## 6 Perspectives: Greenhouse Artificial Neural Networks Application
### The Input Variables in the ANNs and in the Prediction of Greenhouse Microclimate  
One of the important issues in a neural network is to reduce the number of inputs. [PERSON] et al. [60] used PCA as the base in a BP neural network. In the proposed network, the input layer consisted of 4 inputs, which were the main components and the output layer was 1, the internal humidity of the greenhouse. To determine the behavior of the PCA-based BP network, it was compared to a conventional BPNN network, where the training times of these methods were 42 and 130, respectively. The PCA allows network training to be faster. However, although the PCA-based network achieved 85% accuracy it is still less than the conventional BPNN network, and this is because there is a loss of original data information. With these results they also compared the PCA-based BPNN network with a stepwise regression model, when calculating the mean squared error (MSE), the PCA-based BP network performed better with a value of 1.6745 while with the stepwise regression method they obtained a value of 4.5437. The use of techniques such as the PCA to determine and simplify the entries of the ANNs is a very viable option, although this may affect its effectiveness, it is still better than other methods and is rarely used.  
Another way to delimit their number is to apply the sensitivity analysis to the different input variables to determine which ones are more relevant to the variables whose behavior is to be determined, just as did [PERSON] et al. [125], who found that solar radiation and outdoor air temperature are the factors that have the greatest impact on the temperature and internal humidity of the greenhouse. Similarly, [PERSON] et al. [128], using a sensitivity analysis, determined that the most important variables for predicting temperature are outdoor temperature and solar radiation. Both [PERSON] et al. [125] and [PERSON] et al. [128] analyzed the effect of considering separate models for each of the outputs, that is, a model to predict humidity, one more to predict internal temperature and finally a model in which consider two outputs, the humidity and the internal temperature of the greenhouse. They concluded that the separate models present poorer predictions, this due to noise from unnecessary inputs. For their part, [PERSON] et al. [128] obtained very similar results in the three models, when calculating the coefficients for the three cases, in the first model which predicted the internal temperature they obtained a value of 0.976, while the model that predicted the internal humidity obtained a value of 0.982 and the third model for the internal temperature and internal humidity obtained values of 0.975. [PERSON] et al. results show that the third model is less efficient in predicting the output variables, however it is not significant and has the advantage of predicting the output variables at the same time.  
One way in which you can \"feedback\" to a FFNN is through delays in the input variables, this process consists of considering certain outputs also as inputs; these outputs, when considered inputs, have a certain delay to be able to feed the network with new information on the variable of interest. [PERSON] et al. [129] tested three network configurations with this type of delay to forecast the relative humidity, the infrared index, the light index and the internal temperature of the greenhouse. For example, to predict internal temperature, [PERSON] et al. [129] found that the optimal structure should use a direct feeding neural network with 7 input delays and 5 neurons in the hidden layer. In this way, a way to build FFNNs that have been exploited very little is presented, and of the studies analyzed only [PERSON] et al. [129] and [PERSON] et al. [130] explore this path.