia-da-hemodialise-que-deixou-quase-60-mortos-completa-20-anos.html.
Lecun, Y., Bengio Y., Hinton. G. (2015) “Deep Learning”. Nature, v521, p436-444. http://dx.doi.org/10.1038/nature14539
Mosleh, M. A. A., Manssor, H., Malek, S., Milow, P., Salleh, A. (2012) “A preliminary study on automated freshwater algae recognition and classification system.” BMC Informatics 13 (Suppl 17), S25. http://dx.doi.org/10.1186/1471-2105-13-S17-S25
Zeder, M., S. Peter, T. Shabarova, J. Pernthaler. (2009) “A small population of planktonic Flavobacteria with disproportionally high growth during the spring phytoplankton bloom in a prealpine lake.” Environ. Microbiol. http://dx.doi.org/10.1111/j.1462-2920.2009.01994.x
Published
2019-06-11
How to Cite
LASKOSKI, Geisla de Albuquerque Melo; SOBIERANSKI, Antonio Carlos; LUDWIG, Thelma Alvim Veiga; DE OLIVEIRA , Lucas Ferrari.
Automatic recognition and counting of raw water cyanobacteria from reservoirs in the Curitiba region. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 19. , 2019, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 258-263.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2019.6259.
