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Abstract and Figures

Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud Computing have accelerated the digital transformation of the conventional agricultural practices promising increased production rate and product quality. The adoption of smart farming though is hampered because of the lack of models providing guidance to practitioners regarding the necessary components that constitute IoT based monitoring systems. To guide the process of designing and implementing Smart farming monitoring systems , in this paper we propose a generic reference architecture model, taking also into consideration a very important non-functional requirement, the energy consumption restriction. Moreover, we present and discuss the technologies that incorporate the four layers of the architecture model that are the Sensor Layer, the Network Layer, the Service Layer and the Application Layer. A discussion is also conducted upon the challenges that smart farming monitoring systems face.
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An Architecture model for Smart Farming
Anna Triantafyllou
Dep. of Informatics and Telecommunications Engineering
University of Western Macedonia
Kozani, Greece
Dimosthenis C. Tsouros
Dep. of Informatics and Telecommunications Engineering
University of Western Macedonia
Kozani, Greece
Panagiotis Sarigiannidis
Dep. of Informatics and Telecommunications Engineering
University of Western Macedonia
Kozani, Greece
Stamatia Bibi
Dep. of Informatics and Telecommunications Engineering
University of Western Macedonia
Kozani, Greece
Abstract—Smart Farming is a development that emphasizes
on the use of modern technologies in the cyber-physical field
management cycle. Technologies such as the Internet of Things
(IoT) and Cloud Computing have accelerated the digital trans-
formation of the conventional agricultural practices promising
increased production rate and product quality. The adoption of
smart farming though is hampered because of the lack of models
providing guidance to practitioners regarding the necessary
components that constitute IoT based monitoring systems. To
guide the process of designing and implementing Smart farming
monitoring systems , in this paper we propose a generic refer-
ence architecture model, taking also into consideration a very
important non-functional requirement, the energy consumption
restriction. Moreover, we present and discuss the technologies
that incorporate the four layers of the architecture model that
are the Sensor Layer, the Network Layer, the Service Layer and
the Application Layer. A discussion is also conducted upon the
challenges that smart farming monitoring systems face.
Index Terms—Wireless Sensor Networks, Internet of Things,
Precision Agriculture, Smart Farming, Communication technolo-
gies, Cloud Computing
Nowadays, the digital transformation of the agricultural
sector is considered a priority in order to face the numerous
challenges presented in the fields. Environmental monitoring
and remote controlling in agriculture is rapidly growing to-
wards developing more productive and competitive agricultural
systems and tools. Precision Agriculture and Smart Farming
can lead to this direction. These two terms refer to the
integration of advanced technologies into existing agricultural
practices so as to achieve fine-grid crops management. Smart
farming systems can provide to farmers meaningful real-
time environmental data from the cultivation fields aiming to
boost competitiveness and profit. Almost every aspect of the
agricultural field can benefit from these kind of technological
advances ranging from planting and irrigation processes to
plant protection and harvesting methods. Most of the current
and forthcoming agricultural technologies under Precision
Agriculture fall into the following three categories that are
expected to become the pillars in each Smart Farm:
The Internet of Things (IoT) is a large communication
network involving a vast number of distributed devices
around the network, so as to recognize and notify users
instantly about real-time events. These devices, having
basic computational skills, are called smart objects. Smart
objects are characterized by a unique identifier, i.e., a
name tag for device description and an address for com-
munication. In most cases IoT devices have constrained
resources in terms of power, processing, memory and
The Unmanned Aerial Vehicles (UAV) are flying vehicles
that do not have a pilot on their spindle, but make flights
either autonomously or by means of remote control.
Unmanned aircrafts that can be used for remote sensing
are part of Unmanned Aerial Systems (UASs), which
include all necessary devices and procedures to operate
an UAV, while managing the kind of data it collects.
Sensors are measuring devices that convert an external
stimulus, input signal, into an appropriately measurable
output signal. A sensor is a device that will convert
a macroscopic size (light, power, pressure, etc.) to an
electrically measurable size, and then, after processing
this electrical signal, it will convert it into a standardized
signal with certain characteristics. Exposure to a particu-
lar analyzer or change in environmental conditions alters
one or more of the sensor properties in a measurable
manner, either directly or indirectly.
The adoption of smart farming though is hampered because
of the lack of models providing guidance to practitioners
regarding the necessary components that constitute IoT based
monitoring systems. The contribution of this paper lies upon
the presentation of a simple reference architecture model for
a smart farming monitoring system. This architecture model
engages novel IoT technologies [1] and Wirelesse Sensor
Networks (WSNs) capabilities so as to provide a sufficient
view of precision agriculture. What is more, the proposed
architecture enables a combination of modern remote sensing
techniques such as UAV tracking, Global Positioning System
(GPS) for location detection, Geographic Information Systems
(GIS), real-time monitoring with different types of sensors
and intelligent input control systems. These technologies have
already been tested in various agricultural fields in different
countries for the cultivation of rice, wheat, tomatoes, vegeta-
bles, potatoes, ornamental flowers, chilly, cacao, pepper, corn,
olives, apples, lemons, grape and others. By incorporating new
technologies into agricultural production growers will be able
to manage their crops at a different and more advanced kind
of level in detail that was not possible a few years ago.
