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REVIEW
A Review of the Applications of the Internet of Things (IoT)
for Agricultural Automation
Wan-Soo Kim
1
&Won-Suk Lee
2
&Yong-Joo Kim
1,3
Received: 31 July 2020 /Revised: 15 November 2020 /Accepted: 17 November 2020
#The Korean Society for Agricultural Machinery 2020
Abstract
Purpose The Internet of Things (IoT) is a network of devices for communicating machine to machine (M2M) based on wired and
wireless Internet. IoT in agriculture is a revolutionary technology that can be applied to agricultural production year-round. The
aim of this study is to summarize cases of IoT being applied to agricultural automation in the agricultural sector and to discuss the
limitations and prospects for expanding the application of IoT technology in Korea.
Methods The application of IoT in agriculture was classified and analyzed based on previous data, and the sensors and com-
munication technologies used were compared. Based on the analysis results, the limitations of and prospects for IoT in agriculture
were discussed.
Results IoT was widely used in agriculture, such as management systems, monitoring systems, control systems, and unmanned
machinery. In addition, the various wireless communication technologies used in agriculture, such as Wi-Fi, long-range wide
area network (LoRaWAN), mobile communication (e.g., 2G, 3G, and 4G), ZigBee, and Bluetooth, were also used in IoT-based
agriculture.
Conclusion With the development of various communication technologies, such as 5G, it is expected that faster and broader IoT
technologies will be applied to various agricultural processes in the future. IoT-based agriculture equipped with a communication
system suitable for each agricultural environment can contribute to agricultural automation by increasing crop quality and
production and reducing labor.
Keywords Agricultural automation .Internet of thing .IoT applications .Wireless sensor network
Nomenclature
DGPS Differential global positioning system
GPRS General packet radio service
GPS Global positioning system
GSM Global system for mobile communications
IoT Internet of things
LoRa Long range
LoRaWAN Long-range wide area network
M2M Machine to machine
NFC Near-field communication
RFID Radio frequency identification
WSN Wireless sensor network
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
*Won-Suk Lee
wslee@ufl.edu
1
Department of Biosystems Machinery Engineering, Chungnam
National University, 99, Daehak-ro, Yuseong-gu, Daejeon 34134,
Republic of Korea
2
Department of Agricultural and Biological Engineering, University
of Florida, 1741 Museum Rd, Gainesville, FL 32611, USA
3
Department of Smart Agriculture Systems, Chungnam National
University, 99, Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of
Korea
Journal of Biosystems Engineering
https://doi.org/10.1007/s42853-020-00078-3
Online ISSN 2234-1862
Print ISSN 1738-1266
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 manage-
ment, monitoring, and agriculture (Borgia 2014;Pereraetal.
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 ap-
proximately 10 billion by 2050, which means that more agri-
cultural 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 indus-
try, with innovative ideas and technological advances such as
sensor systems and wirelesssensor networks, has beenable to
increase production and allocate resources more efficiently
(Ray 2016). IoT contributes significantly to innovative, smart
farming (Ande and Rojatkar 2017). Agriculture with IoT en-
ables 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 elim-
inating waste, streamlining operations, and establishing a se-
cure 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), green-
house environmental control (Liao et al. 2017), autonomous
agricultural machinery (Reid et al. 2016), and drones
(Boursianis et al. 2020), thus contributing to agricultural au-
tomation. 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;Işıketal.2017). In addition, farmers can collect mean-
ingful 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;Ravindra2018).
During the last few decades, IoT technologies have been
applied to many specific agricultural processes by using var-
ious sensors and network technologies (Xu et al. 2014; Patil
and Kale 2016). Because of the advance of sensor and net-
work 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 moni-
toring 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 ex-
pand 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 fol-
lows: (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 technolo-
gies 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 commu-
nication 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, ex-
changes 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 ser-
vices. The functions of IoT are widely known as data collec-
tion and processing, planning and decision-making, and pre-
scriptions and services (Zhou et al. 2012; Zhang et al. 2017).
Fig. 1showed IoT in agriculture including a series of process-
es that collect data on items such as crops, livestock, agricul-
tural machinery, and farms; build a database based on the
collected data; make an appropriate prescription through anal-
ysis of key data from experts; and deliver prescription to con-
sumers using text message.
