Introduction

The term “Internet of things (IoT)” was first used by Kevin Ashton, director of the Auto-ID Center at the Massachusetts Institute of Technology (MIT), in 1999. He predicted that IoT mounted on items with radio frequency identification (RFID) and sensors used in everyday life would be established in the future. IoT means technology and an environment that can exchange data in real time through Internet communication by sensors installed on different objects (Borgia 2014). IoT can be used for big data analytics, cloud computing, etc. in various industries (Baseca et al. 2019). To date, devices connected to the Internet have required human manipulation to send and receive information. However, IoT enables the exchange of information between objects by using Bluetooth, near-field communication (NFC), sensor data, and networks without human assistance (Gubbi et al. 2013). Due to these advantages, IoT technology is being applied in various industrial fields, such as cities, smart healthcare, homes and buildings, energy, transportation, waste management, monitoring, and agriculture (Borgia 2014; Perera et al. 2014; Zeinab and Elmustafa 2017).

According to the future food and agriculture report of the Food and Agriculture Organization (FAO) of the United Nations, the world population is expected to increase by approximately 10 billion by 2050, which means that more agricultural production is needed (FAO 2017). To address these problems, many researchers around the world are carrying out studies to increase agricultural productivity (Dhall and Agrawal 2018; Verdouw et al. 2019). The agricultural industry, with innovative ideas and technological advances such as sensor systems and wireless sensor networks, has been able to increase production and allocate resources more efficiently (Ray 2016). IoT contributes significantly to innovative, smart farming (Ande and Rojatkar 2017). Agriculture with IoT enables agricultural automation, thereby increasing agricultural production (Lee et al. 2013; Bu and Wang 2019). In addition, IoT in agriculture can be used to improve crop yields by eliminating waste, streamlining operations, and establishing a secure food supply chain (Huang 2016). The global IoT market is expected to reach 1256.1 billion dollars by 2025 from 690 billion dollars in 2019, with a CAGR of 10.53% for 2020–2025 (Mordor Intelligence 2019). IoT technology has resulted in a new paradigm for agriculture and has been applied to various agricultural processes, such as farm management (Köksal and Tekinerdogan 2019), farm monitoring (Muangprathub et al. 2019), livestock monitoring (Pan et al. 2016), irrigation control (Nawandar and Satpute 2019), greenhouse environmental control (Liao et al. 2017), autonomous agricultural machinery (Reid et al. 2016), and drones (Boursianis et al. 2020), thus contributing to agricultural automation. For example, farmers can integrate wireless sensors and mobile networks to monitor farming conditions in real time and easily control farms (Abd El-kader and El-Basioni 2013; Işık et al. 2017). In addition, farmers can collect meaningful data through IoT technology, which is used to generate yield maps that enable the production of low-cost, high-quality crops through precision agriculture (Vasisht et al. 2017; Ravindra 2018).

During the last few decades, IoT technologies have been applied to many specific agricultural processes by using various sensors and network technologies (Xu et al. 2014; Patil and Kale 2016). Because of the advance of sensor and network technology, there are various types of networks that users can select. Each sensor and network system has advantages and disadvantages, and farmers can implement high-efficiency, low-cost IoT-based agriculture by selecting the proper sensors and networks in consideration of their farm conditions and working environments. In addition, to date, IoT has been widely used as a single solution, such as monitoring and control of green houses, rather than a process that manages the entire agriculture including the management of crops and agricultural machinery; thus, there is a need to expand IoT technology to a wide range of agriculture processes (Talavera et al. 2017; Tzounis et al. 2017). Therefore, it is necessary to review existing applications and limitations for the extended application of IoT technology in agriculture.

The aim of this study is to provide useful information for developing and applying IoT platforms suitable for Korean agricultural environments. The specific objectives are as follows: (1) collecting and categorizing the various cases in which existing IoT is applied to agriculture; (2) summarizing sensors, networks, and controllers used in each application; (3) analyzing the various wireless communication technologies used in IoT-based agriculture; and (4) discussing some limitations and prospects.

