# Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development

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## Abstract

**:**

## 1. Introduction

## 2. Artificial Neural Networks

_{n}, they are weighted and added up before going through some activation function to generate its output, this process is represented as ξ = ΣX

_{i}·W

_{i}. For each of the outgoing connections, this activation value is multiplied by the specific weight W

_{n}and transferred to the next node. If it considers a linear activation, the output would be given by y=α(wx+b) [65].

#### 2.1. The Activation Function of an Artificial Neural Network

#### 2.2. Types of Artificial Neural Network

- ▪
- Feedforward neural networks (FFNNs);
- ▪
- Recurrent neural networks (in discrete time) or differential (in continuous time);

#### 2.2.1. Feedforward Neural Networks

#### 2.2.2. Recurrent Neural Networks

- ▪
- Hopfield network: each neuron is completely symmetrically connected with all other neurons in the network. If the connections are trained using Hebbian learning, then the Hopfield network can function as a solid memory and resistant to the alteration of the connection. Hebbian learning involves synapses between neurons and their strengthening when neurons on both sides of the synapse (input and output) have highly correlated outputs [87] as shown in Figure 5b. There is a guarantee in terms of convergence for this network [88].
- ▪
- Elman network: this is a horizontal network where a set of “context” neurons is added. In Figure 5c the context units are connected to the hidden network layer fixed with a weight. The subsequent fixed connections result in the context units always keeping a copy of the previous values of the hidden units, maintaining a state, which allows sequence prediction tasks [89].
- ▪
- Jordan network: these are very similar to Elman’s networks. However, context units feed on the output layer instead of the hidden layer.

#### 2.3. Learning of Artificial Neural Networks

## 3. Application of Artificial Neural Networks for the Prediction of the Greenhouse Microclimate

#### 3.1. Greenhouse Microclimate

_{2}[103,104] are analyzed to predict different events implementing artificial intelligence, statistics and engineering [49,105,106,107,108].

#### 3.2. Feedforward Neural Networks Models for Prediction of Microclimate in Greenhouse

#### 3.3. Recurrent Neural Networks Models for Prediction of Microclimate in Greenhouses

#### 3.4. Other Artificial Neural Networks Models for Prediction of Microclimate in Greenhouses

## 4. Artificial Neural Networks in Energy Optimization of Greenhouses

## 5. Other Applications of Artificial Neural Networks in Greenhouses

_{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 Linker 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).

_{2}enrichment. Another aspect that is related to the concentration of CO

_{2}is photosynthetic efficiency and crop growth, and Moon 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.

_{2}concentration, and temperature. The network was a feedforward, they used software (Predict®, 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, Juan 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. 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.

_{2}concentration [165]. Zhang 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, Ting 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.

## 6. Perspectives: Greenhouse Artificial Neural Networks Application

#### 6.1. Agriculture 4.0 and the ANNs

#### 6.1.1. Precision Agriculture and Internet of Things

#### 6.1.2. Smart Agriculture

#### 6.2. Artificial Neural Networks and Greenhouses

#### 6.3. Classic Models versus ANNs

#### 6.4. The Input Variables in the ANNs and in the Prediction of Greenhouse Microclimate

#### 6.5. The Hidden Layer of ANNs and Their Importance in Prediction of Greenhouse Microclimate

^{2}also depends on the values in the propagation parameters.

#### 6.6. Learning Algorithms in the ANNs

#### 6.7. Database for ANNs and Prediction of Greenhouse Microclimate

#### 6.8. Artificial Intelligence

#### 6.9. Future of Deep Learning in Greenhouse Agriculture

_{2}, humidity, radiation, outside temperature and indoor temperature. One of the main disadvantages of this method is exposed: the large amount of data necessary for the training process.

#### 6.10. Future of Hybrid ANNs in Greenhouse Agriculture

## 7. Guidelines for the Application of Neural Networks in Greenhouses

## 8. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Interest topics in greenhouses and models classification. Genetic algorithms: GA; Particle swarm optimization: PSO; Artificial neural networks: ANNs.

**Figure 5.**Recurrent neural networks structure: (

**a**) Simple structure of a recurrent network, (

**b**) Hopfield network structure, (

**c**) Elman network structure.

**Figure 7.**Artificial neural networks on greenhouse microclimate prediction. ANNs: Artificial neural networks; MLP: Multilayer perceptron; RBF: Radial basis function.

