Overview of Edge Computing in the Agricultural Internet of Things: Key Technologies, Applications, Challenges

Abstract

The application of the Internet of Things in agricultural development usually occurs via a monitoring network that consists of a large number of sensor nodes, thus gradually transforming agriculture from a human-oriented and single-machine-centric production model to an information-and software-centric production model. Due to the large area coverage of agriculture and the variety of production objects, if all farmland perception information is gathered into the cloud server, the server will exert greater pressure on the network, which reduces the speed of response to event processing. This problem may be perfectly solved by the recent emergence of Edge computing, which can share the load of the cloud server and reduce the delay. Edge computing has prospects in agricultural applications, such as pest identification, safety traceability of agricultural products, unmanned agricultural machinery, agricultural technology promotion, and intelligent management. The application of the Agricultural Internet of Things integrates artificial intelligence, the Internet of Things, and blockchain and Virtual/Augmented Reality technologies. This paper primarily reviews the application of Edge computing in the Agricultural Internet of Things and investigates the combination of Edge computing and Artificial Intelligence, blockchain and Virtual/Augmented reality technology. The challenges of Edge computing task allocation, data processing, privacy protection and security, and service stability in agriculture are reviewed. The future development direction of Edge computing in the Agricultural Internet of Things is predicted.

A Low-Cost Platform for Environmental Smart Farming Monitoring System Based on IoT and UAVs

“…However, the current agricultural land is restricted by various factors such as land and climate patterns, population density, and rapid urbanization, which are constantly posing threats to the availability of arable land [5]. To illustrate, in 1991, the total arable area for food production was 19.5 million square miles (39.47% of the world's land area), which was reduced to approximately 18.6 million square miles (37.73% of the world's land area) in 2013 [6]. Thus, the gap between the demand and supply of food is becoming more significant and alarming as time progresses.…”

A Systematic Survey on the Role of Cloud, Fog, and Edge Computing Combination in Smart Agriculture

“…The research papers such as [ 10 , 43 , 44 , 45 , 46 , 47 ] are the latest review papers on smart agriculture/smart farming and IoT, and Ref. [ 35 ] is the latest review on Edge Computing in the agricultural Internet of Things. A significant number of publications and reviews published in 2020, in recent years, clearly show that researchers are very much interested in the field of smart agriculture where applying IoT and other new computing technologies such as Cloud, Fog, and Edge.…”

“…Mist computing uses microcomputers and microcontrollers to feed into Fog computing nodes [ 34 ]. Edge Computing is mainly contributing to agricultural applications such as pest identification, safety traceability of agricultural products, unmanned agricultural machinery, agricultural technology promotion, and intelligent management [ 35 ]. Moreover, Edge computing enables the evolution to 5G by bringing Cloud capabilities near to end-users [ 36 , 37 , 38 ].…”

“…However, as mentioned before, Fog Computing was first mentioned and defined in 2012 by Flavio Bonomi at CISCO. Some authors believe that Fog Computing is one of the classifications of Edge Computing [ 32 , 35 ]. For example, Ref.…”

Agriculture 4.0 and Smart Sensors. The Scientific Evolution of Digital Agriculture: Challenges and Opportunities

“…In highly technological scenarios, these concerns increase the efforts of researchers and practitioners in the search for better encryption and security techniques to reduce the risks of cyber-attacks and ensure data security and privacy [164]. According to Zhang et al (2020) [165] farmers are insecure about data security, as well as the physical requirements for equipment and effective tools for data protection and distributed systems for agricultural management. Hussein (2019) [164] reinforces the care with the computational structure necessary for cloud storage to be effective and secure.…”