Internet of Things Technologies

The Internet originally was controlled only by the user. Machine-to-machine (M2M) technology based on wired and wireless Internet with the development of intelligent communication technology was then developed (Adame et al. 2014). M2M is a passive concept that collects information by installing sensors and networks, functions on all objects, exchanges data through communication functions, and finally provides information to the user. IoT technology evolved in M2M means technology that communicates among various objects without human intervention and then provides services. The functions of IoT are widely known as data collection and processing, planning and decision-making, and prescriptions and services (Zhou et al. 2012; Zhang et al. 2017). Fig. 1 showed IoT in agriculture including a series of processes that collect data on items such as crops, livestock, agricultural machinery, and farms; build a database based on the collected data; make an appropriate prescription through analysis of key data from experts; and deliver prescription to consumers using text message.

Fig. 1
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Procedures of the Internet of Things (IoT) in agriculture

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IoT architecture can be divided into a perception layer for recognition, a network layer for data transmission and reception, and an application layer for agricultural applications, as shown in Fig. 2 (Shi et al. 2019). Generally, in the perception layer, sensor nodes are installed in various areas, such as farms, crops, livestock, greenhouses, and agricultural machinery, to sense different parameters in real time. The measured data are transmitted to the local gateway, and in the network layer, the local gateway receives the data and uploads the sensor data to the cloud using various wireless sensor networks (WSNs). This system can be applied to various processes and applications of agriculture, including management, monitoring, control, and unmanned machinery (Moon et al. 2018; Wang et al. 2020).

Fig. 2
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Architecture of IoT including the application layer, network layer, and perception layer

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Perception Layer

The key task of the perception layer in IoT is to recognize the physical properties of the target (e.g., crop, farm, livestock, and machinery). The perception layer consists of various sensors, agricultural machinery, WSN, actuator, controller, RFID, etc. (Ye et al. 2013). In particular, various sensors have been used in agriculture since there are various types of variables to be considered, such as atmosphere, soil, outdoor area (farm field), indoor area (greenhouse), and water. The main sensors used are temperature, moisture, humidity, pressure, pH, ultrasonication, and accelerometer (Muhammad et al. 2016; Pal et al. 2017; Suárez et al. 2018). There are a variety of IoT sensing technologies, such as WSNs, NFC, RFID, image processing, and global positioning systems (GPSs). Considering flexibility and autonomous concepts in agriculture, WSNs have been applied to many practical applications to provide high-resolution real-time sensing information about the condition of the physical world (Liao et al. 2012). WSN in IoT-based agriculture means a group of geographically distributed sensor nodes that collect and monitor data on tasks. Fig. 3 shows a data flow of the connection between various sensor nodes installed on farmland, gateway sensor nodes that integrate each sensor node, and users. WSNs also automatically route data to a decision center as digital signals. The information collected through the sensors is simply processed through the embedded device and uploaded to the upper layer through the network layer for database construction and big data analysis (Shi et al. 2019).

Fig. 3
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Wireless sensor network system

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Network Layer

The network layer processes the received real-time data from the perception layer and transports the data remotely to the application layer using a telecommunications network, local area network (LAN), and the Internet (Xiaojun et al. 2015; Foughali et al. 2018). The network layer has a microprocessor or microcontroller that uses a communication module to send data collected at the perception layer to the application layer through the transporting media (Narendran et al. 2017). In addition, there are several media to transport data, such as 3G/4G/5G, Wi-Fi, Bluetooth, IEEE-802.11, NFC, global system for mobile communications (GSM), ZigBee, and general packet radio service (GPRS). As such, the network layer transmits not only various types of data collected in the perception layer to the application layer but also control commands of the application layer to the perception layer so that related devices in the perception layer can be activated (Shi et al. 2019).

Application Layer

The application layer is a smart processing device that applies data processed at the network layer and is the highest level of architecture in the IoT layer (Foughali et al. 2018). This layer includes various intelligent systems, such as managing data across agriculture; monitoring and controlling plants, animals, machinery, and farms; early warning and diagnosing infections of diseases and infestations of pests; and running autonomous machinery. In addition, the application layer mainly processes and analyzes data, evaluates the system, predicts future trends in the system, makes decisions based on past data sets, and sends prescriptions to end-users (Xiaojun et al. 2015). Thus, it is possible to minimize the damage by appropriately addressing the problems that may occur in agriculture early and to maximize production efficiency, thereby contributing to the improvement of farmers’ income.

