## 5 Other Applications of Artificial Neural Networks in Greenhouses
They made arbitrary connections from the hidden layer to the context layer, they also used the hyperbolic tangent function as an activation function in the hidden layer, while in the other layers they used linear activation functions. A fuzzy GA, was used for the learning process, which deals with a modification to the traditional method of GA through a crossover with fuzzy logic. The simulation results showed that the modified Elman network and the fuzzy genetic algorithm are better for the description of the system compared to an Elman network trained using a BP algorithm. The transpiration of plants in greenhouses is an element that represents a challenge in matters of modeling since the elements that intervene with this phenomenon remain a challenge for their mathematical representation [161]. The application of ANNs for modeling the transpiration of greenhouse crops is a way of presenting reliable results, as presented by [162]. The exposed model consisted of a modified BP network, since the randomness of the conventional BP algorithm in the weights and the threshold in each training represented a disadvantage for the prediction of transpiration. The modified algorithm was a genetic algorithm that, through an optimization adjustment function selected the best weights and thresholds, used a network called genetic algorithms-back propagation neural network (GA-BPNN). Also, using a NNARX model, the error accumulated by the long training time was only recorded. Wireless sensor networks (WSN) are a new form of distributed computing and are encompassing a wide variety of applications that can be implemented with them [163]. In greenhouses it is primarily concerned with collecting environmental information and sending it to the grouping nodes via wireless data link. WSN is a type of self-organizing wireless network that takes data as its core [164]. The role of this technology and ANNs is that they are a good combination for controlling greenhouses. WSN can be used to monitor CO\({}_{2}\) concentration [165]. [PERSON] et al. [166] carried out a greenhouse control system using a WSN to collect data on temperature, humidity and CO\({}_{2}\) concentration. They related the internal environmental factors and the actuators of the system for the implementation of a fuzzy rule and combined with a neural network. The fuzzy neural network consisted of three inputs and six outputs to improve control precision. Moreover, [PERSON] et al. [167] measured and collected real-time data on air temperature, humidity, CO\({}_{2}\) concentration, soil temperature, soil moisture, and light intensity using WSN. The measurement of these parameters was to predict the photosynthetic rate of plants and in turn to quantitatively regulate CO\({}_{2}\). The prediction model was established based on a BP neural network. The environmental parameters were used as input neurons after being processed by PCA, and the photosynthetic rate was taken as the output neuron. There are many important areas where WSN can improve. One of the aspects to consider is to give the sensor networks the ability to reprogram themselves wirelessly, allowing users not to physically interact with the sensor nodes. This wireless reprogramming can be based on the concept of NNs as proposed by [PERSON] et al. [163], and thus be able to implement it in greenhouses.