Name | Graphic | Function | |
---|---|---|---|

Linear | $f\left(\xi \right)=a\bullet \xi +b$ | ||

Binary step | if $\xi \ge 0$, if $\xi <0$, | then $f\left(\xi \right)=1$, then $f\left(\xi \right)=0$, | |

Piecewise linear | if $\xi \ge {\xi}_{max}$, if ${\xi}_{min}>\xi >{\xi}_{max}$, if $\xi \le {\xi}_{min}$, | then $f\left(\xi \right)=1$, then $f\left(\xi \right)=a\bullet \xi +b$, then $f\left(\xi \right)=0$, | |

Sigmoid | $f\left(\xi \right)=\frac{1}{1+{e}^{-b\bullet \xi}},$ | interval (0,1) | |

Gaussian | $f\left(\xi \right)={e}^{-{\xi}^{2}},$ | interval (0,1] | |

Hyperbolic tangent | $f\left(\xi \right)=\frac{2}{1+{e}^{-2\bullet \xi}}-1,$ | interval [−1,1] |

**Table 2.**Applications of feedforward neural network models for prediction of microclimate in greenhouses.

Author | Inputs Variables | Outputs Variables | Artificial Neural Network (ANN) Architecture | Activation Functions | Training Method | Comments |
---|---|---|---|---|---|---|

Zeng et al. [38]; Hu et al. [99] | - Outside temperature
- Outside humidity
- Wind speed
- Solar radiation
- Carbon dioxide concentration
- Heating
- Ventilation
- Carbon dioxide injection
| - Inside temperature
- Inside humidity
| - Feedforward neural network (FFNN) specifically radial base function (RBF).
- Input layer
- Hidden layer
- Hidden layer
- Output layer
| - Gaussian transfer function for the hidden layer
| Gradient descent back-propagation (BP) | Results show that the model proposed has better adaptability, and more satisfactory real-time control performance compared with the offline tuning scheme using genetic algorithm (GA) optimization and proportional, and derivative control (PD) method. |

He et al. [60] | - Outside air temperature
- Outside humidity
- Wind speed
- Solar radiation
- Inside air temperature
- Open angle of top vent and side vent
- Open ration of sunshade curtain
| - Inside humidity
| FFNN. The model had three layers: - Input layer
- Hidden layer
- Hidden layer
- Output layer
| - The sigmoid transfer function for the hidden layer
- The logistic sigmoid transfer function for the output layer
| BP | The principal component analysis (PCA) simplified the data samples and made the model had faster learning speed. |

Ferreira et al. [112] | - Outside air temperature
- Solar radiation Inside humidity
| - Inside temperature
| FFNN specifically RBF. | Off-line methodology:- In method 1 they used the linear least squares (LS)
- In method 2 they used the orthogonal least squares (OLS)
- In method 3 they used the Levenberg – Marquardt (LM)
- In method 1 they used the extended Kalman filter (EKF)
- In method 2 they based on the interpolation problem with generalized radial basis functions (GRBFs) with regularization
- In method 3 they used the LM
| In this paper off-line training methods and on-line learning algorithms are analyzed.Whether off-line or on-line, the LM method achieves the best results. | |

Dariouchy et al. [123] | - External humidity
- Total radiation
- Wind Direction
- Wind Velocity
- External Temperature
- Internal temperature
- Internal humidity
| - Internal temperature
- Internal humidity
| FFNN. | Logistic sigmoid transfer function for all layers | BP | Different architectures were tested. Initially, networks with a single hidden layer were built by successively adding two additional neurons to it. Networks with two hidden layers were also tested, triangular structures were considered, for which the number of neurons in one layer is greater than the next. The optimal model was composed of a hidden layer with six neurons. |

Taki et al. [124] | They used four ANNs models: First model: - Inside air temperature
- Solar radiation on the roof
- Wind speed
- Outside air temperature
- Inside soil temperature
- Inside air humidity
- Solar radiation on the roof
- Inside air temperature
- Inside air temperature
- Solar radiation on the roof
- Inside roof temperature
- Inside air humidity
- Inside air temperature
- Inside roof temperature
- Outside air temperature
- Solar radiation on the roof
| They used four ANNs models: First model: - Roof temperature
- Soil humidity
- Soil temperature
- Inside air humidity
| FFNN. | - Sigmoid transfer function for the hidden layer
- Linear transfer function for the output layer
| Basic BP | Demonstrated that multilayer perceptron (MLP) network with 4 inputs in first layer, 6 neurons in hidden layer and one output, and MLP network with 4 inputs in the first layer, 9 neurons in hidden layer and one output had the best performance to predict inside soil, inside air humidity, inside roof and soil temperature with a low error. |

