Dynamic temperature models of a soiless greenhouse

In this paper climate discrete-time dynamic models for the inside air temperature of a soilless greenhouse are identified, using data acquired during two different periods of the year. These models employ data from air temperature and relative humidity.

Identification of greenhouse climate dynamic models

In this paper climate discrete-time dynamic models for the inside air temperature of two different greenhouses are identified, using data acquired during two different periods of the year. These models employ data from air temperature and relative humidity (both outside and inside the greenhouse), solar radiation, wind speed, and control inputs (ventialtion, etc.).

Application of radial basis function neural networks to a greenhouse inside air temperature model

The problem with the adequacy of radial basis function neural networks to model the inside air temperature as a function of the outside air temperature and solar radiation, and the inside relative humidity in an hydroponic greenhouse is addressed.

Real-time parameter estimation of dynamic temperature and humidity models for greenhouse adaptive climate control

A real-time parameter estimator for the climate discrete-time dynamic models of a greenhouse located at the North of Portugal are presented. The experiments showed that the second order models identified for the air temperature and humidity achieve a close agreement between simulated and experimantal data.

In vitro spore germination of Polystichum drepanum, a threatened fern from Madeira Island

Polystichum drepanum (Sw) C. Presl is a threatened fern endemic to a few forest areas in the north-west of Madeira Island. The aims of this work were to establish suitable culture conditions for in vitro germination of spores, and to evaluate short-term storage conditions for P drepanum spores. The highest frequency of germination was obtained in Murishage and Skoog (MS) liquid medium, without agitation. However, gametophytes maintained in MS liquid medium did not grow and, after 4 weeks, became anoxic and died. Thus, after germination in liquid medium, gametophytes were transferred to an MS double-phase culture system for further growth. The effects of storage period, temperature, and relative humidity during storage on in vitro spore germination were studied. Spore viability was assessed after 2, 4 and 6 months, and high viability (> 94%) was observed in all the assays. However, germination capability decreased with increased storage periods. The number of sporophytes obtained also decreased with prolonged storage periods. The results indicate that spores of R drepanum stored for 4 months at 21 degrees C maintain high viability and high germination frequency. ne sporophytes obtained were acclimatised in a mixture of peat and vermiculite [2:1 (v/v)] under high relative humidity (90-95%). Seventy-five sporophytes were successfully acclimatised to ex vitro conditions and showed active growth in the glasshouse.

Predicting the greenhouse inside air temperature with RBF neural networks

The application of the Radial Basis Function (RBF) Neural Network (NN) to greenhouse inside air temperature modelling has been previously investigated (Ferreira et al., 2000a). In those studies, the inside air temperature is modelled as a function of the inside relative humidity and of the outside temperature and solar radiation. A second-order model structure previously selected (Cunha et al., 1996) in the context of dynamic temperature models identification, is used.

Comparison of on-line learning algorithms for RBF models in greenhouse environmental control problems

The problem with the adequacy of radial basis function neural networks to model the inside air temperature as a function of the outside air temperature and solar radiation, and the inside relative humidity in an hydroponic greenhouse is addressed.