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INTRODUCTION: This study sought to analyze the relationships between cutaneous leishmaniasis and its epidemiological, environmental and socioeconomic conditions, in the 22 microregions of Pará state, Brazil, for the period from 2017 to 2022. METHODOLOGY: In this ecological and exploratory study, the microregions were used as spatial units because they are formed by contiguous municipalities with similar characteristics. The epidemiological, environmental, socioeconomic, and public health policy data employed were obtained from the official information systems at the Ministry of Health, National Institute for Space Research, and Brazilian Institute of Geography and Statistics. A fuzzy system was developed to identify risk factors for the disease, using Python programming language. The results were analyzed with the bivariate Global Moran spatial analysis technique. RESULTS: It was observed that the Altamira microregion had the highest risk percentage for the disease, while Breves had the lowest, with significant differences in the relevance of its conditioning factors, mainly related to land use and cover patterns, in addition to demography and living conditions index, education and public health policies. CONCLUSIONS: The fuzzy system associated with the geostatistical technique was satisfactory for identifying areas with health vulnerability gradients related to deforestation, pasture, poverty, illiteracy, and health services coverage, as its conditioning variables. Thus, it was demonstrated that deforestation was the main risk factor for the disease. The system can also be used in environmental and epidemiological surveillance.

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Resumen: México ocupa el primer lugar en obesidad infantil en el mundo, por lo que resulta importante identificar variables asociadas al consumo alimentario. El objetivo del presente trabajo fue establecer si la forma en que el consumo de alimentos se modifica en función de las normas sociales alimentarias y la publicidad alimentaria que recibe la población infantil escolar. Se diseñó un estudio multivariado predictivo utilizando sistemas de lógica difusa tipo dos de intervalo (IT2 FLS), y comparando su ajuste con modelos convencionales, como la regresión lineal múltiple (RLM). Se trabajó con las respuestas emitidas por 196 niños en un estudio previo y almacenadas en una base de datos, seleccionando solo las que correspondieron a las variables de interés para el estudio. Las normas sociales a evitar, el número de comidas y la compra de alimentos por la publicidad alimentaria permitieron predecir el consumo alimentario de los niños mediante IT2 FLS. En RLM las horas de comidas tuvo mayor capacidad predictiva que el número de comidas. El IT2 FLS proporcionó un mayor coeficiente de determinación (R2 = 0.649), que el de la RLM (R2 = 0.370). El consumo alimentario, al ser un fenómeno multicausal y complejo, puede ser mejor predicho al utilizar métodos de análisis que manejen de forma más flexible la incertidumbre, como lo hace la IT2 FLS.

Abstract: Mexico ranks first in childhood obesity in the world, so it is important to identify variables associated with food consumption. The objective of this work was to establish whether the way in which food consumption is modified depending on social food norms and food advertising received by school children. A predictive multivariate study was designed using interval type two fuzzy logic systems (IT2 FLS), and comparing its fit with conventional models, such as multiple linear regression (RLM). We worked with the responses issued by 196 children in a previous study and stored in a database, selecting only those that corresponded to the variables of interest for the study. The social norms to avoid, the number of meals and the purchase of food through food advertising made it possible to predict children's food consumption through IT2 FLS. In RLM, mealtimes had a greater predictive capacity than the number of meals. The IT2 FLS provided a higher coefficient of determination (R2 = 0.649) than that of the RLM (R2 = 0.370). Food consumption, being a multicausal and complex phenomenon, can be better predicted by using analysis methods that manage uncertainty more flexibly, as the IT2 FLS does.

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ABSTRACT Objective: To describe the possibility of applying Fuzzy Logic in analyzing the vulnerability of Women Who Have Sex with Women to Sexually Transmitted Infections/HIV/AIDS. Methods: We developed a Fuzzy Logic system with 17 input variables and one output variable, using data related to vulnerability in a municipality located in the Midwest region of the State of São Paulo, Brazil. Results: The factor with the greatest positive impact was the confirmation that a low understanding of Sexually Transmitted Infections/HIV/AIDS is associated with higher vulnerability. Conversely, the statement "Not disclosing sexual activity to healthcare professionals," where individuals do not admit to having sex with women, had the least impact. Conclusions: Fuzzy Logic facilitates the identification of vulnerability, expressed through the analysis of interaction between variables in each dimension. This makes it a promising method to assist in analyzing the vulnerability of specific populations.

