## 3 Application of Artificial Neural Networks for the Prediction of the Greenhouse Microclimate
### Greenhouse Microclimate  
Greenhouses are complex and non-linear systems, and a means to achieve a controlled agricultural production [7]. Greenhouse production systems have a complex dynamic impulse by external factors (meteorological), control mechanisms (ventilation openings, exhaust fans, heaters, evaporative cooling systems, etc.) and internal factors (crops and internal components) [6].  
Concerning the greenhouse microclimate and its control, the crop represents the central element, but also the most complex part of the system. Due to this complexity and the great diversity of crops in greenhouses, it is common to consider only certain general issues that are more relevant to the response of crops in relation to greenhouse microclimate [97].  
Greenhouse climates refer to the set of environmental variables in this system that affect the growth of crops and their development [98]. Greenhouse microclimate control has received considerable attention in recent years due to its great contribution to the improvement of crop yield [13; 29; 99]. The different factors such as temperature [100; 101], relative humidity [102], amount of CO\({}_{2}\)[103; 104] are analyzed to predict different events implementing artificial intelligence, statistics and engineering [105; 106; 107; 108].  
Numerous greenhouses use a conventional control, but this control strategy may not be suitable to guarantee the desired performance [15; 26]. In this scenario, various strategies and control techniques have been proposed like generalized predictive control [109], optimal control [110], model predictive control [16; 111], NNs control [112], fuzzy control [113; 114; 115], robust control [116; 115] and linear-quadratic adaptive control [117]. The vast majority of these proposals are simulations of the behavior of the variables and possible control against these changes and are also focused on a specific crop [118].  
The application of NNs in the control of microclimates is a topic that has currently gained interest. NNs provide reliable models that can reflect the non-linear characteristics of the greenhouse that are difficult to solve using traditional techniques, do not require any prior knowledge of the system. and are very suitable for modeling dynamic systems in real time [119; 120].  
Temperature and humidity are of the most relevant parameters in the greenhouse microclimate, since they have complex exchanges and interactions of heat and mass between the inner air, other elements of the greenhouse and the outside. Building a model is a difficult assignment with simple mathematical formulas or transformation functions. However, the method of building models with ANNs has a great capacity for mapping non-linear functions, which is applied to many production process systems [121].  
For the network design, air temperature and humidity of the greenhouse air are generally considered outputs, this due to the aforementioned factors. However, setting the inputs is more complicated and required an understanding of the system. It is not convenient to consider a large number of inputs, as this could cause uncontrolled extrapolations instead of increasing the estimation power. Three elements can be considered in order to consider an input variable: (1) correlation of selected input with other inputs, (2) physical dependence nature of the output and the input (3) input variable range. The third point, solar radiation and greenhouse temperature, can be considered an example [96].