IoT architecture can be divided into a perception layer for
recognition, a network layer for data transmission and recep-
tion, 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 machin-
ery, 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
J. Biosyst. Eng.
sensor data to the cloud using various wireless sensor net-
works (WSNs). This system can be applied to various process-
es and applications of agriculture, including management,
monitoring, control, and unmanned machinery (Moon et al.
2018;Wangetal.2020).
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 sen-
sors, 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 vari-
ables 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, im-
age processing, and global positioning systems (GPSs).
Considering flexibility and autonomous concepts in agricul-
ture, 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 dis-
tributed sensor nodes that collect and monitor data on tasks.
Fig. 3shows 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
Fig. 2 Architecture of IoT
including the application layer,
network layer, and perception
layer
Fig. 1 Procedures of the Internet of Things (IoT) in agriculture
J. Biosyst. Eng.
automatically route data to a decision center as digital signals.
The information collected through the sensors is simply proc-
essed through the embedded device and uploaded to the upper
layer through the network layer for database construction and
bigdataanalysis(Shietal.2019).
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 sys-
tem for mobile communications (GSM), ZigBee, and general
packet radio service (GPRS). As such, the network layer trans-
mits 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 infec-
tions of diseases and infestations of pests; and running auton-
omous 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 appro-
priately addressing the problems that may occur in agriculture
early and to maximize production efficiency, thereby contrib-
uting 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 ad-
dress 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) manage-
ment systems, (b) monitoring systems, (c) control systems,
and (d) unmanned machinery, as shown in Fig. 4(Aqeel-ur-
Rehman et al. 2014;Talaveraetal.2017).
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 communica-
tion technology, it is easier to collect and store data for agri-
culture, 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 machin-
ery, and the following is a representative example. Table 1
shows the sensors and networks of the smart management
system used in the previous study.
Agricultural Machinery
AGCO, a leading global agricultural machinery company,
proposed the “connected farm service,”which is a
Fig. 3 Wireless sensor network
system
J. Biosyst. Eng.
management system for farms and agricultural machinery
(Chaudhary et al. 2015). In particular, the agricultural machin-
ery service management system was developed by installing a
remote monitoring terminal on large-scale intelligent agricul-
tural machinery and developing the related mobile application
software and server software (Zhang et al. 2017). IoT technol-
ogy was applied to conventional agricultural production to
provide useful information such as on the management of
agricultural machinery operations, real-time equipment man-
agement of agricultural machinery, and agricultural machin-
ery 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 de-
cisions 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 preci-
sion agricultural management system (PAMS) based on IoT
and WebGIS. PAMS was designed for the management of
large agricultural production farms. This system was devel-
oped by using advanced technologies such as IoT technology
and WebGIS to provide functions such as data collection, data
retrieval, data analysis, production monitoring and manage-
ment, remote operation of production processes, and support
for production decisions. Agricultural management informa-
tion 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 man-
agement 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
Fig. 4 Application of IoT in
agriculture, including
management systems, monitoring
systems, control systems, and
unmanned machinery
Table 1 IoT-based smart
management systems applied
in agriculture
Sensors Network Reference
Agricultural machinery GPS GPRS, Wi-Fi, 4G Zhang et al. (2017)
GPS GPRS, Bluetooth Chaudhary et al. (2015)
Farm Soil temperature sensor,
soil pH sensor, soil
moisture sensor
Wi-Fi Vasisht et al. (2017)
Water Moisture sensor, passive
infrared sensor,
temperature sensor
GSM Hadipour et al. (2020)
Pressure sensor, flowmeter,
ultrasonic sensor
Wi-Fi Narendran et al. (2017)
J. Biosyst. Eng.
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 2shows the sensors and net-
works used in the previous studies.
