## 5 Other Applications of Artificial Neural Networks in Greenhouses  
The ability of ANNs to model complex and non-linear systems allows their application in different tasks in greenhouses, not only in predicting the microclimate where the great majority of studies focus. As indicated, the internal temperature and humidity are among the variables that generate the most interest to predict their behavior. However, other elements such as CO\({}_{2}\) enrichment in hot climates exert considerable weight for the proper functioning of the greenhouses, since a balance is required between the need to ventilate and enrich as explained by [PERSON] et al. [157]. They developed NNs for the prediction of temperature and CO\({}_{2}\) concentration separately, the training algorithm used was the BP. The activation function chosen for the hidden layer was sigmoidal, while the linear activation function was used for the output layer. In this case, it was decided to reduce the size of the NN instead of a more complex NN with multiple inputs and multiple outputs (MIMO). The models fit the data well, and also generated reliable optimization results. In addition, they demonstrated the effect of evaporation cooling by extending the duration of CO\({}_{2}\) enrichment. Another aspect that is related to the concentration of CO\({}_{2}\) is photosynthetic efficiency and crop growth, and [PERSON] et al. [158] performed an ANN to predict the concentration of CO\({}_{2}\) in greenhouses considered environmental factors. The network consisted of a feedforward, with an architecture of an input layer (10 neurons), two hidden layers (the number of neurons of 32, 64, 128, 256, 512, 1024, and 2048 were being changed with the aim of finding the optimal ANN, both layers had the same number of neurons) and one output layer (one neuron). The variables considered as inputs were internal temperature, internal relative humidity, internal atmospheric pressure, photosynthetic photon flow density (PPFD), external temperature, external relative humidity, external atmospheric pressure, wind speed, and wind direction while the CO\({}_{2}\) concentration was the output variable. The transfer function that was used throughout the layers was the rectified linear unit (ReLU) and the training algorithm was the AdamOptimizer. The results obtained show that the prediction of CO\({}_{2}\) concentration is possible through ANNs with a coefficient of determination of 0.97. However, the estimates made in the study were limited to data obtained from each greenhouse and the authors indicate that it is necessary that ANNs should be trained with data from several measurement sites to generalize all possible situations. The networks potential for the growth improvement of greenhouse crops by means the forecast and description of the microclimate has been exposed, these studies are based on the fact of having the ideal conditions of the plant controlling through predictions one, two or several environmental factors. However, other research has evaluated the close relationship between crop yield, growth and water use in response to changes in the greenhouse climate. An ANN was developed by [159] to predict the yield, growth and amount of water used in tomato crops under the greenhouse. The input variables were radiated, relative humidity, growth, CO\({}_{2}\) concentration, and temperature. The network was a feedforward, they used software (Predict(r), v3.21) for the construction of the network which was responsible for defining the structure and the training process. The yield, growth, and use of water responded similarly to the climatic variables. Radiation and temperature remain the most influential variables, however, the CO\({}_{2}\) concentration has a significant weight in the positive change of the output variables. On the other hand, [PERSON] et al. [160] modeled the tomato growth process. The factors considered influential and input elements were solar radiation, temperature, humidity, and CO\({}_{2}\). A modified Elman network was used to model the dynamics of the system.