Applications of the Internet of Things in Agriculture

Recent advances in wireless sensor networks have made it easier to measure a variety of data types (Glaroudis et al. 2020). These advances have made it possible for IoT to address various agricultural problems and enable sustainable and efficient farming (Antony et al. 2020). In agriculture, IoT is used for a wide range of activities, and applications can be broadly divided into four categories as follows: (a) management systems, (b) monitoring systems, (c) control systems, and (d) unmanned machinery, as shown in Fig. 4 (Aqeel-ur-Rehman et al. 2014; Talavera et al. 2017).

Fig. 4
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Application of IoT in agriculture, including management systems, monitoring systems, control systems, and unmanned machinery

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Management System

Until recently, farmers had been lacking the tools to manage their farms based on a cost, benefit, and profitability analysis. However, due to the development of sensor and communication technology, it is easier to collect and store data for agriculture, and now, it is important to comprehensively manage and utilize various types of collected data (Diène et al. 2020). In agriculture, the management system is applied to various factors, such as farm, energy, water, and agricultural machinery, and the following is a representative example. Table 1 shows the sensors and networks of the smart management system used in the previous study.

Table 1 IoT-based smart management systems applied in agriculture
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Agricultural Machinery

AGCO, a leading global agricultural machinery company, proposed the “connected farm service,” which is a management system for farms and agricultural machinery (Chaudhary et al. 2015). In particular, the agricultural machinery service management system was developed by installing a remote monitoring terminal on large-scale intelligent agricultural machinery and developing the related mobile application software and server software (Zhang et al. 2017). IoT technology was applied to conventional agricultural production to provide useful information such as on the management of agricultural machinery operations, real-time equipment management of agricultural machinery, and agricultural machinery operation and control needs (Li et al. 2018). With these systems, agricultural productivity can be improved as field conditions, and operating conditions of agricultural machinery are monitored remotely.

Farm

Farm management information systems (FMISs) based on IoT have been proposed to assist farmers in making effective decisions by managing all measured data from installed sensors on farms (Paraforos et al. 2016; Köksal and Tekinerdogan 2019). This system was used to provide data collected on items such as machines, seeds, pesticides, and fertilizers that are used on farms and financial analysis results to farmers based on big data analysis. Ye et al. (2013) proposed a precision agricultural management system (PAMS) based on IoT and WebGIS. PAMS was designed for the management of large agricultural production farms. This system was developed by using advanced technologies such as IoT technology and WebGIS to provide functions such as data collection, data retrieval, data analysis, production monitoring and management, remote operation of production processes, and support for production decisions. Agricultural management information systems (AMIS) can be applied broadly across entire cycles of agriculture to increase agricultural productivity and help farmers make effective choices (Yan-e 2011).

Water

As water shortages have increased rapidly, the multi-intelligent control system (MICS) was introduced for the management of water resources in the agricultural sector (Hadipour et al. 2020). The proposed system is based on IoT and has been used for the management of all water resources by monitoring and controlling water consumption and water levels in reservoirs. The system has provided a satisfactory solution for water management in the agricultural sector, and it has been reported that this system can save up to 60% of water.

Monitoring System

In agriculture, previous studies related to monitoring have been classified into monitoring diseases, fields, greenhouses, livestock, pests, and soil. Table 2 shows the sensors and networks used in the previous studies.

Table 2 IoT-based smart monitoring systems applied in agriculture
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Disease

An IoT-based cognitive monitoring system for early plant disease forecasting was developed (Khattab et al. 2019). The monitoring system was used not only to provide environmental monitoring data to maintain an optimal crop cultivation environment but also to predict conditions leading to an epidemic outbreak using environmental sensor data. This system was equipped with artificial intelligence and prediction algorithms that emulate the decision-making capabilities of human experts and was designed to issue warning messages to users. Zhao et al. (2020) proposed an effective automated system deployed in the agricultural IoT using a multicontext fusion network (MCFN) to recognize crop disease in the wild. The system was inspired by the usefulness of agricultural IoT, and the deep learning system, the MCFN, was developed for real crop direct recognition based on the IoT. The proposed MCFN achieved an excellent identification accuracy of 97.5% in wild crop disease recognition.