Table 2 IoT-based smart monitoring systems applied in agriculture
Sensors Network Reference
Disease Air temperature and humidity sensor, soil temperature and
moisture sensor, wind speed/direction sensor, rain
meter, solar radiation sensor, leaf wetness sensor
GRPS, GSM, 3G, 4G Khattab et al. (2019)
Humidity sensor, temperature sensor ZigBee Foughali et al. (2018)
Field Soil moisture sensor, temperature sensor Wi-Fi AshifuddinMondal and Rehena (2018)
Humidity sensor, soil moisture sensor, temperature sensor Wi-Fi Dholu and Ghodinde (2018);
Maheswari et al. (2019)
CO
2
sensor, humidity sensor, light intensity sensor,
relative humidity and ambient temperature sensor, soil
moisture sensor
LoRa, 4G Heble et al. (2018)
Ball float liquid level sensor, digital light intensity sensor,
magnetic float sensor, soil moisture sensor, temperature
and humidity sensor
Wi-Fi, 3G Mohanraj et al. (2016)
Camera module, light sensor, temperature and humidity
sensor
Wi-Fi Veloo et al. (2019)
Greenhouse Humidity sensor, illumination sensor, pressure sensor,
temperature sensor
MICAz Akkaşand Sokullu (2017)
Air humidity sensor, air temperature sensor, soil
temperature sensor
ZigBee Wang et al. (2019)
Air quality (CO
2
) sensor, light sensor, soil moisture sensor GSM Aafreen et al. (2019)
Illumination sensor, temperature and humidity sensor GSM, Wi-Fi, ZigBee Liao et al. (2017)
GPRS Geng et al. (2019)
Air temperature sensor, humidity sensor, pH sensor, soil
moisture sensor, water
volume sensor
Wi-Fi Dagar et al. (2018)
Livestock Biometric sensor, temperature and humidity sensor,
weather meter (wind direction, wind speed, and rainfall)
LoRaWAN Debauche et al. (2018)
Biogas sensor, fire sensor, humidity sensor, temperature
sensor, ultrasonic sensor, water level sensor
Wi-Fi Memon et al. (2016)
Accelerometer, air contaminant sensor, CO
2
sensor,
humidity sensor, NO
2
sensor, O
2
sensor, temperature
sensor
ZigBee, 3G Edwards-Murphy et al. (2016)
Accelerometer, thermistor ZigBee Nadimi et al. (2012)
Pest Humidity sensor, illumination sensor, temperature sensor GSM, ZigBee Liao et al. (2012)
Hyperspectral sensor LoRa Gao et al. (2020)
Acoustic sensor, passive infrared sensor GSM Gavaskar and Sumithra (2017)
Soil pH sensor, soil humidity sensor, soil temperature sensor Bluetooth Na et al. (2016)
Wi-Fi Ananthi et al. (2017)
Soil moisture sensor Bluetooth, GPRS, Wi-Fi (2.4 GHz),
Wi-Fi (5 GHz), ZigBee, 3G
Karim and Karim (2017)
pH sensor, soil moisture sensor, soil temperature sensor ZigBee Patil and Kale (2016)
Soil moisture and temperature sensor, temperature and
humidity sensor, ultraviolet light radiation sensor
Wi-Fi, ZigBee Goap et al. (2018)
Soil moisture sensor, temperature and humidity sensor,
ultrasonic sensor
Wi-Fi Muangprathub et al. (2019)
Nitrogen-phosphorus-potassium (N-P-K) sensor Wi-Fi Lavanya et al. (2018)
J. Biosyst. Eng.
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 pro-
vide environmental monitoring data to maintain an opti-
mal crop cultivation environment but also to predict con-
ditions leading to an epidemic outbreak using environ-
mental sensor data. This system was equipped with arti-
ficial intelligence and prediction algorithms that emulate
the decision-making capabilities of human experts and
wasdesignedtoissuewarningmessagestousers.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 ex-
cellent identification accuracy of 97.5% in wild crop dis-
ease 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 ag-
riculture through low-cost sensors and networks. An intelli-
gent 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 offield monitoring and
agricultural automation has been proposed based on a frame-
work 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 tem-
perature 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 (Akkaşand Sokullu 2017). For exam-
ple, conventional methods to monitor environmental fac-
tors 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 ad-
dress 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 green-
house 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;Astilletal.2020). Moocall, a system for monitor-
ing the movement of pregnant cows using motion sen-
sors, 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 mor-
tality rate. Moocall reported that the accuracy of the sys-
tem 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 monitor-
ing, 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 deci-
sions by reducing the need for manual observations and
human decision-making and can be applied to facilitate
the automation of these processes by significantly reduc-
ing 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 fac-
tor 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 manage-
ment 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 recur-
rence 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
J. Biosyst. Eng.
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 environ-
ment 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 moni-
toring 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 ex-
cessive 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 con-
ditions so that high-quality crops on farms can grow well.
Table 3shows information on fields, sensors, controllers,
and networks where IoT-based control systems were applied
to agriculture.
Farm
A control system incorporating IoT technology in crop
production has been developed (Markovićet al. 2015).
