Adaptive neuro-fuzzy inference systems for epidemiological analysis of soybean rust

  • Authors:

  • Marcelo De Carvalho Alves;Edson AmpéLio Pozza;JoãO De CáSsia Do Bonfim Costa;Luiz Gonsaga De Carvalho;Luciana Sanches Alves

  • Affiliations:

  • Federal University of Mato Grosso, Faculty of Agronomy and Veterinary Medicine, Department of Soil and Rural Engineering, Av. Fernando Correa da Costa 2367, Boa Esperança, 78060-900 Cuiab ...;Federal University of Lavras, Department of Phytopathology, Campus of Federal University of Lavras, Cx 3037, CEP 37200-000 Lavras MG, Brazil;CEPEC/CEPLAC/MAPA, Phytopathology Section, P.O. Box 07, 45650-000 Itabuna, BA, Brazil;Federal University of Lavras, Minas Gerais, Brazil, Campus, Cx 3037, CEP 37200-000 (UFLA), Engineering Department (DEG), Brazil;Federal University of Mato Grosso, Faculty of Architecture, Engineering and Technology, Department of Sanitary and Environmental Engineering, Av. Fernando Correa da Costa 2367, Boa Esperança, ...

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2011

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Abstract

The objective of this work was to develop and to evaluate adaptive neuro-fuzzy inference systems as methodology to describe the severity of soybean rust (Phakopsora pachyrhizi) monocyclic process in soybean [Glycine max (L.) Merr.], under effects of leaf wetness, temperature, and days after fungi inoculation. The experiment was conducted in growth chambers with mean air temperatures of 15, 20, 25 and 30^oC and leaf wetness periods of 6, 12, 18 and 24h. The plants were inoculated by spraying a suspension of P. pachyrhizi inoculum at concentration of 10^4 uredinosporemL^-^1. A disease assessment key was adopted for estimate amounts of soybean rust at 0, 6, 9, 12 and 15 days after fungi inoculation. A hybrid neural network training with 3 and 3000 epochs was applied to disease severity data for optimization of fuzzy system parameters used to describe the severity of soybean rust based on leaf wetness, temperature and days after fungi inoculation. Higher accuracy and precision of the neuro-fuzzy systems estimates were obtained after training with 3000 epochs. Nevertheless, training with 3 epochs produced smoother estimates. The neuro-fuzzy systems enabled to describe the severity of soybean rust monocyclic process under effects of leaf wetness, mean air temperature and days after fungi inoculation and was better applied for Conquista cultivar, followed by Savana and Suprema cultivars. Higher soybean rust severity was verified under temperatures among 20^oC and 25^oC, leaf wetness above 6h, with higher values above 10h, and 15 days after fungi inoculation. Temperatures near 15^oC increased the latent period of the disease but not inhibited its development after 10 days of fungi inoculation.