RESUMEN Objetivo: Describir la posibilidad de aplicación de la Lógica Difusa en el análisis de la vulnerabilidad de las Mujeres que tienen Relaciones Sexuales con Mujeres a las Infecciones de Transmisión Sexual/VIH/sida. Métodos: Se desarrolló un sistema de Lógica Difusa con 17 variables de entrada y una de salida, utilizando datos relacionados con la vulnerabilidad en un municipio ubicado en el Centro-Oeste del Estado de São Paulo, Brasil. Resultados: El factor de mayor impacto positivo fue la confirmación de que la baja comprensión sobre las Infecciones de Transmisión Sexual/VIH/sida está asociada con una mayor vulnerabilidad. Por otro lado, la declaración "No exponerse ante el profesional de la salud", donde la persona no admite tener relaciones sexuales con mujeres, tuvo el menor impacto. Conclusiones: La Lógica Difusa permite la identificación de la vulnerabilidad, expresada por el análisis de interacción entre las variables de cada dimensión. Esto la convierte en un método prometedor para ayudar en el análisis de la vulnerabilidad de poblaciones específicas.

RESUMO Objetivo: descrever a possibilidade de aplicação da Lógica Fuzzy na análise da vulnerabilidade de Mulheres que fazem Sexo com Mulheres às Infecções Sexualmente Transmissíveis/HIV/aids. Métodos: Desenvolveu-se um sistema de Lógica Fuzzy com 17 variáveis de entrada e uma de saída, utilizando dados relacionados à vulnerabilidade em um município localizado no Centro-Oeste do Estado de São Paulo, Brasil. Resultados: O fator de maior impacto positivo foi a confirmação de que a baixa compreensão sobre Infecções Sexualmente Transmissíveis/HIV/aids está associada a uma maior vulnerabilidade. Por outro lado, a declaração "Não se expor para a profissional de saúde", onde a pessoa não admite ter relações sexuais com mulheres, teve o menor impacto. Conclusões: A Lógica Fuzzy oportuniza a identificação da vulnerabilidade, expressa pela análise de interação entre as variáveis de cada dimensão. Isso a torna um método promissor para auxiliar na análise da vulnerabilidade de populações específicas.

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Notable efforts have been devoted to the development of biomechanical models of the spine, so the development of a motion system to control the spine becomes expressively relevant. This paper presents a fuzzy controller to manipulate the movement of a 3D robotic mechanism of the lumbar spine, which is driven by tendons. The controller was implemented in Matlab/Simulink R2023a software, MathWorks (Brazil), considering mathematical modeling based on the Lagrangian methodology for simulating the behavior of the lumbar spine dynamic movement. The fuzzy controller was implemented to perform movements of two joints of the 3D robotic mechanism, which consists of five vertebrae grouped into two sets, G1 and G2. The mechanism's movements are carried out by four servomotors which are driven by readings from two sensors. For control, the linguistic variables of position, velocity and acceleration were used as controller inputs and the torque variables were used for the controller output. The experimental tests were carried out by running the fuzzy controller directly on the 3D physical model (external to the simulation environment) to represent flexion and extension movements analogous to human movements.

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Measuring lycopene in tomatoes is fundamental to the agrifood industry because of its health benefits. It is one of the leading quality criteria for consuming this fruit. Traditionally, the amount determination of this carotenoid is performed using the high-performance liquid chromatography (HPLC) technique. This is a very reliable and accurate method, but it has several disadvantages, such as long analysis time, high cost, and destruction of the sample. In this sense, this work proposes a low-cost sensor that correlates the lycopene content in tomato with the color present in its epicarp. A Raspberry Pi 4 programmed with Python language was used to develop the lycopene prediction model. Various regression models were evaluated using neural networks, fuzzy logic, and linear regression. The best model was the fuzzy nonlinear regression as the RGB input, with a correlation of R2 = 0.99 and a mean error of 1.9 × 10-5. This work was able to demonstrate that it is possible to determine the lycopene content using a digital camera and a low-cost integrated system in a non-invasive way.