The rest of the paper is organized as follows. In Section
II, the proposed smart farming monitoring system architecture
is introduced. In Section III, the sensor layer is presented,
followed by the network layer and its suitable protocols and
technologies in Section IV. Section V refers to the provided
services of the proposed monitoring system, while Section VI
focuses on IoT agricultural applications. Section VII presents
energy saving technologies that be implemented in cooperation
with networking technologies of the system. Existing chal-
lenges are mentioned and discussed in Section VIII. Finally,
Section XI concludes this study.
A precision farming system consists mainly of the sensing
agricultural parameters, the identification of sensing location
and data gathering, the routing of data from crop field to
control station for decision making, the actuation and control
decision based on sensed data and the visualization of results
to the grower through an application. According to these
procedures four basic agricultural layers are defined in our
model as presented in Fig.1:
The Sensor Layer including all kinds of crops sensors
and smart objects for data collection and monitoring.
Sensors can be placed under ground(in the soil), on the
crops or on UAVs [2]. Underground sensors are specially
manufactured so as to be water resistant and usually refer
to measurements of moisture, ph and soil chemical prop-
erties like sulfur. UAV sensors measure environmental
parameters like humidity, temperature, wind speed, lumi-
nosity or solar radiation. However, the most popular kind
of sensors to be placed on UAVs are thermal cameras.
Thermal drones which use vision imaging cameras have
so many positive uses by detecting heat coming from
almost all objects and materials turning them into images
and video.
The Network Layer consisting of all available commu-
nication technologies between sensors and the Internet.
In order deploy efficient crop and field management the
IoT platform utilizes Wireless Sensor Networks (WSNs).
The use of WSN in smart farming systems provides
immediate monitoring and optimization of crop quality,
while offering a potential for large area surveillance with
Fig. 1. Precision farming system architecture.
high sampling densities. The constant monitoring of a
great number of environmental parameters by distributed
sensor nodes along the field help the grower supervise
and maintain optimal conditions to achieve maximum
productivity with remarkable energy savings.
The Service Layer involving processing and analysis of
the collected data. A significant number of studies fo-
cusing on precision agriculture discuss the most efficient
data management and data mining techniques so as to
avoid a low level of productivity in the fields via accurate
predictions. Data processing is supported by Decision
Support Systems (DSS) that take care of the overall
management of available collected information from the
fields towards optimizing crop yield, maintaining quality
and saving resources. It is well known that farmers
suffer great economic losses due to incorrect weather
forecasting or incorrect irrigation methods. Data analysis
is the most important component of IoT agricultural
systems resulting to efficient pesticide use and protection
against diseases.
The Application Layer providing the visualization of
information of the sensor network. The farmer is provided
with the ability to inspect the results of the review
produced by the services of the system and take action
accordingly. The application software presents informa-
tion in a user friendly way and may refer to different
kind of field optimization deployments such as irrigation,
pesticide drift control, cultivation process, crop disease
prediction and protection.
In order to support the efficiency and effectiveness of a
smart farming monitoring system energy consumption should
be kept under control. Due to the limited battery life and
constrained resources of sensor nodes, energy saving tech-
niques must be applied across the sensor and network layer
accordingly. Energy saving techniques deal with the active and
inactive operational time in each sensor node, the scheduling
of information transmission and the routing process of data
The Sensor Layer is in charge of acquiring the data of the
different climatic and soil variables involved in the growth and
production of the crops. In the Sensor Layer each sensor sends
the acquired data in the cloud through a WSN. The WSN (see
more in Section IV) is made up of sensor nodes that operate
under a mesh topology, a coordinator node and a gateway.
Each node in such a network is connected to one or more
sensors [3]. In precision agriculture the most important types
of sensors for measuring the different types of corps attributes
Optical Sensors: Optical sensors are usually embedded
in aerial vehicles and use light reflection information to
measure the varying properties of soil and vegetation. In
that case the sensors acquire image data, that are further
analysed with photogrammetry techniques. Object detectors
and pattern recognition form the basic building block for
extracting information from the images. Such information may
involve the vegetation and soil color, the moisture content and
temperature of soil and vegetation, the position, height, size
and shape of vegetation along with the level of chlorophyll.
In this category we find visible light sensors, multispectral
sensors, hyperspectral sensors and thermal sensors.
