I.L. López-Cruz , L. Hernández-Larragoiti
In producing tomatoes and other vegetables in greenhouses, it is important to optimize and to control the indoor environment, by using mechanistic dynamic models of the system. However, the development and use of greenhouse models based on first principles is a costly and a time-consuming task. Black-box models based on measurements of inputs and outputs of the system are a promising approach to study complex and nonlinear systems such as greenhouses. In the present research, neuro-fuzzy models are generated and studied in order to predict the behavior of air temperature and relative humidity inside a naturally ventilated Mexican greenhouse. Input variables were: air temperature (°C) and relative humidity (%), global solar radiation (W m-2) and wind velocity (m s-1), all measured outside the greenhouses. Output variables were air temperature (°C) and relative humidity (%) measured inside the greenhouse. The sampling time was each minute. Several neuro-fuzzy models, were generated using the ANFIS (Adaptive Neuro-Fuzzy training of Sugeno-type Inference System) neuro-fuzzy model, which is available in the Fuzzy Logic Toolbox of Matlab. Both grid and fuzzy subtractive clustering partitioning of the data were used to generate the fuzzy inference system. Also several (50:50%, 60:40%, 65:35%, 70:30%, 75:25%, and 80:20%) empirical data partitioning were analyzed. Up to three membership functions (Gaussian, Generalized Bell and Trapezoidal) were considered for the inputs and the constant and lineal membership functions for the outputs. Besides, were tested several training epochs. The dataset was generated with climatic information collected from a greenhouse with natural ventilation located at the University of Querétaro, Mexico, with semi-arid weather conditions. Several criteria were used to evaluate models’ performance such as: RMSE, R2, 1:1 line for measured and calculated values, and also the regression line between calculated and measured values. The results showed that neuro-fuzzy models predict accurately climate behavior inside a greenhouse.
López-Cruz , I.L. and Hernández-Larragoiti, L. (2012). NEURO-FUZZY MODELS FOR AIR TEMPERATURE AND HUMIDITY OF A GREENHOUSE. Acta Hortic. 927, 611-617
DOI: 10.17660/ActaHortic.2012.927.75
greenhouse climate, dynamic models, fuzzy systems, neural nets

Acta Horticulturae