On the farm, the control system was used to collect and
monitor data using autonomous sensor devices and con-
trol 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
Table 3 IoT-based smart control systems applied in agriculture
Sensors Controller Network Reference
Farm Gas sensor, IR motion sensor,
temperature and relative humidity
sensor, water flow sensor
Atmel ATmega328 Wi-Fi Markovićet al. (2015)
Acoustic sensor, soil fertility sensor,
soil moisture and humidity sensor
Raspberry Pi Wi-Fi Navulur and Prasad
(2017)
Greenhouse Illumination sensor, temperature and
humidity sensor
Texas Instrument MSP430
F1611
Wi-Fi, ZigBee Liao et al. (2017)
Conductivity sensor, CO
2
sensor,
illumination sensor, temperature and
humidity sensor
STMicroelectronics
STM32L072CZ
Bluetooth low energy,
LoRaWAN
Singh et al. (2020)
Irrigation Soil moisture sensor, temperature and
humidity sensor, water level sensor
Atmel ATmega328 ZigBee Saraf and Gawali (2017)
Humidity sensor, soil moisture sensor,
temperature sensor
Espressif Systems ESP8266 Wi-Fi Nawandar and Satpute
(2019)
Programmable Logic Controller
(PLC)
Wi-Fi Işık et al. (2017)
Humidity sensor, salinity sensor, soil
moisture sensor, temperature sensor
Raspberry Pi Wi-Fi Islam and Dey (2019)
Soil moisture sensor, soil temperature
sensor
Microchip PIC24FJ64GB004 GPRS, ZigBee Gutiérrez et al. (2014)
Soil moisture sensor, temperature and
humidity sensor, ultrasonic sensor
Atmel ATmega328 Wi-Fi Kumar et al. (2017)
Water quality pH sensor, temperature sensor Arduino Mega 2560 Wi-Fi Khatri et al. (2018)
J. Biosyst. Eng.
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 com-
munication 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 de-
signed 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 sen-
sor node and the controller.
Irrigation
IoT-based irrigation systems are used to efficiently utilize wa-
ter 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 irriga-
tion systems (Muhammad et al. 2016). An autonomous sprin-
kler 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 pro-
posed 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 develop-
ment 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, tak-
ing complete control over the quality of farming operations
(Lipiński et al. 2016;Reidetal.2016). With the recent devel-
opment of wireless communication technology, IoT has been
applied to agricultural machinery, and the development of
fully autonomous tractors has been accelerated. Multiple ag-
ricultural 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 inte-
grated 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 effi-
ciency. 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 prac-
tices to a new level of intelligence in precision agriculture
(Boursianis et al. 2020). Since UAVs can be applied to agri-
culture 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 tech-
nology 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
J. Biosyst. Eng.
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 efficien-
cy, and flight restrictions depending on the climate environ-
ment. 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 prob-
lems, IoT-based UAVs are expected to transform convention-
al agriculture more innovative and efficient in the future.
Wireless Communication Technologies Used
in Agriculture
In recent years, wireless transmission technology has devel-
oped rapidly. There are various types of communication tech-
nologies, 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;Frenzel2012;
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 technol-
ogies enable the automation of the entire cycle of agriculture,
thus facilitating highly convenient and highly efficient agri-
culture. ZigBee and Bluetooth consume minimal power and
cost little, so they are widely used in agricultural IoT. In par-
ticular, ZigBee is an integrated, standard short-range wireless
communication technology that consumes minimal power,
costs little, and is versatile, and among various communica-
tion technologies, ZigBee is widely used for IoT implementa-
tion in agriculture (Farooq et al. 2019).
Various types of wireless networks have different charac-
teristics, such as frequency, power consumption,
communication ranges, and data transfer limits. The transmis-
sion 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 4shows 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 com-
pared and selected (Sadowski and Spachos 2020).