Field

In agriculture, field monitoring can be used to manage crop growing environments to improve crop quality and yield. Field monitoring is a typical example of applying IoT to agriculture through low-cost sensors and networks. An intelligent agricultural field monitoring system that monitors soil humidity and temperature was proposed (AshifuddinMondal and Rehena 2018). Data collected through this system are saved in the cloud for future data analysis, which can be used for field management. The application of field monitoring and agricultural automation has been proposed based on a framework containing the knowledge management (KM) base and monitoring module (Mohanraj et al. 2016). This system was used to enable efficient use of water resources and labor cost savings.

Greenhouse

In greenhouses, environmental conditions such as temperature and humidity are important factors affecting plant quality and productivity (Wang et al. 2019). Continuous monitoring of these environmental variables provides farmers with useful information to maximize crop productivity (Akkaş and Sokullu 2017). For example, conventional methods to monitor environmental factors of greenhouses and the growth of Phalaenopsis have low resolution, require high levels of labor intensity, are time-consuming, and have lack of automation. To address these problems, an IoT-based system to monitor the environmental factors of an orchid greenhouse and the growth status of Phalaenopsis was proposed (Liao et al. 2017). The proposed system consists of an IoT-based environmental monitoring system and an IoT-based wireless imaging platform, and this system can measure the environmental factors in an orchid greenhouse and the growth of orchid leaves in real time.

Livestock

In agriculture, monitoring systems have been used to collect data on various types of livestock, such as cows (Guerra 2017), and poultry (Li et al. 2015; Pan et al. 2016; Astill et al. 2020). Moocall, a system for monitoring the movement of pregnant cows using motion sensors, has been developed (Guerra 2017). This system was designed to send SMS text to farmers two hours before a cow is calving, and it was used to reduce the calf mortality rate. Moocall reported that the accuracy of the system is over 95% and that the mortality rate at calving was reduced by 7%. Precision livestock farming (PLF) is a system for the overall management, such as monitoring, data analysis and decision-making, and control and intervention, of various livestock (Wolfert et al. 2017). PLF systems can be used to make more efficient decisions by reducing the need for manual observations and human decision-making and can be applied to facilitate the automation of these processes by significantly reducing the time and effort required to manage livestock (Halachmi and Guarino 2016). In addition, it has been used to manage livestock by monitoring in real time, which can provide farmers with a platform to manage multiple animals more efficiently (Smith et al. 2015). The environment of a poultry house is an important factor for production that can be monitored and optimized. A typical poultry environment includes temperature, air velocity, ventilation rate, litter quality, humidity, and gas concentrations, including carbon dioxide and ammonia (Dallimore 2017). An IoT-based smart poultry management system was proposed for farm process automation and decision-making using various sensor systems (Astill et al. 2020).

Pest

An autonomous early warning system to prevent the recurrence of pests such as the massive Oriental fruit fly (Bactrocera dorsalis (Hendel)) was proposed (Liao et al. 2012). This system was used to reduce farmers’ excessive dependence on chemical pesticides. In addition, it contained two wireless communication protocols, ZigBee and GSM, and three key components, wireless monitoring nodes (WMN), a remote-sensing information gateway (RSIG), and a host control platform (HCP). The proposed study offered a real-time warning system to inform system administrators and government officials about the occurrence of crucial events via the GSM platform so that farms and future food security could be protected.

Soil

Since the soil environment directly affects the growth of crops, it is very important to maintain a proper soil environment for crops. Monitoring the soil environment is used to change existing farming practices and maximize agricultural production (Na et al. 2016). An IoT-based, smart soil monitoring system for agricultural production was developed (Ananthi et al. 2017). In this system, various sensors, such as pH sensors, temperature sensors, and humidity sensors, were used to monitor the soil, and the collected data on the soil environment were transmitted to the user using mobile applications. This system can be used for making decisions related to irrigation systems and pesticide spraying. Fertilizers are used to replenish nutrients in soil that lack nutrients. This lack of nutrients affects the yield and quality of the crop, and the yield can be increased by using an appropriate amount of fertilizer. Moreover, the use of excessive fertilizer causes excessive spending by farmers. Farmers lack information about the soil environment, and it is difficult to know the appropriate amount of fertilizer required. To address this problem, IoT-based fertilizer systems have been introduced in some studies. These systems include monitoring soil nutrients, analyzing the required amount of fertilizer, and spraying fertilizer using a control system.