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Germplasm banks are the most significant repository for plant genetic resources for food and agriculture (PGRFA) worldwide. Despite their strategic importance, national germplasm banks of tropical megadiverse developing countries such as Colombia have extremely limited funds. Therefore, making strategic decisions about research investment is essential. Here, we designed a data-driven approach to build an index that sorts Colombian PGRFA into three groups: high, medium, or low priority, based on four pillars of information from open-access databases and aligned with the sustainable goals of no poverty and zero hunger: Geographic origin, vulnerability status, economic benefits, and food security importance. We analyzed 345 PGRFA using the index, separating them into two groups, 275 already conserved in the Colombian germplasm bank (BGVCOL group) and 70 not currently conserved in the BGVCOL (NCB group). We used fuzzy logic to classify each PGRFA by each pillar and integrate it to obtain a priority index. Missing data for native crops were frequent in the BGVCOL group. Therefore we adopted an imputation strategy to fill the gaps and calculated the uncertainty. After applying the index, PGRFA with higher priority were 24 (8.72%) from the BGVCOL (i.e., 15 potatoes, three tomatoes, two tree tomatoes, pineapple, cocoa, papaya, and yacon) and one from NCB (i.e., coffee). We concluded that this methodology successfully prioritized PGRFA in Colombia and shows the big holes of knowledge for future research and alternatives to improve this index. The versatility of this methodology could be helpful in other genebanks with budget limitations for research investment. Supplementary Information: The online version contains supplementary material available at 10.1007/s10531-023-02599-7.

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BACKGROUND: Most organizations perceive the concept of ergonomic actions as a local tool used to improve workplace issues. Ergonomics however is not included in global management systems. The paradigm of ergonomics action in large organizations proposed by this study is that of management systems whose primary objective is the pressing need for continuous performance improvement, acquisition of excellence, and integration into all aspects of the business. OBJECTIVE: The general objective of this work is to draw a frame presenting a model of ergonomics management based on good practices in managing this discipline in large industrial companies. METHOD: In this study, we describe a strategy based on the fuzzy set theory to aggregate a group of good practices expressed by experienced ergonomists attached to large industrial companies for handling the lack of ergonomics in an organization. RESULTS: A set of good practices was compiled as a basis for application in ergonomics in large companies. CONCLUSION: This study shows that ergonomics should not be considered an isolated phenomenon but in a strategic, proactive, integrated, and perennial way as one of the management subjects.

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Scheduling residential loads for financial savings and user comfort may be performed by smart home controllers (SHCs). For this purpose, the electricity utility's tariff variation costs, the lowest tariff cost schedules, the user's preferences, and the level of comfort that each load may add to the household user are examined. However, the user's comfort modeling, found in the literature, does not take into account the user's comfort perceptions, and only uses the user-defined preferences for load on-time when it is registered in the SHC. The user's comfort perceptions are dynamic and fluctuating, while the comfort preferences are fixed. Therefore, this paper proposes the modeling of a comfort function that takes into account the user's perceptions using fuzzy logic. The proposed function is integrated into an SHC that uses PSO for scheduling residential loads, and aims at economy and user comfort as multiple objectives. The analysis and validation of the proposed function includes different scenarios related to economy-comfort, load shifting, consideration of energy tariffs, user preferences, and user perceptions. The results show that it is more beneficial to use the proposed comfort function method only when the user requires SHC to prioritize comfort at the expense of financial savings. Otherwise, it is more beneficial to use a comfort function that only considers the user's comfort preferences and not their perceptions.

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BACKGROUND: Different modalities of quarantines were one of the main measures implemented worldwide to avoid the spread of SARS-CoV2 virus. AIM: To analyze and compare retrospectively the implementation of the Step- to-Step plan devised by the Chilean Ministry of Health during the pandemic. To propose a decision-making path based on an artificial intelligence fuzzy system to determine confinements in specific territories. MATERIAL AND METHODS: The Step-to-Step Plan threshold values such hospital network capacity, epidemic spreading, testing and contact tracing capability were modeled using fuzzy numbers and fuzzy rule-based systems. RESULTS: Ministry of Health's decision-making opportuneness were unrelated with the Step-to-Step Plan indicators for deconfinement. Such disagreements undermined epidemiological indicators. CONCLUSIONS: Using an artificial intelligence system could improve decision-making transparency, emergency governance, and risk communication to the population.

