An Interval Fuzzy Logic-Based System to Predict Pests in Agriculture

  • L. M. Rodrigues FURG
  • G. P. Dimuro FURG
  • D. T. Franco FURG
  • J. C. Fachinello UFPEL

Resumo


Precision Agriculture is a relatively new area of study in Brazil, with its expansion in the mid 90s. In this period, there were government incentives in credit lines for rural producers, making possible the use of new technologies in rural areas for maximizing production and reducing costs. In this sense, this work proposes to apply environmental sensing technologies to assist farmers to detect the possibility of pests occurrence (or proliferation) in their culture. For that, the Arduino platform is being used in combination with adequate sensors to capture meteorological conditions in a given region. We apply an approach based on Interval Fuzzy Logic for the assessment of the sensing data to report if the weather conditions are favorable for the emergence of pests, especially fungi, which depend on factors such as temperature, humidity and leaf wetness. Besides, a discussion about an experiment performed to test the developed system is presented. The experiment is based on a common disease encountered, mainly, in the southern region of the state of Rio Grande do Sul, in Brazil. The disease, called Brown Rot, is caused by a fungus known as Monilinia fructicola.

Referências

Agrios, G. (1996). Fitopatología. Limusa, Mexico, 2 edition. In Spanish.

Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., and Cayirci, E. (2002). A survey on sensors networks. IEEE Communications Magazine, 40(8):102–114.

Arduino Team (2012a). Arduino. URL [link]. (Jan, 2012).

Arduino Team (2012b). Softwareserial library. URL [link]. (Oct, 2012).

Arduino Team (2013). SD Library. URL [link]. (Nov, 2012).

Baggio, A. (2005). Wireless sensor networks in precision agriculture. Technical report, Delft University of Technology.

Bedregal, B. C., Dimuro, G. P., Santiago, R. H. N., and Reiser, R. H. S. (2010). On interval fuzzy S-implications. Information Sciences, 180(8):1373 – 1389.

Bedregal, B. C. and Takahashi, A. (2006). The best interval representation or t-norms and automorphisms. Fuzzy Sets and Systems, 157(24):3220–3230.

Cavalheiro, S. A. C., Foss, L., Aguiar, M. S., Dimuro, G. P., and Costa, A. C. R. (2011). On interval fuzzy numbers. In Post-Proceedings of the Workshop-SCHOOL on Theoretical Computer Science, pages 1–6. IEEE, Los Alamitos.

Ceken, C. (2008). An energy efficient and delay sensitive centralized mac protocol for wireless sensor networks. Computer Standards & Interfaces, 30(1–2):20–31.

Dimuro, G. P., Bedregal, B. C., Santiago, R. H. N., and Reiser, R. H. S. (2011). Interval additive generators of interval t-norms and interval t-conorms. Information Sciences, 181(18):3898–3916.

Fachinello, J. C., Tibola, C. S., Vicenzi, M., Parisotto, E., Picolotto, L., and Mattos, M. L. T. (2003). Produção integrada de pêssegos: Três anos de experiência na região de Pelotas – RS. Revista Brasileira de Fruticultura, 25(2):256–258. In Portuguese.

Fernandes, C. F. (2005). A importância da fitopatologia. URL [link]. In Portuguese. (Nov, 2012).

Grattan-Guiness, I. (1976). Fuzzy membership mapped onto interval and many-valued quantities. Zeitschrift für mathematische Logik und Grundlagen der Mathematik, 22(1):149–160.

Hart, M. (2012). TinyGPS: A compact Arduino GPS/NMEA parser. URL [link]. (Oct, 2012).

Huang, H. F. (2009). A novel access control protocol for secure sensor networks. Computer Standards & Interfaces, 31(2):272–276.

Lodwick, W. A. (2004). Preface. Reliable Computing, 10(4):247–248.

Mendel, J. M. (2007). Advances in type-2 fuzzy sets and systems. Information Sciences, 177(1):84–110.

Monk, S. (2012). Timer library for arduino. URL [link]. (Set, 2012).

Monteiro, L. B., Mio, L. L. M., Serrat, B. M., Motta, A. C., and Cuquel, F. L. (2004). Fruteiras de Caroço: Uma Visão Ecológica. UFPR – Departamento de Fitotecnia e Fitossanitarismo, Departamento de Solos e Engenharia Agrícola. In Portuguese.

Moore, R. E. (1979). Methods and Applications of Interval Analysis. SIAM, Philadelphia.

Moore, R. E., Kearfott, R. B., and Cloud, M. J. (2009). Introduction to Interval Analysis. SIAM, Philadelphia.

Nguyen, H. T. and Walker, E. A. (2006). A First Course in Fuzzy Logic. Chapman & Hall/CRC, Boca Raton.

Octave Forge (2013). Extra packages for GNU Octave. URL [link]. (Jan, 2013).

Rural BR Agricultura (2009). Agricultura de precisão é um investimento com retorno certo. URL [link]. In Portuguese. (Feb, 2013).

Santos, J., Raseira, M. C. B., and Zanandrea, I. (2012). Resistência à podridão parda em pessegueiro. Bragantia, 71(2):219–225. In Portuguese.

Tamayo, R. A. C., Ibarra, M. G. L., and Macías, J. A. G. (2010). Better crop management with decision support systems based on wireless sensor networks. Electrical Engineering Computing Science and Automatic Control (CCE), pages 412–417.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3):338–353.

Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning I. Information Sciences, 8(3):199–249.

Zadeh, L. A. (2008). Is there a need for fuzzy logic? Information Sciences, 178(13):2751–2779.
Publicado
23/07/2013
RODRIGUES, L. M.; DIMURO, G. P.; FRANCO, D. T.; FACHINELLO, J. C.. An Interval Fuzzy Logic-Based System to Predict Pests in Agriculture. In: WORKSHOP DE COMPUTAÇÃO APLICADA À GESTÃO DO MEIO AMBIENTE E RECURSOS NATURAIS (WCAMA), 4. , 2013, Maceió/AL. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2013 . p. 1025-1035. ISSN 2595-6124.