Seginer et al. [125] | Weather variables:- Outside temperature
- Outside humidity
- Outside solar radiation
- Wind speed
- ▪
- Heater heat flux
- ▪
- Vent opening angle
- ▪
- Misting time fraction
- ▪
- Leaf area index (LAI)
- ▪
- Julian day
- ▪
- Hour of day
| - ▪
- Inside temperature
- ▪
- Soil temperature
- ▪
- Inside humidity
- ▪
- Inside radiation
| FFNN. For the model of the neural network (NN) used a commercial program (NeuroShell™, Ward System Group, Inc.) The model had three layers: - ▪
- Input layer
- ▪
- Hidden layer (The number of the nodes was determined by the program)
- ▪
- Output layer
| Sigmoid function (S-shape logistic function) for the three layers | BP | They found that leaf area index (LAI) did not have a significant influence on the internal conditions of the greenhouse. Also, they determined that the wind direction has minimal effects on the results. |

Laribi et al. [126] | - ▪
- Outside temperature
- ▪
- Outside humidity
- ▪
- Wind speed
- ▪
- Solar radiation
| - ▪
- Internal temperature
- ▪
- Internal humidity
| FFNN. The networks had three layers: - ▪
- Input layer
- ▪
- Hidden layer with 7 neurons
- ▪
- Output layer with 2 neurons
| - ▪
- The sigmoid transfer function for the hidden layer
- ▪
- The linear transfer function for the output layer
| BP | Two approaches were used to predict the climate of the greenhouse, multimode modeling and neural networks. They point out that the neural network model is easier to obtain and specify that it can be used to simulate different output variables at the same time. |

Bussab et al. [127] | - ▪
- External temperature
- ▪
- External global radiation
- ▪
- External relative humidity
- ▪
- Wind speed
| - ▪
- Internal Temperature
- ▪
- Internal
- ▪
- Relative Humidity
| FFNN. A multilayer NN with two hidden layers: - ▪
- First hidden layer with 40 neurons
- ▪
- Second hidden layer
- ▪
- with 20 neurons
| - ▪
- The hyperbolic tangent function for input layer and for the first hidden layer
- ▪
- The linear function for second hidden layer
| BP | The NN obtained better results in the prediction of the internal temperature than of the internal relative humidity |

Salazar et al. [128] | - ▪
- Outside average temperature
- ▪
- Outside relative humidity
- ▪
- Wind velocity
- ▪
- Solar radiation
| Three different network architectures were tested, where the number of outputs was varied:- ▪
- 1st inside temperature
- ▪
- 2nd inside relative humidity
- ▪
- 3rd inside temperature and relative humidity
| FFNN. The networks had three layers: - ▪
- Input layer
- ▪
- Hidden layer
- ▪
- Output layer
| Hyperbolic tangent function for all layers | BP | They report that the third network obtained better results in the prediction of temperature and relative humidity, which explains the interactions between these two variables. Also, they emphasize the relevance of the input variables in the predicted variables, in this study the solar radiation was the most important. |

Alipour et al. [129] | - ▪
- Wind speed and direction
- ▪
- Relative humidity
- ▪
- Infra-red light
- ▪
- Visible light
- ▪
- Air temperature
- ▪
- Carbon dioxide concentration
| - ▪
- Inside temperature
- ▪
- Light
- ▪
- Inside Relative humidity
- ▪
- Carbon dioxide
| FFNN. Three different configurations were tested: - ▪
- The feedforward neural network with several delays in input
- ▪
- Two layers with one feedback from the hidden layer and delay in input
- ▪
- Three layers neural network with two feedbacks from hidden layer and delay in input
| Not specified | The three-layer neural network with two hidden-layer feedbacks and delayed entry showed better relative humidity and light index results. The FFNN with multiple entries delays better predicted the temperature and infrared index. | |

