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

Environmental Modelling and Software

Volumn 26, Issue 9, 2011, Pages 1089-1096

  Alves, Marcelo de Carvalho ^a   Pozza, Edson Ampélio ^b   Costa, João de Cássia do Bonfim ^c   Gonsaga de Carvalho, Luiz ^b   Alves, Luciana Sanches ^a  

^a FEDERAL UNIVERSITY OF MATO GROSSO   (Brazil)

^b FEDERAL UNIVERSITY OF LAVRAS   (Brazil)

^c CEPEC/CEPLAC/MAPA   (Brazil)

Author keywords

Anfis; Artificial intelligence; Disease; Epidemiology; Fuzzy logic; Modeling; Neural networks; Neuro fuzzy; Rust; Soybean

Indexed keywords

ANFIS; MODELING; NEURO-FUZZY; RUST; SOYBEAN;

AMINO ACIDS; ARTIFICIAL INTELLIGENCE; ATMOSPHERIC TEMPERATURE; ESTIMATION; FUNGI; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; FUZZY SETS; FUZZY SYSTEMS; NITROGEN FIXATION; SUSPENSIONS (FLUIDS);

FUZZY LOGIC;

ACCURACY ASSESSMENT; AIR TEMPERATURE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CULTIVAR; DISEASE SEVERITY; EPIDEMIOLOGY; EXPERIMENTAL STUDY; FUZZY MATHEMATICS; OPTIMIZATION; RUST DISEASE; SAVANNA; SOYBEAN;

FUNGI; GLYCINE MAX; PHAKOPSORA PACHYRHIZI;

EID: 79955896932     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2011.03.008     Document Type: Article

References (32)

1. Alves M.C., Pozza E.A., Ferreira J.B., Araújo D.V., Costa J.C.B., Deuner C.C., Muniz M.F.S., Zambenedetti E.B., Machado J.C. The effects of temperature and leaf wetness periods on the development of soybean rust in the cultivars consquista, Savana and Suprema. Summa Phytopathologica 2007, 33:239-244.
2. Amini M., Abbaspour K.C., Johnson C.A. A comparison of different rule-based statistical models for modeling geogenic groundwater contamination. Environmental Modelling & Software 2010, 25:1650-1657.
3. Batchelor W.D., Yang X.B., Tschanz A.T. Development of a neural network for soybean rust epidemics. Transactions of the ASAE, St. Joseph 1997, 247-252.
4. Bromfield K.R. Soybean Rust 1984, American Phytopathological Society, St. Paul, Monograph.
5. Casey P.S. The epidemiology of soybean rust - Phakopsora pachyrhizi Syd. Soybean Rust Newsletter 1980, 4:3-5.
6. Castañeda-Miranda R., Ventura-Ramos E., Peniche-Vera R.R., Herrera-Ruiz G. Fuzzy greenhouse climate control system based on a field programmable gate array. Biosystem Engineering 2006, 94:165-177.
7. Draper N.R., Smith H. Applied Regression Analysis 1998, John Wiley & Sons, New York.
8. Jang J.S.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems Man and Cybernetics 1993, 23:665-685.
9. Jang J.S.R., Sun C.T. Neuro-fuzzy modeling and control. Proceedings of the IEEE 1995, 83:378-406.
10. Jang J.S.R., Sun C.T., Mizutani E. Neuro-fuzzy and Soft Computing 1997, Prentice Hall, Upper Saddle River.
11. Keogh, R.C., 1974. Studies on Phakopsora pachyrhizi Syd.: The causal agent of soybean rust. M.S. thesis, University of Sydney.
12. Kim K.S., Wang T.C., Yang X.B. Simulation of apparent infection rate to predict severity of soybean rust using a fuzzy logic system. Phytopathology 2005, 95:1122-1131.
13. Klir G.J., Yuan B. Fuzzy Sets and Fuzzy Logic: Theory and Applications 1995, Prentice Hall, New Jersey.
14. Liu D.H.F., Abonyi J. Software for fuzzy logic control. Instrument Engineers' Handbook, Process Control and Optimization 2006, 360-374. CRC Press, Boca Raton, FL. B.G. Lipták (Ed.).
15. Massad E., Ortega N.R.S., Struchiner C.J., Burattini M.N. Fuzzy epidemics. Artificial Intelligence in Medicine 2003, 29:241-259.
16. Melching J.S., Dowler W.M., Koogle D.L., Royer M.H. Effects of duration, frequency, and temperature of leaf wetness periods on soybean rust. Plant Disease 1989, 73:117-122.
17. Mellit A., Kalogirou S.A. Artificial intelligence techniques for photovoltaic applications: a review. Progress in Energy and Combustion Science 2008, 34:574-632.
18. Pan L., Yang S.X. Analysing livestock farm odour using an adaptive neuro-fuzzy approach. Biosystems Engineering 2007, 97:387-393.
19. Pivonia S., Yang X.B. Assessment of the potential year-round establishment of soybean rust throughout the world. Plant Disease 2004, 88:523-529.
20. Ritchie, S., Hanway, J.J., Thompson, H.E., 1982. How a soybean plant develops, Ames: Iowa State University of Science and Technology, Cooperative Extension Service, Special Report, 53.
21. Scherm H., Yang X.B. Interannual variations in wheat rust development in China and the United States in relation to the El Niño/southern oscillation. Phytopathology 1995, 85:970-976.
22. Sugeno M. Industrial Applications of Fuzzy Control 1985, Elsevier Science, New York.
23. Takagi T., Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Transaction on Systems, Man and Cybernetics 1985, 15:116-132.
24. Tsoukalas L.H., Uhrig R.E. Fuzzy and Neural Approaches in Engineering 1997, John Wiley & Sons, New York.
25. Ureña R., Rodríguez F., Berenguel M. A machine vision system for seeds germination quality evaluation using fuzzy logic. Computers and Electronics in Agriculture 2001, 32:1-20.
26. Von Altrock C. Fuzzy Logic and Neuro Fuzzy Applications Explained 1995, Prentice Hall.
27. Wieland R., Mirschel W. Adaptive fuzzy modeling versus artificial neural networks. Environmental Modelling & Software 2008, 23:215-224.
28. Yang C.C., Prasher S.O., Landrya J.A., Ramaswamy H.S. Development of a herbicide application map using artificial neural networks and fuzzy logic. Agricultural Systems 2003, 76:561-574.
29. Yang X.B., Tschanz A.T., Dowler W.M., Wang T.C. Development of yield loss models in relation to reductions of components of soybean infected with Phakopsora pachyrhizi. Phytopathology 1991, 81:1420-1426.
30. Zadeh L.A. Fuzzy logic, neural networks, and soft computing. Communications of the ACM 1994, 37:77-84.
31. Zambenedetti E.B., Alves E., Pozza E.A., Araújo D.V., Godoy C.V. Evaluation of monocyclic parameters and intensity of the Asian soybean rust (Phakopsora pachyrhizi) in both several soybean genotypes and canopy position. Summa Phytopathologica 2007, 33:75-78.
32. Zambenedetti E.B., Alves E., Pozza E.A., Araújo D.V. Germination of urediniospores from Phakopsora pachyrhizi on different techniques of preservation. Summa Phytopathologica 2007, 33:83-85.