Electrochemical Sensors: These types of sensors acquire
data regarding the the nutrient contents of soil and its associ-
ated pH. Electrodes in these sensors work by detecting specific
ions in the soil. Different families of electrochemical sensors
can be recognized depending on the electrical magnitude used
for transduction of the recognition event: potentiometric , that
indicates change of membrane potential); conductometric, that
indicates change of conductance; impedimetric , that indicates
change of impedance; and voltammetric or amperometric that
indicates change of current for an electrochemical reaction
with the applied voltage in the first case, or with time at a
fixed applied potential in the latter.
Location Sensors: Location sensors provide spatial infor-
mation regarding the positioning of an element. These types of
sensors use signals from GPS satellites to determine latitude,
longitude, and altitude to within feet. Three satellites mini-
mum are required to triangulate a position. Precise positioning
is the cornerstone of precision agriculture. GPS integrated
circuits like the NJR NJG1157PCD-TE1 are a good example
of location sensors.
Weather Stations: Weather stations are free-standing units
situated at different locations throughout the cultivating fields.
These stations measure various data for precision agriculture
such as airflow, seasonal rainfall, leaf moisture, speed of
wind, humidity level, direction of wind, atmospheric pressure
and solar radiation, etc.
Summarizing the above the most frequently acquired data
from sensors in the agricultural domain are:
Sensor Type Sensor Model
Soil moisture sensor 10-HS,SY-HS-220, FC-28
Temperature sensor LM35, SHT15, DS18B20
Humidity sensor DHT22, DHT11
Electric conductivity sensor DFR0300
Wind speed and direction sensor SEN0170
Barometric pressure sensor BMP180
Carbon dioxide sensor CDM4161A, MHZ16
Ph sensor MCP1525
Light sensor TSL2561, BH1750
Solar radiation sensor 6450 TSR
Thermal sensors ThermoMAP
Soil moisture and temperature
Environmental humidity and temperature
Leaf wetness
Electric conductivity
Wind speed and direction
Barometric pressure
Carbon dioxide
Ph value
Light intensity
Solar radiation
A sensor node consists of a radio transceiver with an internal
antenna or a connection to an external antenna, a micro-
controller, an electronic circuit for interfacing with the sensors
and an energy source, usually a battery or a built-in energy
harvested form. There are numerous commercial models of
micro-controllers to be used in precision agriculture applica-
tions. The most popular ones are the Arduino, the Raspberry
Pi, the Atmega328 and the LPC2148 boards. Accordingly,
commonly used wireless communication modules used are
the XBee module, the WSN802G module and the NRF24L01
module. A sensor node can vary in size and cost, depending
on the complexity of its capabilities. Size and cost constraints
result in corresponding limitations on resources such as energy,
memory, computing speed, and bandwidth of communications.
The types of sensors that are mostly used in Smart farming
monitoring systems are summarized in Table I.
In precision agriculture WSN communication protocols and
technologies are used to support the connection between
sensor nodes in the network and also to provide a channel
for communication between the coordinator node and the
gateway. According to the type of application, such as preci-
sion farming, field irrigation management or greenhouse crop
management, the sensor network topology may also differ.
Each node utilizes a routing protocol [4] in the view of trans-
ferring the data collected to the coordinator node. The kind
of communication technology to be used between the nodes
depends on the type of application and the characteristics of
the hardware and software selected.
Based on a large number of experimental studies on agri-
cultural fields, there is not an ideal combination of a spe-
cific communication technology and a routing protocol. The
basic goal is to build each smart monitoring system upon
application appropriate networking technologies in order to
operate efficiently with minimum energy consumption. Once
the coordinator node obtains the data it forwards the flow of
information to the gateway so as to reach the main server,
where the database is located. However, in some cases the
coordinator node can be substituted by a base station to obtain
the collected data by the use of a WiFi connection as presented
in [5], or another cellular communication technology.
A. Precision agriculture communication protocols
There is a wide variety of networking technologies suitable
for the deployment of smart farming applications.The most
popular are the following:
The IEEE 802.15.4 standard is a widely used network-
ing technology in precision agriculture and defines the
physical layer and the Media Access Control (MAC)
technique in Low-Rate Wireless Personal Area Networks
ZigBee is another suitable technology for short range
radio communication in the fields utilizing low-power
devices capable of transmitting data over long distances
using intermediate stations.
LoRa is a type of wireless configuration that has been
created to achieve long-range connections for Low -
power Wide Area Networks (LPWANs). LoRAWAN is a
protocol for managing communication between LPWAN
gateways and nodes.
6LoWPAN (IPv6 over Low-Power Wireless Personal
Area Networks) is defined for devices that are IEEE
802.15.4 compatible and efficiently encapsulate IPv6 long
headers in IEEE 802.15.4 small frames.