Outanoute et al. [130] | Values and the previous value of:- ▪
- External temperature
- ▪
- External relative humidity
- ▪
- Command of heater and ventilator
- ▪
- Internal temperature
- ▪
- Internal relative humidity
| - ▪
- Internal Temperature
- ▪
- Internal relative humidity
| FFNN. The networks had three layers: - ▪
- Input layer
- ▪
- Hidden layer (the number of nodes depending on the type of network training)
- ▪
- Output layer
| - ▪
- The logistic sigmoid transfer function for the hidden layer
- ▪
- The linear transfer function for the output layer
| - ▪
- Gradient descent with momentum and adaptive learning rate algorithm (GDX) for seven nodes on the hidden layer
- ▪
- Broyden-Fletcher-Golfarb-Shanno (BFGS) quasi-newton BP for five nodes on the hidden layer
- ▪
- Resilient Back-propagation algorithm (RPROP) for twelve nodes on the hidden layer
| Three NNs were tested with different training algorithms.BFGS is better than the GDX and the RPROP. |

Taki et al. [131] | - ▪
- Outside air temperature
- ▪
- Wind speed
- ▪
- Outside solar radiation
| - ▪
- Inside air temperature
- ▪
- Soil temperature
- ▪
- Plant temperatures
| FFNN.- ▪
- Feedforward networks, specifically MLP and RBF, were used in this investigation. Also, different algorithms for network training were applied and compared with each other and with the support vector machine (SVM) method
| For MLP:- ▪
- No transfer function for the first layer was used
- ▪
- Sigmoid functions for the hidden layers
- ▪
- The linear transfer function for the output layer
| - ▪
- LM back- propagation
- ▪
- Bayesian regularization
- ▪
- Scaled conjugate gradient BP
- ▪
- RPROPVariable learning rate BP
- ▪
- Gradient descent with momentum BP
- ▪
- Gradient descent with adaptive learning rate BP
- ▪
- Gradient descent BP
- ▪
- BFGS quasi-Newton back- propagation
- ▪
- Powell–Beale conjugate gradient BP
- ▪
- Fletcher–Powell conjugate gradient BP
- ▪
- Polak–Ribiere conjugate gradient BP
- ▪
- One step secant BP
| Thirteen different training algorithms were used for ANNs models. Comparison of the models showed that RBFANNs has lowest error between the other models |

**Table 3.**Applications of recurrent neural network models for prediction of microclimate in greenhouses.

Author(s) | Inputs Variables | Outputs Variables | Artificial Neural Network (ANN) Architecture | Activation Functions | Training Method | Comments |
---|---|---|---|---|---|---|

Fourati et al. [133] | - ▪
- External temperature
- ▪
- External hygrometry
- ▪
- Global radiant
- ▪
- Wind speed
| - ▪
- Internal temperature
- ▪
- Internal hygrometry
| Recurrent neural networks (RNN).- ▪
- Elman neural network
| Sigmoid function for the hidden layer | Back-propagation (BP) | Elman neural network was used to emulate the direct dynamics of the greenhouse. Based on this model, a multilayer feedforward neural network (FFNN) was trained to learn the inverse dynamics of the process to be controlled. |

Fourati et al. [134] | - ▪
- External temperature
- ▪
- External hygrometry
- ▪
- Global radiant
- ▪
- Wind speed
| - ▪
- Internal temperature
- ▪
- Internal humidity
| RNN.- ▪
- Elman neural network
| Sigmoid function for the hidden layer | Neural control using with Online training:- ▪
- Generalized learning
- ▪
- Specialized learning
| In order to evaluate the different control strategies (offline and online training), they defined an error criterion. When they compared the error between training methods, obtained that online methods are better than offline method (FFNN based on Elman neural network). |

Hongkang et al. [135] | - ▪
- Internal temperature
- ▪
- Internal humidity
| RNN.- ▪
- Elman neural network
| Sigmoid function for the hidden layer | Dynamic BP | Different from the traditional batch trained neural network, the dynamic BP method in the training process uses the output of the previous step together with the next input to the network, and the calculator outputs the weights. They compared a dynamic BP RNN whit untrained RNN, the Elman network based on dynamic BP algorithm can accurately predict the temperature and humidity in the greenhouse better than the untrained RNN | |