Control System

IoT is used in agriculture to control resources such as the environment of farms and greenhouses, irrigation, and water quality (Giri et al. 2016). In particular, control systems in agriculture have been used to maintain optimal growing conditions so that high-quality crops on farms can grow well. Table 3 shows information on fields, sensors, controllers, and networks where IoT-based control systems were applied to agriculture.

Table 3 IoT-based smart control systems applied in agriculture
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Farm

A control system incorporating IoT technology in crop production has been developed (Marković et al. 2015). On the farm, the control system was used to collect and monitor data using autonomous sensor devices and control the actuators. The most commonly deficient nutrients in farm soil are nitrogen, phosphorus, and potassium, or N, P, and K, respectively (Warpe and Pippal 2016). IoT technology-based systems with NPK sensors using light-dependent resistors (LDRs) and light emitting diodes (LEDs) have been developed (Lavanya et al. 2018). The system provides guidance on the amount of fertilizer required for farmers at regular intervals by monitoring and analyzing nutrients present in the soil.

Greenhouse

The greenhouse environment greatly influences the growth environment of crops, and maintaining an appropriate greenhouse environment can increase crop quality and yield. Liao et al. (2017) monitored the environmental factors of an IoT-based greenhouse and analyzed the temperature and relative humidity showing the highest growth rate, and they developed a control system to maintain the environment of the greenhouse at the optimal temperature and humidity. Park et al. (2019) developed a wireless sensor node that complies with the communication interface standard for effective communication between the sensor and the controller in the green house and evaluated the data transmission speed according to the distance. The wireless sensor node and controller are designed to communicate wirelessly using Bluetooth, and the data rate was 100% up to 25 m between the sensor node and the controller. They reported that further studies on long-distance wireless communication methods such as LoRa are needed to expand the communication range between the sensor node and the controller.

Irrigation

IoT-based irrigation systems are used to efficiently utilize water resources in terms of precision agriculture (Goap et al. 2018). To supply the optimum water required by the soil, numerous studies have been conducted on IoT-based irrigation systems (Muhammad et al. 2016). An autonomous sprinkler system was developed that operates based on the real-time water content of the soil (Chowdhury and Raghukiran 2017). This system was used to maintain a certain level of moisture by controlling the sprinkler based on the moisture content data of the soil that was measured by an IoT real-time sensor without a user. In addition, IoT functions were applied to the autonomous sprinklers to prevent excessive water use and plant death by controlling the sprinklers remotely from anywhere in the world based on weather forecasting.

Water Quality

IoT-based smart solutions that control water quality based on pH to treat municipal wastewater and its reuse for agricultural purposes have been developed (Khatri et al. 2018). The proposed solution made it possible to maintain water quality within the prescribed standards so that municipal wastewater could be recycled and used for agricultural purposes after reprocessing.

Unmanned Machinery

Autonomous Machinery

Autonomous agricultural machinery has been under development since the concept of precision agriculture emerged in the 1980s using various advanced sensor systems (BigAg 2018). Autonomous agricultural machinery is being developed using many advanced sensors and systems. Agricultural machinery companies that are global leaders have developed a tractor with autoguidance technology using GPS to improve working efficiency and reduce labor requirements (Zhang et al. 2018). Tractor companies such as John Deere and Case IH are conducting ongoing research on autonomous tractors (Guerra 2017). Tractors with automatic steering have several advantages, such as repeatable path tracking, reducing overlap and facilitating operations under low visibility conditions, taking complete control over the quality of farming operations (Lipiński et al. 2016; Reid et al. 2016). With the recent development of wireless communication technology, IoT has been applied to agricultural machinery, and the development of fully autonomous tractors has been accelerated. Multiple agricultural machines are connected to each other by exchanging data through communication. For example, multiple tractors can be connected and communicate to copy the steering angle and speed of the main tractor for simultaneous operation (Guerra 2017). John Deere, for example, has developed integrated systems that help manage their work remotely, such as the Machine Sync system, AutoTrac Vision, and AutoTrac RowSense system. Machine Sync systems allow tractors to communicate directly with combines and other systems to increase the efficiency and accuracy of crop harvesting. AutoTrac Vision enables equipment to follow actual planted crop rows, reducing crop damage and improving work efficiency. The AutoTrac RowSense system is used to avoid crops and ensure full coverage for fertilizer applications and other applications.