Bluetooth Low Energy is a global personal area net-
work protocol built for transmitting small data pieces
infrequently at low rates with significantly low power
consumption per bit.
RFID (Radio Frequency Identification) is a different
technology that utilizes radio signals to monitor and
identify in real-time objects without requiring line-of-
sight communication. An RFID system includes a reader,
a tag, and a host and is presented as ideal for field
monitoring in multiple studies.
Moreover, the communication between sensor nodes and a
base station can be supported by:
the WiFi protocol, based on the IEEE 802.11 stan-
dard. This standard specifies the set of media access
control (MAC) and physical layer (PHY) protocols for
implementing wireless local area network (WLAN) Wi-
Fi computer communication in various frequencies.
the GSM (Global System for Mobile Communications), a
standard developed by the European Telecommunications
Standards Institute (ETSI) to describe the protocols for
second-generation (2G) digital cellular networks used by
mobile devices such as mobile phones and tablets.
the GPRS (General Packet Radio Service) technology
standard that provides rapid sending and receiving of
data over the GSM mobile networks based on packet
switching, a well known network transmission process.
the 2G, 3G and 4G (LTE) are respectively the 2nd, 3rd
and 4th generation of GSM technology aiming at higher
Table 2 summarizes the communication technologies adopted
in smart farming systems according to literature.
B. Precision agriculture routing protocols
Data routing algorithms play an important role in WSNs
by establishing the path of communication for data exchange
between sensor nodes and base stations on a network. A
variety of routing techniques have been proposed until now,
aiming to achieve higher performance with minimal power
IoT and WSN routing protocols can be categorized ac-
cording to network structure and the way information will
be disseminated through the network. A routing protocol can
belong to more than one categories, aiming to satisfy as many
performance metrics as possible.
According to the the way by which routing protocols make
the routing decisions the following categorization can be
Proactive routing ( table-driven): The basic feature
of protocols that belong in this category is the periodic
renewal and updating of the routes and destinations that
are formed between the nodes throughout the network.
This provides for great performance in terms of latency,
but decreases battery lifetime. Popular routing protocols
for precision agriculture applications in this category are:
the RPL (Routing over Low Power and Lossy Net-
works) based on distance-vector routing. It constructs
a Destination Oriented Acyclic Graph (DODAG)
whose root is the sink node so as to direct all traffic
towards the sink node. RPL is the ideal routing
protocol for wireless network applications based on
6LoWPAN communication technology.
the DSVD (Destination-Sequenced Distance-Vector)
protocol based on distance-vector routing. It can be
efficiently used to monitor soil parameters, as it
provides route availability to all network destinations
with minimal delay in the route setup process.
the LEPS (Link Estimation Parent Selection) pro-
tocol based on a different technique regarding the
maintenance of routing tables. The basic idea of
link-state routing is that each node builds a map of
the network in the form of a graph regarding the
interconnection of nodes.
Reactive routing ( on-demand): Routing protocols based
on this technique discover routes on demand based on
the transmission of route request packets. In this case, the
Communication technology Data rate Frequency band Range References
IEEE 802.15.4 20-250 Kbps 2400/915/868z 10m [6]
IEEE 802.15.4 - ZigBee 20-250 Kbps 2400/915/868z 10-100m [7]
IEEE 802.15.4 - 6LoWPAN 20-250 Kbps 2.4 GHz 10-30m [8], [6]
Wi-Fi - IEEE 802.11 450 Mbps 2.4GHz 5GHz 100m [9], [5]
GPRS-2G GSM 64Kbps 900MHz-1800MHz 100m [10]
3G 14.4Kbps-2Mbps 1.6-2GHz 100m [10]
4G - LTE 100Mbps-1Gps 2-8GHz 100m [11]
LoRa 0.3 50 Kbps 433,868,780,915MHz 2-5km [12], [13]
Bluetooth LE 1 Mbps 2.4 GHz 2.485GHz >100m [14]
RFID 400Kbps 125KHz-915MHz 3m [15]
routes are discovered only when data transfer is declared.
The basic advantage of this technique is decreasing traffic
load, in case the network changes. The downside to
reactive protocols is their latency, since transmissions
over unknown or expired routes face delays, for which
either the application or the routing protocol has to
account by buffering or dropping data. Popular routing
protocols for precision agriculture applications in this
category are:
the TinyLunar (Tiny Lightweight UNderlay Adhoc
Routing) [16] able to monitor agro-cultivation. Tiny-
LUNAR provides clearly defined interfaces that al-
low the upper layers to from the route characteristics
and work with the IEEE 802.15.4 communication
the AODV (Ad-hoc On-Demand Distance Vector)
protocol is suitable for use by the ZigBee communi-
cation protocol for interconnection of sensor nodes.
the DSR (Dynamic Source Routing) forms a route
on demand when a transmission node requests it.
