**1. Introduction**

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comparison Pyonometer. Trans ASAE 10: 693-696 Uzuner, B A (1996). Soil mechanics. Technique Press, Ankara, Turkey The soybean rust (*Phakopsora pachyrhizi* H. Sydow & P. Sydow) was reported in soybean (*Glycine max* L. Merrill) in many tropical and subtropical regions, causing significant reductions in productivity and quality of seeds (Bromfield, 1984, Hartman et al., 2005; Kawuki et al., 2004; McGee, 1992, Medina et al., 2006, Sinclair & Backman, 1989; Vale, 1985, Yang et al., 1990, Yang et al., 1991; Yorinori & Lazzarotto, 2004), with losses of up to 70% in production (Bromfield, 1976). The rust occurs in almost all soybean fields in Brazil. The states with high occurrence of the disease in 2003/04 were Mato Grosso, Goias, Minas Gerais and São Paulo. Considering Brazilian states in 2002/03, soybean rust caused losses of 4.011 million of megagrams or the equivalent of US\$ 884.25 million, while in 2004, the losses were approximately US\$ 2.28 billion (Yorinori & Lazzarotto, 2004).

The success of pathogen infection depends on the sequence of events determined by spore germination, appressoria formation and penetration. Each of these events, the subsequent colonization and sporulation, are influenced by biotic factors such as pathogen-host and abiotic environment. Among abiotic factors, temperature and leaf wetness play a crucial role, especially in the monocyclic germination, infection and colonization of *P. pachyrhizi* in soybeans. Thus, several studies were conducted to model the effects of temperature and humidity on the disease progress for Brazilian cultivars (Vale, 1984, Vale et al., 1990) and for different cultivars adapted to other countries (Batchelor et al., 1997, Kim et al., 2005, Marchetti et al. 1975; Melching et al. 1989; Pivonia & Yang, 2004, Reis et al., 2004). According to Sinclair & Backman (1989), the range of optimum temperature for infection is 20 °C to 25 °C. Under these conditions, with the availability of free water on the leaf surface, the infection starts after 6 hours of the deposition of the spore (Marchetti et al., 1975; Melching et al. 1989; Vale et al., 1990). However, after 12 hours (Marchetti et al. 1975; Melching et al., 1989) up to 24 hours of leaf wetness (Vale et al., 1990) was more successful in establishing infection (Sinclair & Backman, 1989). Therefore, such studies are important for estimating the potential occurrence and formulate strategies to control disease in geographic regions not yet reported (Pivonia & Yang, 2005) and to investigate the potential of spreading in major producing regions throughout the months of the year (Alves et al., 2006; Pivonia & Yang, 2004).

Linear regression approaches (Vale et al., 1990), nonlinear regression (Reis et al., 2004), artificial intelligence techniques, such as neural networks (Batchelor et al., 1997, Pinto et al., 2002) and fuzzy logic (Kim et al., 2005), were used to model the influence of abiotic variables on the disease progress. However, in the case of using regression and neural networks, there is a need to perform data collection for the best fitting models (Reis et al., 2004) and network training (Batchelor et al., 1997). On the other hand, considering fuzzy logic technique, quantitative measures are no longer urgently needed to develop a model (Kim et al., 2005), notwithstanding the choice of these observations are used in the modeling process (Mouzouris & Mendel, 1997). In this context, fuzzy logic was applied to model physical, chemical and biological process, with uncertainty and ambiguous nature (Kim et al., 2005, Massad et al. 2003; Schermer, 2000; Uren et al., 2001).

Other features that justify the application of fuzzy logic systems (FLS) are related to the flexibility of the technique, ease of understanding the concepts, ability to model complex nonlinear functions, development based on the expertise of specialists, integration with other automation techniques and finding support in the natural language used by humans (Cox, 1994; Tanaka, 1997).

Likewise, there is no precise measurements of the influence of other variables such as soil fertility, resistant cultivars, climatic variables, management practices in the progress of the disease, being necessary to create a subjective measure to assess the potential progress of the disease.

Considering the importance of the soybean crop in Brazil, as well as the risk caused by the rust and the losses due to its occurrence, it is necessary to know epidemiological aspects of the disease in Brazilian cultivars in order to enable disease intensity prediction. Therefore, the objective of this work was to study the effects of temperature and leaf wetness on the monocyclic process of soybean rust in cultivars Conquista, Savana and Suprema, based on a fuzzy logic system and nonlinear regression models.