Unmanned Aerial Vehicle

IoT-based unmanned aerial vehicles (UAVs) have contributed to transforming agriculture from traditional cultivation practices to a new level of intelligence in precision agriculture (Boursianis et al. 2020). Since UAVs can be applied to agriculture for various purposes, such as irrigation, fertilization, pesticide use, weed management, plant growth monitoring, crop disease management, and field-level phenotyping, their utilization is expected to increase continuously (Mukherjee et al. 2020). An IoT-based, low-altitude remote-sensing technology for UAVs has been widely used for environmental monitoring of farmland fields, and it has been used to analyze pest and disease outbreaks in crops based on captured images of farmlands using spectral cameras (Gao et al. 2020). In addition, thermal or heat-seeking cameras installed on UAVs (or drones) can be used to monitor the thermal properties of plants and crops; detect the presence of harmful wild animals on farmlands; and monitor plants, diseases, and water scarcity (Saha et al. 2018). UAVs are expected to provide advanced technology to the agricultural industry through strategies and plans based on real-time data collecting and processing (Ravindra 2018). However, despite these advantages, there are still limitations to be improved, such as power source problems (i.e., short operating time), communication efficiency, and flight restrictions depending on the climate environment. In addition, it is still difficult to develop independent agricultural UAV technology in Korea, and most of them are dependent on imported components. By solving these problems, IoT-based UAVs are expected to transform conventional agriculture more innovative and efficient in the future.

Wireless Communication Technologies Used in Agriculture

In recent years, wireless transmission technology has developed rapidly. There are various types of communication technologies, such as Wi-Fi, LoRaWAN, mobile communication (e.g., 2G, 3G, and 4G), ZigBee, and Bluetooth, for applying IoT to agriculture (Anastasi et al. 2009; Frenzel 2012; Gutiérrez et al. 2014; Ojha et al. 2015; Jayaraman et al. 2016; Fernández-Garcia and Gil 2017; Jawad et al. 2017; Vaquerizo-Hdez et al. 2017). These communication technologies enable the automation of the entire cycle of agriculture, thus facilitating highly convenient and highly efficient agriculture. ZigBee and Bluetooth consume minimal power and cost little, so they are widely used in agricultural IoT. In particular, ZigBee is an integrated, standard short-range wireless communication technology that consumes minimal power, costs little, and is versatile, and among various communication technologies, ZigBee is widely used for IoT implementation in agriculture (Farooq et al. 2019).

Various types of wireless networks have different characteristics, such as frequency, power consumption, communication ranges, and data transfer limits. The transmission range of the data is an important indicator for selecting which communication technology to apply to a particular type of agriculture, which is related to cost (Ray 2017). Therefore, farmers should select a communication technology with an appropriate data transmission range according to the required agricultural characteristics. Table 4 shows the characteristics of different types of wireless networks. Most WSNs used in agriculture need to cost little, use minimal power, and have low data rates (Kalaivani et al. 2011; Kang and Chen 2020). To apply IoT to agriculture, wireless networks can be compared and selected (Sadowski and Spachos 2020).

Table 4 Characteristics of the wireless technologies used in agriculture (Farooq et al. 2019)
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Potential IoT Value in Agriculture

Recently, a substantial challenge has been feeding the global population, and the FAO reported that approximately 70% more food will be needed in 2050 than in 2006 for the growing global population. IoT has been documented as a revolutionary concept in farming to meet the upcoming food crisis (Meola 2020). There are various studies about using IoT technology for food safety. Libelium applied 3G technology to address environmental issues and improve environmental management in vineyards in Northwest Spain (Martinez 2014). The results revealed that phytosanitary treatments (i.e., fungicides and fertilizers) were reduced by more than 20% and growth production increased by approximately 15%. An integrated control strategy (ICS) method for irrigating romaine lettuce in a greenhouse was implemented (Hong and Hsieh 2016). This process resulted in the ICS decreasing water and electricity use by 90%. An automated irrigation system (AIS) using the WSN and GPRS modules for optimum water use in crops was developed (Gutiérrez et al. 2014). It was found that in comparison to a traditional irrigation system, AIS decreased water used by 90%.