However, routing is determined by the source node
to the final destination and is not based on the routing
table of each intermediate node.
According to WSN structure, routing algorithms perform in
terms of one of the following three categories:
Flat routing: Flat routing protocols depend on neighbour
nodes to broadcast the collected information. Sensor
nodes near the sink node (sink) have a high demand on
energy because they handle all the information within the
OLSR (Optimized Link State Routing Protocol) is a
proactive protocol based on flat routing that utilizes
information about the status of the nodes to select
the appropriate path for packet forwarding. OLSR
can be effectively used to route information packets
in a video sensing system so as to program irrigation
in a field using unmanned aerial vehicles (UAVs).
ProtoSense is a another routing protocol based on
the reliable retransmission of information using con-
firmation messages. This request-confirmation pat-
tern not only reduces the extra transmission packets
but additionally improves the packet delivery rate,
resulting in a reduction in packet collision rate and
power consumption.
Hierarchical routing: The hierarchical approach divides
the network into clusters. Each cluster depends on a
cluster head node to manage the routing of information
between other clusters or base stations. Hierarchical rout-
ing is the most popular routing method in smart farming
monitoring systems and soil parameter monitoring [15],
– APTEEN (Periodic Threshold-Sensitive Energy-
Efficient Sensor Network) is considered one of the
most efficient routing protocols for crop monitoring
cultivation in this category, taking into account en-
ergy saving and network lifetime [17].
Location-based routing: Protocols in this category use
the location of the node broadcasting information to relay
data to specific areas instead of the entire network.
– LORA-CBF (Location Routing Algorithm with
Cluster-Based Flooding) [18] is a location-based
algorithm which uses the flood method in a hi-
erarchical network structure to route data packets.
Based on flood method, each incoming packet is
sent through every link except the one it arrives
from. The advantages of this protocol include the
good management of network scalability, taking into
account the battery life of sensor nodes.
According to operating procedures, routing protocols can also
be divided into three more categories:
Multi-path routing protocols: Routing protocols of this
category use more than one route to send data. A multi-
path routing protocol can be used to implement a smart
farm monitoring system so as to balance the data transfer
load and conserved energy.
Metric routing protocols: Metric routing protocols take
into account various control parameters related to network
performance, such as power consumption, number of suc-
cessful transmissions, latency, etc. An enhanced version
of RPL, included in this category, is RPAL [8]. This
routing model combines network power meter metrics
and link quality metrics to select the appropriate routes.
Quality of Service (QoS) routing protocols: Routing
protocols in this category aim to maintain the quality
of services and to control power consumption, during
data transmission. Based on this kind of protocols, the
source node can construct an efficient routing path while
avoiding the carrier sense range effect on transmission
In a smart farming monitoring system, the basic component
of intelligence is considered to be the study and filtering
of the collected data. These processes enable the advance
of cultivation procedures and increase productivity. A large
percentage of smart agriculture applications are based on
simulators, commercial programs and specific programming
languages for implementing and controlling the data system.
The service layer utilizes modern software tools in order to
efficiently satisfy multiple tasks, presented in Table 3.
Information management is deployed so the farmer can
consult, record and modify the information collected by the
WSN in tables, statistical graphs and interactive maps. In
addition can download daily, monthly and annual reports of
historical data. However, the farmer can mainly see the current
data of the monitored variables of one or all the WSN nodes
and also consult the history. The interaction with the network
and services layer is achieved using an intermediate layer of
management logic [19]. WSN data will be stored in an online
database [20].
The system also enables big data analytics in agriculture
monitoring by utilizing tools such as Mahout and and various
IoT platforms [21]. The collected data are recorded in a
specific format, so as to correct errors, eliminate duplicate
and inconsistencies and also to solve noise problems. Data
processing techniques based on new models - algorithms for
data classification [22], [23] are also utilized to minimize the
size of redundant data and fasten the analysis.
Moreover, the system performs data mining processes
based on Hadoop or Apache Spark Framework to identify and
discover hidden patterns in the collected data, once they are
processed, in the form of reviews.
What is more, all these services are hosted in the cloud to be
able to access them remotely from any geographical location.
Based on the proposed precision agriculture monitoring
system architecture, the farmer has the ability to interact
with the IoT applications of the system to remotely manage
the cultivation process. Such applications may concern any
aspect of the agricultural field ranging from planting and
irrigation processes to plant protection and harvesting methods
[24]. The applications that can be adopted may involve the
fertilizer application, the weed mapping, the spraying process,
the irrigation of the field and the alert system.
