Practical Design of a WiFi-based Wireless Sensor Network for

Precision Agriculture in Citrus Crops

Laura García

, Sandra Viciano-Tudela

, Sandra Sendra

and Jaime Lloret

Universitat Politècnica de València, Camino de Vera, s/n., 46022 Valencia, Spain

Keywords: Internet of Things (IoT), Wireless Sensor Network (WSN), IEEE 802.11, Smart Agriculture, Citrus Crops,

Coverage, RSSI, Practical Deployment.

Abstract: Agriculture is one of the most important economic sectors in the world. On the one hand, most of its

importance resides in the food that is produced. On the other hand, this sector employs millions of people in

the world both directly and indirectly. The introduction of Internet of Things (IoT) solutions for crop

monitoring and management was the next step to improve the quality of the product, the quantity in the

production, and to reduce the use of resources such as water. In this paper, a practical design of a WiFi-based

Wireless Sensor Network (WSN) for citrus crop monitoring is presented. A mathematical model is obtained

from the results of practical tests performed with low-cost devices for different height configurations of both

the Access Point (AP) and the emitter according to the distance. The maximum coverage for the AP for each

configuration is obtained as well. The results show the number of sensor nodes necessary to monitor the field

according to its extension, and the number of APs needed to provide coverage for all the nodes deployed on

the citrus field for each configuration. This way, a tool for the design of WSNs to monitor citrus plots is

provided.

1 INTRODUCTION

Agriculture is one of the strongest economic sectors

in the world, employing over one-third of the

population on the planet (Perri, 2017). Furthermore,

many families depend on it, where often the entire

family is employed in this sector. This is more evident

in developing countries where agriculture is the key

to the survival of people. Agriculture is also related to

sustainability and human rights issues such as water

scarcity, contamination due to chemical fertilizers,

child labor, or the contamination of the products

caused by the usage of untreated wastewater and

polluted water for irrigation. These aspects are raising

the concern of both farm workers and consumers,

leading varied organizations to take action to improve

these problems.

As the focus on sustainability increases, the

United Nations has established a set of sustainable

development goals for 2030 (UN, 2022) to raise

https://orcid.org/0000-0003-2902-5757

https://orcid.org/0000-0001-6621-0148

https://orcid.org/0000-0001-9556-9088

https://orcid.org/0000-0002-0862-0533

awareness of some aforementioned aspects. Eight of

the 17 proposed goals can be related to agriculture

and how it can be optimized and be more sustainable

using technology. The first goal associated with

agriculture and its concerns is the second goal, which

is focused on wasting less food and supporting local

farmers. The sixth goal is focused on avoiding

wasting water, which in precision agriculture can be

accomplished by the implementation of smart

irrigation systems (Garcia et al., 2020). Furthermore,

the seventh goal is focused on energy efficiency.

Precision agriculture Internet of Things (IoT) systems

can implement energy efficiency algorithms and use

solar panels to power the system to reduce energy

consumption and obtain green energy. IoT precision

agriculture systems can also be part of the eighth goal

of creating job opportunities for the youth as new job

opportunities would be designed to manage these

systems. The ninth goal is Industry, Innovation, and

Infrastructure investment. The eleventh goal is

García, L., Viciano-Tudela, S., Sendra, S. and Lloret, J.

Practical Design of a WiFi-based Wireless Sensor Network for Precision Agriculture in Citrus Crops.

DOI: 10.5220/0011355300003286

In Proceedings of the 19th Inter national Conference on Wireless Networks and Mobile Systems (WINSYS 2022), pages 107-114

ISBN: 978-989-758-592-0; ISSN: 2184-948X

107

focused on Sustainable Cities and Communities, and

IoT precision agriculture systems can be tied to this

topic. There are papers focused on urban farms.

Moreover, the twelfth foal is focused on Responsible

Consumption and Production. This goal can be

applied to both the materials used in the hardware of

the precision agriculture system and the optimization

of the resources used in the fields. Lastly, the fifteenth

goal is centered on Life on Land and protecting the

environment. This can be achieved by focusing on

making agriculture more sustainable.

As mentioned before, IoT technologies can aid in

achieving the goals proposed by the UN in the sector

of precision agriculture. Moreover, with the increase

in the production of low-cost sensors, it is possible to

implement low-cost precision agriculture solutions

for developing countries to aid farmers and aid in

achieving sustainable agriculture. As a result, most of

the papers on intelligent systems for agriculture are

produced in countries with high dependence on this

sector and low income for the farmers, such as India,

which produced 57.5% of the papers on smart

irrigation for precision agriculture in the world

(Garcia et al., 2020). Furthermore, most of the papers

employed low-cost sensors to ensure their

affordability, increase the chance of these systems

being deployed, and help farmers improve the quality

of both produce and their work life.

