## 6 Perspectives: Greenhouse Artificial Neural Networks Application
### Classic Models versus ANNs  
Greenhouse climate models can be classified into two categories [125]: models for the design of new greenhouses and models for climate control of existing structures. The latter are also known as classic models, and they are based on steady state energy balances. The number of parameters in this type of model is small compared to MIMO (multiple input multiple output) black box models, this being one of its main advantages. On the other hand, mechanistic models provide a clear physical explanation of the greenhouse environment such as static [198; 199] and dynamic models [100]. Static models are based on the static energy balance of the greenhouse components and usually their heat storage capacities are not considered [200]. The relevance of physics-based models in greenhouses is that they take the physical parameters that describe the system, they can include the location of the greenhouse, local weather conditions, geometry, construction materials, hours of operation, systems of air conditioning and settings. That is, they allow its use for the design phase and help to evaluate the energy performance of the greenhouse [125; 201]. However, the current state of climate control still leaves much room for improvement [202] and optimal control of greenhouse environments can be improved by combined models to allow selection of greenhouse designs and control algorithms to maximize the room for improvement benefit such as models based multi-objective optimization [203; 204]. Figure 7: Artificial neural networks on greenhouse microclimate prediction. ANNs: Artificial neural networks; MLP: Multilayer perceptron; RBF: Radial basis function. From the studies presented in Table 2, [PERSON] et al. [127] found that the efficiency of the FFNNs in models based on mass flow and energy equations is better when forecasting the relative humidity and internal temperature of a greenhouse. The ANN was more accurate in 81% of the cases than the classic method in forecasting the internal relative humidity and 62% more efficient in forecasting the internal temperature. These results comply with what was mentioned in [PERSON] et al. [125]. Among the reasons why ANNs are more efficient than classic models is that mentioned above, the ability to consider more parameters, that is, with a sufficient number of adjustable parameters, is capable of making accurate predictions, provided that it presents all the factors that have a significant influence on the outputs (in this case, the internal relative humidity and the internal temperature). In addition, in the case of ANNs, you can always choose to increase the number of neurons in the hidden layers to increase the predictive power of the network. However, there is a point at which the network will not show significant improvement no matter how many neurons it has in the hidden layer. For [PERSON] et al. [127], a configuration with two hidden layers was optimal, where the first hidden layer consisted of 40 neurons and the second of 20. Studies comparing the effectiveness of ANNs with classic models are few, however, [PERSON] et al. [125], [PERSON] [96] and [PERSON] [205] expose several factors that make networks have greater predictive power, the importance of reducing the number of inputs on a neural network and how it can help the network with classic models. Supporting the ANNs with classic models for predicting the microclimate in greenhouses brings great benefits. [PERSON] and [PERSON] [205] were among the first to develop such a model. The reason for using physical models in black box models, specifically in NNs is due to its main disadvantage. The poor extrapolation property, in other words, lacks prior knowledge, and the most evident in sigmoidal black box models. [PERSON] and [PERSON] [205] demonstrated that hybrid models of this type produce efficient predictions, especially in the operational domain, decreasing their precision in the training domain. The proposal is to use the classical model to generate \"synthetic\" training data.