The Variable Rate Fertilizer (VRF) application has as a
target to optimize the usage of nutrients by defining the amount
of fertilizer applied based on the health of the plant. Variable
rate fertilizer in precision agriculture is an area of technology
that focuses on the automated application of fertilizer to a
given landscape. The way in which the materials are applied
is based on data that is collected by sensors, maps, and
gps. VRF applications bring a number of benefits related to
savings on fertilizers and chemicals, potential yield increase
and environmental protection. In the same context is the
Variable Spraying application. These types of applications
implement controllers that turn the herbicide sprayers on and
off. Usually variable spraying applications take into consider-
ation information coming from the weed mapping tools such
as the weed locations. In that case the appropriate volume of
herbicide is estimated and applied in the field based on the
weed intensity.
The Weed Mapping application focuses on the visualiza-
tion of the weed occurrences within a certain crop field with
the help of mappings. The GPS receiver with an aerial vehicle
generates maps which show the weed occurrences. These weed
maps can be combined with fertilizer maps and yield maps.
The IoT-based irrigation system use a microcontroller that
serves as information gateway receiving real-time information
from soil moisture and temperature sensors placed on the
fields. Generally, a moisture/temperature threshold is specified
based on which the microcontroller automatically switches on
the water pump. The microcontroller also has servo motors
to ensure that the area is uniformly irregated. The entire
system can be managed remotely by the end-user through the
dedicated application.
Alert/ notification applications are also very popular in
IoT based precision agriculture. Producers and agriculture
companies implement IoT solutions for instantly tracking their
crop fields. In this case, the data coming from IoT devices is
processed and transformed into knowledge properly visualized
for offering information regarding the health of the vegetation
and the soil, the behavior patterns of the plants, detect signs
of disease on time, identify insects and harmful animals and
instantly alert producers about potential difficulties. This type
of applications serves for storing and analyzing data, providing
producers with relevant recommendations.
The aforementioned applications aim at the efficient field
and crop management so as to:
increase production efficiency
improve product quality
provide more efficient use of chemicals in cultivation
manage pesticide amounts
reduce energy consumption
protect the soil
control water consumption and underground water
The IoT-based agriculture applications can be implemented
for an Android or Windows smart-phone, a tablet or as a
web application. The applications of IoT-based smart farming
apart from conventional, large farming operations, targets also
other growing or common trends in agricultural like organic
farming, family farming (complex or small spaces, particular
cattle and/or cultures, preservation of particular or high quality
varieties etc.), and enhance highly transparent farming. Our
precision agriculture monitoring system can also benefit the
Service Type Tools Description
Information management MySQL Database,
management logic
The process of collecting, storing, managing and maintaining information in all its
Big Data analytics Mahoot, IoT platforms Extracting, cleaning, transforming, modeling and visualization of data with an intention
to uncover meaningful and useful information that can help in deriving conclusion and
take decisions.
Data processing Classification algorithms Classification of data so as to decrease the size of redundant information.
Data mining Hadoop, Apache Spark
Systematic and sequential process of identifying hidden patterns and information in a
large dataset.
dry farming technique that encompass specific agricultural
techniques for the non-irrigated cultivation of crops. Fur-
thermore, greenhouses can utilize our architectural model to
intelligently monitor as well as control the climate, eliminating
the need for manual intervention.
In precision farming applications sensor nodes are usu-
ally powered by low-energy batteries that are difficult or
impossible to recharge or replace. This is considered as a
major disadvantage so as to maintain a real time monitoring
system. Energy saving techniques is vital so as to maintain the
system0s efficiency in smart farming. This kind of techniques
can provide battery life extension by reducing the amount
of communication between the nodes and the base station,
while minimizing the redundant data in the network. Energy
preservation techniques for precision agriculture systems are
presented as a separate architectural level covering the sensing
and networking procedures of smart farming.
In the sensor layer, the proposed energy-saving approach
is an on/off process which is based on the selection of a
subset of nodes that will remain active for a certain period of
time, while others remain inactive. Following this assumption,
SWORD (sleep/wake on redundant data) is an energy preserv-
ing scheme that can be used to collect data on soil moisture [7].
The SWORD algorithm performs data control by removing
redundant data so as to minimize energy consumption and
increase the life of sensor nodes in the network.
In the network layer, data transmissions and receptions
can also be scheduled based on the sleep/awake periods of
sensor nodes at predetermined intervals. For this purpose,
A2S, an automated agricultural precision tracking system can
be utilized [25]. Based on this energy saving technology,
whenever the sensing period is set by the application server,
the sink node keeps the schedule and it spreads the sleep order
message over its network every sensing period. Each time a
node receives the sleep message, it sets the sleep timer’s end
time to the value of the duration field included in the message.
When the meter time ends, the node detects the environment
and battery voltage level and sends the data to the source.