The global market for agricultural IoT devices has been remarkable. According to Business Insider’s premium research service, in 2015, the global shipment of IoT devices was only 30 million USD; however, in 2020, the global shipment of IoT devices is projected be approximately 75 million USD globally for agricultural purposes, which is almost 20% of the annual growth rate. The potential value of IoT is expected to increase significantly, to as high as 15 trillion USD in 2022 compared to 1 trillion USD in 2013 without increased revenues (Tzounis et al. 2017). As the technological development of sensors and networks accelerates, the role of the IoT in agriculture is expected to increase at an unprecedented rate.

Discussion

Recently, many studies have been conducted to apply IoT technology to agriculture. Most of the studies have been conducted on smart monitoring and smart control with IoT. In particular, there was a high concentration of soil, farm, and greenhouse environmental monitoring and irrigation and fertilizer control. Additionally, IoT and cloud computing-based systems have been used to provide a reliable architecture for farmers to provide timely, on-the-spot information via WSN (Mohanraj et al. 2016). Even though IoT is currently being used for agriculture, some limitations still need to be improved, and future prospects in agricultural IoT are discussed in the following sections.

Limitations

Many studies have been conducted to apply IoT technology to various aspects of agriculture, such as smart management, monitoring, and control. However, IoT technology has been applied to specific agricultural operations but not entire agricultural processes. When IoT is integrated into entire agricultural processes, efficiency can be maximized. In particular, the most difficult application of IoT technology is autonomous agricultural machinery. The conditions under which agricultural machinery operates are atypical environments with numerous variable conditions. Therefore, it is not easy to develop and commercialize autonomous tractors that can be operated without humans due to safety considerations. Nevertheless, many studies on autonomous tractors have been conducted in consideration of agricultural feminization, aging farmers, and food productivity. According to John Deere, techniques for fully autonomous tractors have already been developed. However, autonomous agricultural machinery has not yet been commercialized due to the risk of accidents when the vehicle is unmanned. To use autonomous agricultural machinery in a field, it is necessary to prepare for agricultural machinery safety by combining IoT technologies. Additionally, most countries that apply IoT technologies in agriculture have a large scale of farmland. On these large farmlands, it is relatively efficient to use self-driving autonomous machinery. However, since farm fields are relatively small in Korea, frequent turning operations are required, so it is difficult to apply autonomous agricultural machinery. In addition, due to frequent turning operations, there are cases where work is not properly performed at the boundaries of the farmlands, which causes a loss to the yields. Therefore, considering such an agricultural environment in Korea, more precise sensing and control technology is required to utilize IoT-based autonomous agricultural machinery, and accurate field mapping technology can be used to apply autonomous agricultural machinery. RTK-GPS, which is currently used for autonomous agricultural machinery, has a high performance with an error of about 2 cm. However, the cost of the RTK-GPS is too expensive, and the probability of error is high depending on the weather or terrain. Therefore, in order to spread the autonomous agricultural machinery with a low cost, it is necessary to improve the GPS precision by using a sensor fusion method such as a differential global positioning system (DGPS) module + camera module, and precision GPS infrastructure for agriculture must be developed.

In addition, to fully trust and adopt IoT in agriculture, it is important to analyze and prepare for potential threats and various security requirements. First, in most agricultural areas except for greenhouse, IoT devices are used in open environments, so they are directly exposed to harsh environments. Under these conditions, the network environment is affected by various external environmental conditions, such as rain, high temperatures, humidity, and strong winds, which can decrease performance. Therefore, a physical safety device for IoT hardware suitable for external environmental factors is required. Second, IoT-based agriculture should be protected from various risks, such as hacking of collected agricultural data, farm information and host properties, and disruptions of the network and communication. In particular, since IoT uses a number of sensor nodes in a distributed manner, a single security protocol is not sufficient, and it is important to prepare for information leakage. Third, IoT in agriculture requires a large amount of data processing, so multiple sensor nodes are used. Many gateways and protocols are required to support these IoT devices. To manage these complex systems, network applications must be reliable and scalable.