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 revolution-
ary concept in farming to meet the upcoming food crisis
(Meola 2020). There are various studies about using IoT tech-
nology 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 irrigat-
ing 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 re-
search service, in 2015, the global shipment of IoT devices
was only 30 million USD; however, in 2020, the global
Table 4 Characteristics of the wireless technologies used in agriculture (Farooq et al. 2019)
Wireless protocols Standard Frequency
band
Data rates Transmission
range
Power
consumption
Cost
Wi-Fi IEEE 802.11 a/c/b/d/g/n 5–60 GHz 1 Mb/s–7Gb/s 20–100 m High High
LoRaWAN LoRaWAN R1.0 868/915 MHz 0.3~50 kb/s < 30 km Very low High
Mobile communication 2G (GSM), 2.5G (GPRS), 3G
(UMTS, CDMA2000), 4G (LTE)
865 MHz,
2.4 GHz
2G:50–100 kb/s, 3G:
200 kb/s,
4G:0.1–1Gb/s
Entire cellular
area
Medium Medium
ZigBee IEEE 802.15.4 2.4 GHz 20–250 Kb/s 10–20 m Low Low
Bluetooth IEEE 802.15.1 2.4 GHz 1–24 Mbps 8–10 m Very low Low
J. Biosyst. Eng.
shipment of IoT devices is projected be approximately 75
million USD globally for agricultural purposes, which is al-
most 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 tech-
nological 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 tech-
nology 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 agri-
cultural processes. When IoT is integrated into entire agricul-
tural processes, efficiency can be maximized. In particular, the
most difficult application of IoT technology is autonomous
agricultural machinery. The conditions under which agricul-
tural machinery operates are atypical environments with nu-
merous variable conditions. Therefore, it is not easy to devel-
op and commercialize autonomous tractors that can be oper-
ated 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 agricul-
tural machinery in a field, it is necessary to prepare for agri-
cultural 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 autono-
mous 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, con-
sidering such an agricultural environment in Korea, more pre-
cise sensing and control technology is required to utilize IoT-
based autonomous agricultural machinery, and accurate field
mapping technology can be used to apply autonomous agri-
cultural 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 var-
ious security requirements. First, in most agricultural areas
except for greenhouse, IoT devices are used in open environ-
ments, 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 pre-
pare for information leakage. Third, IoT in agriculture requires
a large amount of data processing, so multiple sensor nodes
are used. Manygateways and protocols are required to support
these IoT devices. To manage these complex systems, net-
work applications must be reliable and scalable.
Prospects
Recently, advanced cellular and wireless communication tech-
nologies such as 5G have been continuously evolving, enabling
minimal power consumption and wide communication. These
changes lower infrastructure costs for IoT construction and in-
crease the utilization rate of IoT in agriculture. To date, agricul-
ture 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
J. Biosyst. Eng.
agriculture process, such as monitoring the agricultural environ-
ment, controlling an environment suitable for the growth of crops
(including fertilizers and pesticides), utilizing unmanned agricul-
tural 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 technol-
ogies into the entire agricultural process.
The most needed technology development for the IoT of ag-
riculture is autonomous agricultural machinery. Agricultural ma-
chinery 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 vehi-
cles in the near future, which can greatly increase productivity for
growers operating on large-scale farms. For example, an auton-
omous agricultural machine capable of accurate control can in-
crease the efficiency of farming by performing farming during
the day as well as evening. To date, autonomous driving tech-
nology 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 com-
mercialized in Korea. UAVs are currently used for some agricul-
tural processes, such as sowing, applying pesticides and fertil-
izers, 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 tech-
nologies are converging to create higher technological value.
IoT-based deep learning technology has been applied to var-
ious 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 im-
plementation 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 sys-
tems, and unmanned machinery. In particular, IoT in agriculture
is widely used for monitoring soil, livestock, and greenhouses
and controlling irrigation systems and the environmental condi-
tions of farms and greenhouses. Then, the characteristics of com-
munication 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 characteris-
tics 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 in-
tegrates 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 isnecessary
for the development and commercialization of autonomous
agricultural machinery. In addition, for the commercialization
of autonomous agricultural machinery, it is necessary to im-
prove the precision of GPS. In addition, more precise GPS and
control technology based on IoT must be secured to commer-
cialize autonomous agricultural machines applicable to the
Korean agricultural environment, which is small.
Funding This work was supported by the Korea Institute of Planning and
EvaluationforTechnology in Food, Agriculture, Forestry (IPET) through
Agriculture, Food and Rural Affairs Research Center Support Program,
funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA)
(714002-07). It was also supported by the Korea Institute of Planning and
EvaluationforTechnology in Food, Agriculture, Forestry (IPET) through
Advanced Production Technology Development Program, funded by
Ministry of Agriculture, Food and Rural Affairs (MAFRA) (318072-03).
Compliance with Ethical Standards
Conflict of Interest The authors declare that they have no conflict of
interest.
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