Then, he expects the next sleep request message.
Moreover, another energy saving scheduling technique that
can be deployed in the network layer involves the use of
unmanned flying vehicles in an agricultural crop monitoring
system. Based on this scheme, the node on the unmanned
flying vehicle wakes the ground nodes to retrieve the measured
data. To perform this function, a coded radio signal is sent via
a transmitter to the ground nodes. The nodes are in an inactive
state, except for a small receiver waiting to receive the trigger
What is more, taking advantage of APTEEN hierarchical
routing protocol, a time division multiple access technique
can be implemented as a scheduling method. Based on this
technique, messages are sent to put some nodes in sleep
mode so as to avoid packet collisions between sensor nodes
belonging to different clusters. In addition, carrier sense mul-
tiple access technique is another alternative method, which is
equally effective for avoiding collisions.
The implementation and maintenance of a monitoring sys-
tem in precision agriculture faces several challenges. The
greatest challenge in the sensor layer is for sensor nodes to
achieve efficient and continuous operation for a long time in
a natural environment, while taking into account the climate
change and wildlife interventions. The battery life of sensor
nodes is not considered satisfactory, and it is necessary to
design and implement energy-saving protocols with the highest
possible system performance amongst other precautions. In
addition, depending on the type of application, the supported
agricultural work and the implementation technologies, the
problems that arise can be differentiated. For instance, the
use of sensors and controllers from different manufacturers
prevents communication between them and makes it more
difficult to interconnect with other agricultural components.
Also, the sensor inertia phenomenon has been observed in a
high speed WSN due to non-steep changes in humidity and
soil temperature.
In the network layer, the basic challenges regarding the
operation of a crop monitoring system with WSN and IoT
technologies include the limited computational capabilities of
sensor nodes. The restricted memory of the nodes disables
them to handle large amounts of communication data and
cluster based interconnection procedures. Due to this fact,
long data queues are created in each node, leading to greater
delay in transmissions. The same outcome can be triggered
by the long communication distance of sensor nodes. One
major issue routing algorithms have to deal with in such cases
is the high level of energy consumption, which leads to a
reduction in the overall viability of the network. In precision
agriculture monitoring systems routing protocols should offer
minimum delay, be able to provide efficient services in a
large number of sensor nodes, while taking into account the
limited resources. They should also be capable to accept all
sorts of environments including severe and loss environments,
while providing information security and privacy. Most routing
protocols use some localization technique to obtain knowledge
concerning their locations. The performance of the routing
protocol is a function of network size and transmission media.
So, transmission media of good quality enhances the network
performance directly.
However, in many cases the failure of such advanced
monitoring system may be due to the geographic, cultural
or socio-economic distance between system designers and the
intended user community. Cost is an important limiting factor
in the implementation of such systems. The cost depends to a
great extent on the quality of the materials and the topology
of the network.
This paper proposes the architectural components of a smart
farming monitoring system, based on modern IoT commu-
nication technologies and WSN capabilities, in cooperation
with energy saving protocol schemes. The IoT agricultural
applications enable farmers to collect and analyze meaningful
data. Large landowners and small farmers should welcome
the potential of IoT market for agriculture by installing smart
technologies to increase competitiveness and sustainability
in their productions. The rapid growth of population forces
farmers to meet the demand by implementing agricultural IoT
solutions in a prosperous manner.
This research was co-funded by the European Union and
Greek national funds through the Operational Program Com-
petitiveness, Entrepreneurship, and Innovation, grant number
T1EDK-04873, project ”Drone innovation in Saffron Agricul-
ture,” DIAS.
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... Integration of IoT in the PA context has been the focal point of multiple studies. Some researchers focus on the IoT architecture required to implement an IoT-driven PA system [6][7][8][9][10][11]. They proposed four [6][7][8]11], five [10], and six [9] layer IoT architectures. ...
... Some researchers focus on the IoT architecture required to implement an IoT-driven PA system [6][7][8][9][10][11]. They proposed four [6][7][8]11], five [10], and six [9] layer IoT architectures. From their work, it can be perceived that there are four main layers in an IoT-based PA architecture, which are device, network, cloud, and application, where some of these works break down some layers into sublayers to further discuss the purpose of each layer [9,10]. ...
... However, they did not investigate the integration of these two sensing approaches to improve the aerial data quality. Other researchers have also investigated aerial and ground-based sensing but the ground and aerial data in these works were not related [7,27,31]. Furthermore, they did not investigate or discuss any aspect or potential benefits of integrating these approaches. Combining aerial and ground-based sensing has only been investigated to a limited extent in [26,30]. ...