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In this paper, the latest global COVID-19 pandemic prediction is addressed. Each country worldwide has faced this pandemic differently, reflected in its statistical number of confirmed and death cases. Predicting the number of confirmed and death cases could allow us to know the future number of cases and provide each country with the necessary information to make decisions based on the predictions. Recent works are focused only on confirmed COVID-19 cases or a specific country. In this work, the firefly algorithm designs an ensemble neural network architecture for each one of 26 countries. In this work, we propose the firefly algorithm for ensemble neural network optimization applied to COVID-19 time series prediction with type-2 fuzzy logic in a weighted average integration method. The proposed method finds the number of artificial neural networks needed to form an ensemble neural network and their architecture using a type-2 fuzzy inference system to combine the responses of individual artificial neural networks to perform a final prediction. The advantages of the type-2 fuzzy weighted average integration (FWA) method over the conventional average method and type-1 fuzzy weighted average integration are shown.

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Fault detection and classification are crucial procedures for electric power distribution systems because they can minimize the occurrence of faults. The methods for fault detection and classification have become more problematic because of the significant expansion of distributed energy resources in distribution systems and the change in their currents due to the action of short-circuiting. In this context, to fill this gap, this study presents a robust methodology for short-circuit fault detection and classification with the insertion of distributed generation units. The proposal methodology progresses in two stages: in the former stage, the detection is based on the continuous analysis of three-phase currents, whose characteristics are extracted through maximal overlap discrete wavelet transform. In the latter stage, the classification is based on three fuzzy inference systems to identify the phases with disturbance. The short-circuit type is identified by counting the shorted phases. The algorithm for short-circuit fault detection and classification is developed in MATLAB programming environment. The methodology is implemented in a modified IEEE 34-bus test system and modeled in ATPDraw with three scenarios with and without distributed generation units and considering the following parameters: fault type (single-phase, two-phase, and three-phase), angle of incidence, fault resistance (high impedance fault and low impedance fault), fault location bus, and distributed generation units (synchronous generators and photovoltaic panels). The accuracy is greater than 94.9% for the detection and classification of short-circuit faults for more than 20,000 simulated cases.

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ABSTRACT Objective: Based on the evaluation method with fuzzy integrals, this paper analyzes the results of physical health tests of college students. Methods: Principal component analysis was used to extract the psychological factors. The evaluation model was organized into three levels to evaluate the physical health of college students. Results: The physical health status of University Q students is below average. Most of the students' physical examination results are concentrated in the passing areas, and few students have an excellent to good ratio. Conclusion: The fitness assessment model of university students, based on an assessment with fuzzy integrals, has certain generalizability and applicability. The established index system and the comprehensive evaluation model are suitable for thoroughly evaluating each student and comprehensive fitness evaluation of all boys and girls in a class or college. Level of evidence II; Therapeutic studies - investigation of treatment results.

RESUMO Objetivo: Com base no método de avaliação com integrais fuzzy, este documento analisa os resultados dos testes de saúde física dos estudantes universitários. Métodos: Foi utilizada a análise de componentes principais para extrair os fatores psicológicos. O modelo de avaliação foi organizado em três níveis para avaliar a saúde física dos estudantes universitários. Resultados: O estado de saúde física dos estudantes da Universidade Q está abaixo da média. A maioria dos resultados dos exames físicos dos estudantes está concentrada nas áreas de aprovação, e poucos estudantes têm uma proporção excelente e boa. Conclusão: O modelo de avaliação da aptidão física dos estudantes universitários, baseado em uma avaliação com integrais fuzzy, tem certa generalização e aplicabilidade. O sistema de índice estabelecido e o modelo de avaliação abrangente não só são adequados para avaliação abrangente de cada estudante, mas também para avaliação abrangente da aptidão física de todos os meninos e meninas de uma classe ou faculdade. Nível de evidência II; Estudos terapêuticos - investigação de resultados de tratamento.

Resumen Objetivo: Basándose en el método de evaluación con integrales difusas, este artículo analiza los resultados de las pruebas de salud física de los estudiantes universitarios. Métodos: Se utilizó el análisis de componentes principales para extraer los factores psicológicos. El modelo de evaluación se organizó en tres niveles para evaluar la salud física de los estudiantes universitarios. Resultados: El estado de salud física de los estudiantes de la Universidad Q está por debajo de la media. La mayoría de los resultados de los exámenes físicos de los estudiantes se concentran en las zonas de aprobado, y son pocos los que tienen una proporción excelente y buena. Conclusión: El modelo de evaluación de la aptitud física de los estudiantes universitarios basado en una evaluación con integrales difusas tiene cierta generalizabilidad y aplicabilidad. El sistema de índices establecido y el modelo de evaluación integral no sólo son adecuados para la evaluación integral de cada estudiante, sino también para la evaluación integral de la aptitud de todos los chicos y chicas de una clase o colegio. Nivel de evidencia II; Estudios terapéuticos - investigación de resultados de tratamiento.