However, there is a crucial aspect to consider

regarding the access of the precision agriculture

system to the internet. Fields are often far from

populated areas and do not have access to the cabled

infrastructure of a service provider. Therefore,

wireless communication presents itself as a solution

to provide communication to remote locations.

WiFi is a very popular wireless communication

technology for IoT systems in precision agriculture;

its coverage range allows Arduino microcontrollers to

receive information from the different nodes

distributed through the crop field. The use of

applications and smartphones will enable the farmer

himself to know in real-time the situation of the area

and be able to make sustainability decisions when

necessary, for example, in the use of irrigation water,

and fertilizers, among others. Some studies show that

the signal between nodes varies according to the

height at which the node is located. In (Garcia et al.,

2021), the results show that the lower the size, the

better the signal quality. In addition, it is taken into

account that the vegetation density varies with the

quality and strength of the WiFi signal.

Considering all these issues, this paper presents a

practical design of WIFI-based wireless sensor

networks for precision agriculture in citrus crops. To

do that, we based on our mathematical model in

previous practical experiments with low-cost Wi-Fi

nodes. Our practical design will estimate the number

of sensors and access points (APs) we will need to

cover a field with different sizes for different

conditions.

The rest of the paper is organized as follows.

Section 2 presents the related work. A general

description of WSNs is presented in Section 3.

Section 4 describes the mathematical model in which

our practical design is based. The final results of our

practical design are shown in Section 5. Finally, the

conclusion and future work are presented in Section

2 RELATED WORK

This section presents some works based on WiFi

connections to send data in different types of crop

fields. In addition, with the data collected through the

use of sensors (temperature, humidity, water level,

pH...), the farmer is informed of the situation of the

field, which allows him in real-time to be able to

know the needs of the crop and make decisions.

Firstly, it is important to perform a good design in

the network deployment to ensure the correct

operation. For example, Brinkhoff et al (Brinkhoff et

al., 2017) studied the propagation characteristics of

the 2.4 GHz WiFi signal in natural outdoor

agricultural crop environments using field data. As a

result, they established that crop growth status was

much more significant in determining signal strength

than weather conditions, with signal strength

declining by 8 dB in a cotton field and 20 dB during

the season. dB in a rice field. Another example of wifi

practical deployment is presented by Yang et al.,

(Yang et al, 2022). This paper addressed the problem

of mold affecting wheat in storage. They developed a

low-cost, non-intrusive, and non-destructive

detection system by implementing the use of Wi-Fi

devices. They demonstrated the feasibility of using

WiFi Channel Status Information (CSI) amplitude for

mold detection in stored wheat. Finally, they

established the MiFi system, a radial basis function

(RBF) neural network-based detection model, and

mold detection.

Additionally, those designs are frequently used in

specific applications such as the following ones:

In 2018, (Mei-Hui Liang et al., 2018) proposed a

dynamic monitoring method for China's production

greenhouses. This is because until then, artificial

means were being used, which used cables. The

automatic monitoring methods were based on the 485

WINSYS 2022 - 19th International Conference on Wireless Networks and Mobile Systems

108

bus or the CAN bus, presenting particular problems.

For this reason, and to alleviate these problems,

dynamic monitoring based on Wi-Fi is proposed. To

do this, through the sensor of the designed

greenhouse, the light intensity of the greenhouse,

humidity, and temperature were taken remotely. They

developed a software and hardware system for data

collection from the greenhouse via Wi-Fi. They had

the system running for seven days, and the results

obtained were highly accurate. In that same year,

(Mahmud et al., 2018) established a system based on

carbon dioxide, humidity (both MQ135), and

temperature (DHT11) sensors. These parameters are

necessary for cultivating mushrooms since they

cannot grow at temperatures below 25ºC or above

33ºC, hence their importance. This work focuses on

developing an automatic environmental control

system that allows the farmer to optimally control

crop conditions. The sensors are connected to the

ESP8266 Wi-Fi module to become IoT (Internet of

Things) sensors that send a large amount of data to

the Internet for monitoring and evaluation. The

irrigation system is thus capable of turning on or off

depending on the climatic conditions in which the

crop is found.

On the other hand, in 2020, several authors

presented studies related to WiFi connection and

agriculture. (Rawi et al., 2020) used the Arduino

microcontroller, which captures, processes, and

subsequently analyses the data from the previously

connected sensors. The utilized sensors were used to

establish soil parameters. They used the humidity,

pH, and soil inclination sensor. This study arose from

the need to be able to monitor palm oil fields

remotely. This type of oil is one of the staple products

of Malaysia. In this way, the production of this type

of crop can be controlled because the quality of the

oil depends vitally on the quality of the soil. This

allowed the farmer to control his cultivation since the

data obtained was sent via WiFi to the cloud database

for registration, and, in addition, it was displayed in

real-time on a graph using ThingSpeak. Sreeja et al.

