# Deep Learning Entrusted to Fog Nodes (DLEFN) Based Smart Agriculture
## 3 Deep Learning in Smart Agriculture  
Current smart agriculture systems consist of various ICT devices such as a variety of sensors, high-definition camera devices like CCTVs, autonomous drones, and voice recognition equipment. Such devices generate colossal volumes of data that include unstructured multimedia data such as videos, images, and sounds as well as structured numerical values such as temperature, humidity, illumination, and air condition [1]. In smart agriculture, the collected data are helpful in making optimal decisions for facility automation, such as optimal temperature and humidity control, and automatic feed supply. Commonly, multimedia data have a larger size than structured numeric data. Moreover, complicated algorithms are required to obtain the appropriate information from large media data. Although generated multimedia data are compressed and encoded, audio data in MP3 format by CBR (constant bit rate) have a bit rate of 320 kbps, and HD video (720p, 60 fps) and FHD video (1080p, 60 fps) have bit rates of 3500 kbps and 5500 kbps respectively. In a modern IoT environment, such colossal multimedia data are flowing into a network continuously. Thus, sufficient resources like computing power and bandwidth are essential to execute DL tasks such as context awareness from video data in a real-time environment.  
In order to improve the QoL of farmers, many applications in smart agriculture utilize DL tasks for a variety of tasks, such as commercial value checking for agricultural products, condition management for domestic animals, population estimation, and security [2,3]. These tasks involve the analysis of multimedia data generated by many devices and the extraction of meaningful information to use as a basis for executing processes of applications. The level of convenience and benefit provided to users is determined by the accuracy of the extracted information. Accordingly, many studies have made efforts to improve the quality of the information obtained through DL processing, such as object recognition, context awareness, and character reading. Despite the steady improvement in accuracy among researched DL technologies with similar purposes, handling DL on IoT-based devices with limited energy and computing power remains an important challenge [11]. To overcome these limitations, a universal approach grafting DL technology onto a cloud service is shown in Figure 3. An application collects media data from devices which it can control, and requests analysis of the required information from a cloud server with enough computing power. A DL algorithm for application on the cloud extracts meaningful information from the received data. Subsequently, the information returned from the cloud is used to execute defined functions of the application. Devices transfer data to either a cloud or a gateway according to network infrastructure. If unable to connect to the cloud, devices transfer data to a nearby gateway that can forward it to the cloud.  
Nevertheless, in a cloud-based DL environment, there are two issues that require focus, i.e., (1) network congestion owing to huge data transfer and (2) intensive burden of cloud servers [19]. Even in the cases of intermediate gateways being used to forward data, they demand a very high bandwidth to correspond to all data gathered from related devices. Additionally, cloud servers with high performance also have limitations in terms of simultaneous execution of many DL tasks with huge data [20]. Namely, these two issues raise the possibility of response relay. However, it is worth noting that even a small delay can cause economic losses in smart farms dealing with fields of automation such as production, sales, growth, management, and security.