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This work presents a novel methodology to implement a fuzzy inference system (FIS) to overcome the measurement ambiguity that is typically observed in interferometric sensors. This ambiguity occurs when the measurand is determined by tracing the wavelength position of a peak or dip of a spectral fringe. Consequently, the sensor measurement range is typically limited to the equivalent of 1 free spectral range (FSR). Here, it is demonstrated that by using the proposed methodology, the measurement range of this type of sensor can be widened several times by overcoming the ambiguity over some FSR periods. Furthermore, in order to support the viability of the methodology, it was applied to a couple of temperature interferometric sensors. Finally, experimental results demonstrated that it was possible to quintuple the measurement range of one of the tested sensors with a mean absolute error of MAE = 0.0045 °C, while for the second sensor, the measurement range was doubled with an MAE = 0.0073 °C.

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In this study, the first goal is achieving a hybrid approach composed by an Interval Type-3 Fuzzy Logic System (IT3FLS) for the dynamic adaptation of α and ß parameters of Bee Colony Optimization (BCO) algorithm. The second goal is, based on BCO, to find the best partition of the membership functions (MFs) of a Fuzzy Controller (FC) for trajectory tracking in an Autonomous Mobile Robot (AMR). A comparative with different types of Fuzzy Systems, such as Fuzzy BCO with Type-1 Fuzzy Logic System (FBCO-T1FLS), Fuzzy BCO with Interval Type-2 Fuzzy Logic System (FBCO-IT2FLS) and Fuzzy BCO with Generalized Type-2 Fuzzy Logic System (FBCO-GT2FLS) is analyzed. A disturbance is added to verify if the FBCO-IT3FLS performance is better when the uncertainty is present. Several performance indices are used; RMSE, MSE and some metrics of control such as, ITAE, IAE, ISE and ITSE to measure the controller's performance. The experiments show excellent results using FBCO-IT3FLS and are better than FBCO-GT2FLS, FBCO-IT2FLS and FBCO-T1FLS in the adaptation of α and ß parameters.

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In this work we are presenting an approach for fuzzy aggregation in ensembles of neural networks for forecasting. The aggregator is used in an ensemble to combine the outputs of the networks forming the ensemble. This is done in such a way that the total output of the ensemble is better than the outputs of the individual modules. In our approach a fuzzy system is used to estimate the weights that will be assigned to the outputs in the process of combining them in a weighted average calculation. The uncertainty in the process of aggregation is modeled with interval type-3 fuzzy, which in theory can outperform type-2 and type-1. Publicly available data sets of COVID-19 cases for several countries in the world were utilized to test the proposed approach. Simulation results of the COVID-19 data show the potential of the approach to outperform other aggregators in the literature.

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In this work, we propose a fuzzy inference as a decision support system built in the MATLAB Fuzzy Logic Designer for evaluating manual material handling risk conditions. The input variables for the fuzzy decision were: (1) the total time duration of the manual material handling in one shift of 450 min, with 3 h considered the maximal exposition time; (2) 25 kg as a maximal mass reference which should never be exceeded; (3) the repetitiveness of the manual material handling task through the shift considering as the maximal frequency of four lifts per min. Results of 135 earlier direct ergonomic evaluations made using the method proposed by the ISO 11228-1 were used as validator results, and called "expected results". The experimentation intended to simulate an ergonomic evaluation in different boundary conditions of work and verify if the fuzzy interface could correctly replicate the results of the ergonomic evaluations. As validation, the list with the 135 expected results was compared against the evaluation made by the fuzzy logic interface, called "Work_Conditions". From the comparison, only three evaluations (0.02%) differed with respect to the expected results. Consequently, it is concluded that the fuzzy interface can be used as a tool for automating the determination of manual material handling ergonomic risk levels, with great precision.