(Sreeja et al., 2020) in that year developed an

intelligent agricultural monitoring system to be able

to provide water and air to the crop. They carried out

an innovative method based on IoT, which consisted

of three sensors (pH, water level sensor, and soil

humidity), a WiFi module, and a DC motor. The data

obtained from the sensors and stored and processed

by the microcontroller were sent to the IoT platform

via WiFi connection. The values obtained are sent to

a registered mobile through the GSM modem. In

addition, they established that if the value of the

sensor exceeded the specified threshold, it would

send a notification to the farmer's mobile. This system

allows the farmer, in real-time, to know the state of

the crop field. In this case, it was used for rice

production fields. And finally, (Saini et al., 2020)

showed an IoT platform based on NodeMCU (which

has built-in WiFi) and ThingSpeak. The work shown

develops an intelligent agricultural monitoring

system to alleviate the problems that farmers face

concerning irrigation. With this development, the

farmer is helped to control the irrigation of his field

from a computer or his smartphone. In addition, if the

value is below what is established, an email will be

sent so that the farmer can take the necessary

measures. It allows real-time monitoring of such

essential parameters for the crop as humidity and

temperature directly involved with the need or not to

irrigate. In this way, it is intended to optimize water

resources.

In the year 2021, Lloret et al. (Lloret et al., 2021)

developed a sensor network based on WiFi

communication for a flood irrigation system. With

this, they established the opening and closing of the

gates for the irrigation water. They selected different

sensors to obtain atmospheric parameters

(temperature, humidity, and rainfall), water

parameters (salinity, water height, and water

temperature), and soil parameters such as humidity.

These sensors were installed in a natural environment

to evaluate the correct functioning of the system. In

addition, they developed an application for the user.

The data taken by the sensors were collected and

helped the farmer himself manage the irrigation of his

fields.

3 WSNs FEATURES AND

TOPOLOGIES

This section describes a brief description of the main

characteristic of a WSN and the typical topologies we

can find in applications, such as precision agriculture.

3.1 Overall Description of a WSN

WSNs are based on low-cost and low-consumption

devices (nodes) that are capable of obtaining

information from the environment that surround

them. The collected information can be locally

processed and communicated through wireless links

to a central coordination node. Additionally, these

nodes can have different roles. While some of them

can be only in charge of collecting data, others can act

as a network element required to forward the

Practical Design of a WiFi-based Wireless Sensor Network for Precision Agriculture in Citrus Crops

109

messages transmitted by more distant nodes to the

coordination center. A WSN is composed of

numerous spatially distributed devices, which use

sensors to monitor several parameters including

temperature, sound, vibration, pressure, movement,

or contaminants. Sensors can be fixed or mobile.

The devices used to measure these parameters are

usually autonomous units that consist of a

microcontroller, a power source (commonly a

battery), a radio interface that depends on the

technology used, and finally, digital and analog

inputs/outputs to which sensors can be connected.

Due to battery life limitations, nodes are built

considering, among others, energy constraints, and

they generally spend a lot of time in a low-power

consumption, i.e., sleep mode. WSNs have self-

restoring and self-organizing capabilities, that is, if a

node fails, the network will find new ways to route

data packets. In this way, the network will remain

alive as a whole, even if an individual node fails. Self-

diagnosis, self-configuration, self-organization, self-

restoration, and repair capabilities are properties that

have been developed for this type of network to solve

problems that were not possible with other

technologies.

Possible applications of sensor networks include:

 Smart agriculture.

 Industrial automation

 Smart and automated homes

 Video surveillance

 Traffic monitoring

 Monitoring of medical devices

 Monitoring of weather conditions

 Air traffic control

 Robot control

3.2 Typical WSNs Topologies

Sensor networks are characterized by being

unattended networks with a high probability of failure

(in the nodes, in the topology), usually built ad hoc to

solve a very specific problem (that is, to run a single

application). For this reason, the topology of a WSN

is a critical design factor that depends a lot on its

deployment in the crops to be monitored.

In order to design a topology, it is commonly used

mesh networks. However, it is possible to work with

more simple topologies such as an infrastructure (star

topology) topology where the center of the star is the

gateway (See Figure 1). This gateway device acts as

a communication bridge to a wired network.

Figure 1: Topologies used in WSNs.

