# Deep Learning Entrusted to Fog Nodes (DLEFN) Based Smart Agriculture
## 2 Literature Review  
DL is a kind of artificial neural network (ANN) consisting of many layers that are used to process input data. ANN is the core technology of DL, inspired by the biological neural network of the human brain. An artificial neuron (AN) abstracts the functions of a biological neuron, which consists of a dendrite, soma, and axon. In a neuron, the dendrite performs the function of receiving input signals through electrical signals; the soma sums up the inputs received through the dendrite; and the axon output values are calculated by soma [9]. Figure 1 illustrates a structure of a biological and an artificial neuron. An AN has a limited number of inputs with weights related to them, and an activation function. The output of the AN is the result of the activation function applied to the weighted sum of inputs. ANs are connected with each other to form ANNs that have multiple layers, including an input layer, and output layer, and at least one more hidden layer [9]. Figure 2 depicts an example of a fully connected, multiple-layered ANN with n hidden layers. Each layer of an ANN consists of ANs, and each AN is connected to all the ANs in the next layer. Data move from the input layer through the hidden layers to the output layer. DL is able to handle colossal multimedia data, and DL techniques are able to perform highly accurate object awareness analysis from video and image data [10]. IoT-based application developers are interested in the benefits of DL, since modern IoT devices constantly generate multimedia data that must be analyzes in order to obtain useful information. Smart agriculture also utilizes many IoT multimedia devices and data generated from them to support smart services such as facility automation. Nonetheless, IoT-based data collection hardware is commonly inadequate to independently perform DL due to restricted energy and computational capacity [11]. Accordingly, several studies have been performed to support DL on IoT devices. Cloud computing is a common approach to address low performance issues in IoT devices. However, the continued generation of colossal volumes of data leads to network congestion and focused overhead of the central server in cloud environment [12]. To overcome this issue, fog nodes in FC process part or all of the data before sending it to the cloud. Thus, FC mechanisms considerably reduce data traffic and burden on the cloud. Keeping these advantages in mind, some studies have attempted to distribute DL overhead by applying FC mechanisms. [PERSON] et al. [13] suggested DeepEar with DeepX as novel acceleration schemes to assist dissimilar applications with DL on mobile systems on chip. Their experimental results showed that a mobile device with high performance can deal with a portion of DL processing. [PERSON] et al. [14] suggested a combination of DL and the Apache Spark application for analyzing and learning data at the same time in a cloud environment. Their mobile devices performed DL operations to analyze data while the  
Figure 1: Structures of biological and artificial neurons. (**a**) Biological neuron; (**b**) artificial neuron. Figure 2: Example of a fully connected, multiple-layered artificial neural network. Apache Spark learned data on the cloud simultaneously. In this platform with its two-layer structure, mobile devices were able to capably handle a portion of the work on the cloud. [PERSON] et al. [15] suggested iFogSim to model and simulate an IoT and FC environment. The scheme experimented with average latency and execution time for controlling a loop of intelligent surveillance systems in a cloud and FC environment. The results showed that system control was more competent in FC than cloud computing with regards to energy and execution time. [PERSON] et al. [16] suggested a food classification scheme utilizing DL and FC. The scheme reduced the response time and power consumption of DL applications by sharing works on the FC. [PERSON] et al. [17] suggested a new distributed learning model for sensor devices.