Dahmani et al. [136] | - ▪
- External temperature
- ▪
- External Humidity
- ▪
- Global radiation
- ▪
- Wind speed
- ▪
- Heating input
- ▪
- Opening of the shutter
- ▪
- Misting input
- ▪
- Curtain entrance
| - ▪
- Internal temperature
- ▪
- Internal humidity
| RNN.- ▪
- Elman neural network
| Sigmoid function for the hidden layer | BP | The control law is based on a multilayer perceptron (MLP) network type trained to imitate the inverse dynamics of a greenhouse. The direct dynamics of the greenhouse were described by a RNN of the Elman type |

Salah et al. [137] | - ▪
- External temperature
- ▪
- External hygrometry
- ▪
- Heating
- ▪
- Sliding shutter in degrees
- ▪
- Sprayer
- ▪
- Curtain
| - ▪
- Internal temperature
- ▪
- Internal hygrometry
| RNN. Three Elman neural network are considered: - ▪
- One hidden and context layers
- ▪
- Two hidden and context layers
- ▪
- Three hidden and context layers
| Sigmoid function for the hidden and output layers | Deep learning (DL) where BP algorithm was used | Concluded that the network with two hidden layers and two context layers were the most efficient to describe the system |

Author(s) | Inputs Variables | Outputs Variables | Artificial Neural Network (ANN) Architecture | Activation Functions | Training Method | Comments |
---|---|---|---|---|---|---|

Lu et al. [140] | - ▪
- External temperature
- ▪
- External humidity
- ▪
- Internal temperature
- ▪
- Internal humidity
| - ▪
- Internal temperature
- ▪
- Internal humidity
| Nonlinear autoregressive with external input neural network (NNARX) The fundamental structure was three-layer feedforward neural network (FFNN): - ▪
- Input layer with 2 nodes
- ▪
- Hidden layer with 2 neurons
- ▪
- Output layer with 1 neuron
| - ▪
- Hyperbolic tangent function for hidden layer
- ▪
- Linear transfer function for the output layer
| Levenberg–Marquardt (LM) | Compared the NNARX with the genetic algorithm (GA) model, the prediction obtained by the neural network (NN) method was better |

Zhang et al. [141] | - ▪
- Temperature
- ▪
- Humidity
| - ▪
- Skylight
- ▪
- Sun-shade net Circulation fan
- ▪
- Side windows
- ▪
- Fuel heater
- ▪
- Micro-mist humidifier
| Fuzzy Neural Network The structure was four-layers: - ▪
- Input layer
- ▪
- Second layer were represented a linguistic variable
- ▪
- Third layer where the function was to complete the fuzzy logic inference, and calculate the fitness of each rule
- ▪
- Output layer
| The inputs and outputs are fuzzified | Gaussian function as the membership function for the layers | Compared the fuzzy neural network controller with the conventional proportional, integral and derivative controller (PID) to verify the performance. The fuzzy neural network had small overshoot, fast response, good stability, and small steady-state error |

Patil et al. [142] | - ▪
- Outside air temperature
- ▪
- Outside air relative humidity
- ▪
- Global solar radiation flux density
- ▪
- Cloud cover
| - ▪
- Inside air temperature
| NNARX. The fundamental structure was three-layer feedforwardneural network: - ▪
- Input layer with 4 inputs
- ▪
- Hidden layer with 24 neurons
- ▪
- Output layer with one output
| - ▪
- Hyperbolic tangent function for hidden layer
- ▪
- Linear transfer function for the output layer
| LM | Eighteen different models were tested. auto regressive with exogenous input (ARX), autoregressive moving average with exogenous input variables (ARMAX) and NNARX models were compared to each other and concluded that NNARXperformed better. |

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**MDPI and ACS Style**

Escamilla-García, A.; Soto-Zarazúa, G.M.; Toledano-Ayala, M.; Rivas-Araiza, E.; Gastélum-Barrios, A.
Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development. *Appl. Sci.* **2020**, *10*, 3835.
https://doi.org/10.3390/app10113835

**AMA Style**

Escamilla-García A, Soto-Zarazúa GM, Toledano-Ayala M, Rivas-Araiza E, Gastélum-Barrios A.
Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development. *Applied Sciences*. 2020; 10(11):3835.
https://doi.org/10.3390/app10113835

**Chicago/Turabian Style**

Escamilla-García, Axel, Genaro M. Soto-Zarazúa, Manuel Toledano-Ayala, Edgar Rivas-Araiza, and Abraham Gastélum-Barrios.
2020. "Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development" *Applied Sciences* 10, no. 11: 3835.
https://doi.org/10.3390/app10113835