Prospects

Recently, advanced cellular and wireless communication technologies such as 5G have been continuously evolving, enabling minimal power consumption and wide communication. These changes lower infrastructure costs for IoT construction and increase the utilization rate of IoT in agriculture. To date, agriculture in various countries, including Korea, has been carried out separately for each specific agricultural process, such as sowing, managing crop growth, harvesting, storing, and distributing. However, in the future, IoT technology will enable the entire agricultural cycle to be integrated and managed into a more efficient form. For example, IoT can integrate each specific agriculture process, such as monitoring the agricultural environment, controlling an environment suitable for the growth of crops (including fertilizers and pesticides), utilizing unmanned agricultural machinery, and reducing costs through data management and analysis of agriculture, and IoT can be used to enable overall management. In particular, such a system may be more efficient for large-scale farming than small-scale farming, and IoT will be an essential system in large-scale agriculture. This will require further research into technologies that can integrate IoT technologies into the entire agricultural process.

The most needed technology development for the IoT of agriculture is autonomous agricultural machinery. Agricultural machinery is used in all agricultural operations from sowing to harvesting. However, to date, IoT has focused on areas such as smart farms and field monitoring. Innovations in the field of agricultural machinery will allow remote control of these vehicles in the near future, which can greatly increase productivity for growers operating on large-scale farms. For example, an autonomous agricultural machine capable of accurate control can increase the efficiency of farming by performing farming during the day as well as evening. To date, autonomous driving technology has been one of the IoT technologies that has been the most difficult technology to apply in agriculture. Recently, Korea commercialized 5G technology, and some Korean agricultural machinery companies are carrying out research on autonomous tractors using 5G technology. Therefore, it is expected that self-driving tractors based on 5G and IoT technologies can be commercialized in Korea. UAVs are currently used for some agricultural processes, such as sowing, applying pesticides and fertilizers, and water spraying, but it is expected that their usefulness will increase further through cooperation with other agricultural machinery, such as tractors based on IoT.

According to the 4th Industrial Revolution, various technologies are converging to create higher technological value. IoT-based deep learning technology has been applied to various agricultural processes. For example, based on weather data collected from an IoT system in agriculture, weather changes can be predicted in advance, which is an effective way to plan and control sustainable agricultural production (Jin et al. 2020). It is expected that the automation rate of agriculture will be higher in the future through the fusion of various advanced technologies, so farmers need to understand advanced technologies and adopt suitable systems for the implementation of high-efficiency agriculture.

Conclusions

Recently, IoT has been actively applied to various agricultural technology sectors. In this review, we present a comprehensive review of the application of IoT for agricultural automation. First, a brief review of IoT architecture, such as the perception layer, network layer, and application layer, was conducted. Second, cases of IoT being applied in agriculture were classified and analyzed. As a result, IoT-based agriculture was divided into 4 groups: management systems, monitoring systems, control systems, and unmanned machinery. In particular, IoT in agriculture is widely used for monitoring soil, livestock, and greenhouses and controlling irrigation systems and the environmental conditions of farms and greenhouses. Then, the characteristics of communication technologies such as Wi-Fi, LoRaWAN, mobile communication (e.g., 2G, 3G, and 4G), ZigBee, and Bluetooth, which are most used in IoT-based agriculture, were analyzed. A farmer can realize the high efficiency and low cost of agricultural IoT by selecting a sensor and network based on the characteristics such as transmission range, power consumption, and cost of each network considering the operating environment of the IoT. In addition, an IoT device should be protected when in harsh outdoor agricultural environments, and stable network and data security should be ensured.

A review of the results of previous literature has led us to the following results: In agriculture, IoT is expected to address a variety of existing problems and enable increased quality and production. In addition, IoT can contribute to increasing farm income by reducing labor and input resources. However, as mentioned in the limitations section, a technology that integrates and applies IoT technology to the management of all agriculture is needed. Importantly, the local network should avoid collisions with other networks. In addition, currently, the application of IoT to autonomous agricultural machinery is insufficient, and the integration of IoT technology is necessary for the development and commercialization of autonomous agricultural machinery. In addition, for the commercialization of autonomous agricultural machinery, it is necessary to improve the precision of GPS. In addition, more precise GPS and control technology based on IoT must be secured to commercialize autonomous agricultural machines applicable to the Korean agricultural environment, which is small.