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... It describes the concept of wireless sensor networks.IOT based smart result of crop fitness and mechanism to denote unfit crops. [13]briefly explains about low cost sensor usage, sustainable agricultural practices, holistic agricultural monitoring system, novel agricultural practices and emerging IOT use in the field of agriculture. In precision agriculture [14], with the help of Wireless Sensor Networks, It can help increase optimize resources and reduce cost. ...
Agriculture is the heart of our nation and society. It is influenced by several factors and parameters such as uneven monsoon, changing climate as well as weather circumstances, rainfall and nutrient facts during the harvest. Agriculture is primarily crucial and also the main source of our livelihood. But, owed to the scarceness of nutrients in plants, the human is strained to handle many dare in everyday life. The restoration of the nutrient is essential, in this view there is need to adopt precision agriculture system which change crop related plans and regulations, whereas nutrient management is a major domain that is needed to be spotlight in the field of farming techniques. The main aim of this research is to create an idea of developing a precision based recommender system for nutrient management and the main scope of this paper to describe the initial phase of the research. The experimental study of this research work is conducted using a terrace garden. The nutrient management with respect to the horticulture crop tomato is considered as the objective. The samples are grouped as two sets namely A and B representing samples without using natural fertilizers/manures and with natural fertilizers/manures. The growth parameters are analyzed and the results are presented. The data collection phase using sensors and Arduino kit is described here. The impact of pests and the remedy taken during the period of growth is recorded. The advices from the experts given in the soil tests are considered for preparing this nutrient management system.
... As described in Fig. 1 and in accordance with (Gupta et al., 2020;Triantafyllou et al., 2019), we consider a typical model of architecture for smart farming where to integrate our proposal of anomaly detection module. In general, the architecture is composed of five layers divided as follows: ...
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This work proposes a new approach for Anomaly Detection in smart agriculture systems. Through the use of multi-sensor systems and Decision Support Systems, it is possible to collect, analyze, and process huge amounts of data on agriculture. This supports the farmer in the decision-making process to optimize the results in terms of quality and quantity, to avoid waste, and to maximize profits. However, the use of IoT and intelligent communication technologies can introduce a number of intentional and unintentional weaknesses and flaws in data and information management. The research proposes an Anomaly Detection System (a multi-layered architecture) to mitigate the infrastructure threats in a pro-active way in the Smart Agriculture domain. The design of the proposed architecture is based on a machine-learning algorithmic approach by a multivariate linear regression (MLR) and a long-term memory neural network algorithm (LSTM). The application of the Anomaly Detection System was done on a real dataset coming from a smart agriculture system located in the Apulia region (Italy).
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Agriculture is one of the most important economic sectors that societies have relied on since ancient times. The developing countries have reached now and late to smart agriculture, or precision agriculture, which uses the latest developments in the field of informatics and the internet of things. Many scientific projects have contributed to the development of smart agriculture, however, these projects focus on partial aspects without addressing other aspects. The application of the results of these projects becomes either a partial or a composite application of different heterogeneous works, which renders the desired results ineffective. Accordingly, this paper proposes a comprehensive reference regulatory approach for an advanced agricultural information system that accommodates the agricultural sector within its interconnected levels, public and private, and its various aspects (Monitor, Prediction, Optimization, Control) within a modern information technology vision based on the internet of things (IoT), artificial intelligence and optimization technologies.
Potato, one of the five most important products grown in the world, has a high application potential in terms of smart agriculture. Smart agriculture makes it possible to produce potato crops in an economically, environmentally and socially sustainable way for future generations. Smart farming practices are involved in almost all growing and even marketing processes to increase crop production and quality. Thanks to smart agricultural practices, it has become possible to increase the agricultural productivity of potatoes, to follow the development of certain parameters and to make appropriate decisions. As smart farming develops over the next few years, it will replace traditional farming methods and help narrow the gap between large and small farmers. With the technological progress, it has become possible to control the field situation and make decisions about the crop in real-time environment. Smart-farming may be applied to all processes of potato value-chain production including seeding, irrigation, soil monitoring and nutrients intelligence, harvesting, pest-disease-weed detection, quality, quantity, harvesting, and climate monitoring, forecasting and decision-making applications. These applications are capable to monitor, and define where, when, and how much variability is existent in a field and even take decision autonomously to manage it. In this chapter smart farming concept was emphasized and its difference from precision agriculture was explained. Examples of smart agriculture applications that take place in almost all cultivation and even marketing processes to increase potato production are given under Smart Farming Applications topic. In the last part of the chapter the potential of smart farming technologies for potato production was explored. The covered topics include technologies such Decision Support Systems, IoT (Internet of Things) and Sensors, Unmanned Ground Vehicles (UGVs) and Unmanned Arial Vehicles (UAVs), Global Positioning System (GPS) and Geospatial Information System (GIS), Artificial Intelligence.
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Conference Paper
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