Modeling Key Parameters for Greenhouse Using Fuzzy Clustering Techniques

The clustering techniques are usually used in classification and pattern recognition. Moreover, fuzzy logic is used for system modeling when the information is scarce, inaccurate or its behavior is described using a complex mathematical model. As example of this type of system, a greenhouse is considered, where the variables are: in-house and out-house temperature, humidity for both inside and outside the greenhouse and wind direction. These variables show a dynamic and non-linear behavior; being the in-house temperature and internal humidity the variables of concern for the greenhouse control and modeling. In this project, the development and implementation of three clustering algorithms, being fuzzy K-means, Fuzzy C-means and fuzzy clustering subtractive, is presented. This project is used as the foundation for the design of fuzzy systems and its application in temperature and humidity modeling of a greenhouse used as a laboratory of biotronics at the Universidad Autonoma de Queretaro.