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A prototype that simulates a wheelchair was built using electronic commercial devices and software implementation with the aim to operate the prototype using head movement and analyzing the system response. The controllers were simulated using MATLAB® toolbox and Python™ libraries. The mean time response of the system with manual control was 37,8 s. The mean orientation control response with constant speed was 36,5 s and the mean orientation control response with variable speed was 44,2 s in a specific route. The variable speed response is slower than constant speed due to head motion error. The system was rated such as" very good" by 10 participants using a System Usability Scale (SUS).

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Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these structures. In this context, artificial intelligence techniques show up as tools that can simplify the analysis and verification process not of the internal erosion itself, but of the effects that this pathology causes in the response of the dam to external stimuli. Therefore, within the scope of this paper, a methodological framework for monitoring internal erosion in the body of earth and rockfill dams will be proposed. For that, artificial intelligence methods, especially deep neural autoencoders, will be used to treat the acoustic data collected by geophones installed on a dam. The sensor data is processed to identify patterns and anomalies as well as to classify the dam's structural health status. In short, the acoustic dataset is preprocessed to reduce its dimensionality. In this process, for each second of acquired data, three parameters are calculated (Hjorth parameters). For each parameter, the data from all the available sensors are used to calibrate an autoencoder. Then, the reconstruction error of each autoencoder is used to monitor how far from the original (normal) state the acoustic signature of the dam is. The time series of reconstruction errors are combined with a cumulative sum (CUSUM) algorithm, which indicates changes in the sequential data collected. Additionally, the outputs of the CUSUM algorithms are treated by a fuzzy logic framework to predict the status of the structure. A scale model is built and monitored to check the effectiveness of the methodology hereby developed, showing that the existence of anomalies is promptly detected by the algorithm. The framework introduced in the present paper aims to detect internal erosion inside dams by combining different techniques in a novel context and methodological workflow. Therefore, this paper seeks to close gaps in prior studies, which mostly treated just parts of the data acquisition-processing workflow.

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BACKGROUND: In Brazil, many public hospitals face constant problems related to high demand vis-à-vis an overall scarcity of resources, which hinders the operations of different sectors such as the surgical centre, as it is considered one of the most relevant pillars for the proper hospital functioning, due to its complexity, criticality as well as economic and social importance. Proper asset management based on well-founded decisions is, therefore, a sine-qua-non condition for addressing such demands. However, subjectivity and other difficulties present in decisions make the management of hospital resources a constant challenge. METHODS: Thus, the present work proposes the application of a hybrid approach, formed by the QFD tools, fuzzy logic and SERVQUAL as a decision support tool for the quality planning of the surgical centre of the Onofre Lopes Teaching Hospital (Hospital Universitário Onofre Lopes-HUOL). To accomplish such objective, it was necessary to discover and analyse the main needs of the medical team working in the operating room, through the application of the SERVQUAL questionnaire, associated with fuzzy logic. RESULTS: Then, the most relevant deficiencies were transformed into entries for the QFD-fuzzy, where they were translated into project requirements. Soon after, the analysis of the existing relationships between the inputs and these requirements was carried out, generating the ranking of actions with the greatest impact on the improvement of the surgical centre overall quality. CONCLUSIONS: As a result, it was found that the proposed methodology can optimize the decision process to which hospital managers are submitted, improving the surgical centre operation efficiency.

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Mato Grosso, Brazil, is the largest soy producer in the country. Asian Soy Rust is a disease that has already caused a lot of damage to Brazilian agribusiness. The plant matures prematurely, hindering the filling of the pod, drastically reducing productivity. It is caused by the Phakopsora pachyrhizi fungus. For a plant disease to establish itself, the presence of a pathogen, a susceptible plant, and favorable environmental conditions are necessary. This research developed a fuzzy system gathering these three variables as inputs, having as an output the vulnerability of the region to the disease. The presence of the pathogen was measured using a diffusion-advection equation appropriate to the problem. Some coefficients were based on the literature, others were measured by a fuzzy system and others were obtained by real data. From the mapping of producing properties, the locations where there are susceptible plants were established. And the favorable environmental conditions were also obtained from a fuzzy system, whose inputs were temperature and leaf wetness. Data provided by IBGE, INMET, and Antirust Consortium were used to fuel the model, and all treatments, tests, and simulations were carried out within the Matlab® environment. Although Asian Soybean Rust was the chosen disease here, the model was general in nature, so could be reproduced for any disease of plants with the same profile.

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