WINSYS 2022 - 19th International Conference on Wireless Networks and Mobile Systems

110

The cluster-based topology combines the benefits

of the previous one to give intermediate fault

tolerance, but with greater scalability and energy

efficiency. It is appropriate for medium-scale

applications that use battery-powered nodes. This

topology organizes the nodes into logical groups

called clusters where each router, including the base

station, forms a cluster and it is therefore known as a

cluster head. The end nodes associated with a

particular cluster head belong to its group, and all

their transmissions are controlled by the cluster head.

The base station is identified as the root of the

network and forms the initial cluster. With this

topology, the life of the batteries can be extended

using a mechanism that establishes cyclic periods in

which the radio of the nodes is turned on or off.

4 MATHEMATICAL MODEL

In this section, the coverage models obtained from the

results of the experiments performed with low-cost

devices on citrus fields are presented.

The tests were performed in orange fields with

devices deployed at different heights and distances.

The Access Point (AP) was deployed at heights of

0m, 0.5 m, and 1 m. The emitter was located at

heights of .5 m, 1 m, 1.5 m, and 2 m. The results from

these tests were published in our previous work

(Garcia et al., 2021), as well as the employed

methodology and the description of the utilized

devices. The following models present the theoretical

received power for each of the combinations of AP

height and emitter height. For an emitter height of

0.5m, the equations of the theoretical models with the

AP located at 0m, 0.5m, and 1m are (1), (2), and (3)

respectively. Where d is the distance in meters.

𝑃





𝑑𝐵𝑚



 39.25  11.24 ln 𝑑 (1)

𝑃

.



𝑑𝐵𝑚



 38.54  8.303 ln 𝑑 (2)

𝑃





𝑑𝐵𝑚



 57.47  1.899 ln 𝑑 (3)

The models for the emitter height of 0.5m and

each of the AP heights are equations (4), (5), and (6).

𝑃





𝑑𝐵𝑚



 41.77  10.71 ln 𝑑 (4)

𝑃

.



𝑑𝐵𝑚



 47.68  4.124 ln 𝑑 (5)

𝑃





𝑑𝐵𝑚



 43.43  8.337 ln 𝑑 (6)

Equations (7), (8), and (9) present the models for

the combinations with an emitter height of 1.5m.

𝑃





𝑑𝐵𝑚



 49.199  12.08 ln 𝑑 (7)

𝑃

.



𝑑𝐵𝑚



 39.336  9.037 ln 𝑑 (8)

𝑃





𝑑𝐵𝑚



 50.89  7.516 ln 𝑑 (9)

Lastly, the models for the emitter height of 2m are

equations (10), (11), and (12).

𝑃





𝑑𝐵𝑚



 42.851  12.02 ln 𝑑 (10)

𝑃

.



𝑑𝐵𝑚



 40.715  10.26 ln 𝑑 (11)

𝑃





𝑑𝐵𝑚



 47.002  8.008 ln 𝑑 (12)

According to the previous models, the theoretical

coverage of the utilized low-cost AP devices in a field

of citrus trees, including the losses caused by the

vegetation from the trees is presented in Figures 2, 3,

and 4. Figure 2 presents the results for the AP

deployed on the ground. As can be seen, the best

coverage is obtained for the lower emitter heights as

there is little vegetation density.

Figure 2: Coverage of AP deployed at a height of 0m for each of the emitter heights.

Practical Design of a WiFi-based Wireless Sensor Network for Precision Agriculture in Citrus Crops

111

Figure 3: Coverage of AP deployed at a height of 0.5m for each of the emitter heights.

Figure 4: Coverage of AP deployed at a height of 1m for each of the emitter heights.

Table 1: Maximum coverage for each configuration of AP

and emitter height.

Emitter height

AP Height 0.5 m 1 m 1.5 m 2 m

0 m 38 36 22 22

0.5 m 100 100 90 47

1 m 100 81 46 43

The coverage for the AP height of 0.5m according

to the theoretical models is provided in Figure 3. As

can be seen, the increase in height has led to better

coverage. However, the best results remain for the

medium to lower emitter heights.

Lastly, Figure 4 presents the coverage for the AP

height of 1 m. In this case, the coverage is again

reduced, in comparison to the previous AP height,

and the best results are obtained for the lowest emitter

heights.

Considering the data presented in the previous

figures and selecting -80 dBm as the lowest

admissible received power, the coverage in meters of

the APs for each configuration is presented in Table

5 RESULTS

This section presents the results of the number of

devices that need to be deployed to provide coverage

on citrus fields of varied dimensions. These results

have been obtained considering the node density, the

area to be covered, and the coverage provided by the

devices at different heights as presented in the

previous section.

Figure 5 presents the number of sensor devices

required to monitor the expanse of a field of the

dimensions displayed on the X-axis according to node

densities of 1 node per 5 m

, 10 m

, 50 m

, and 100

. The selection of the node density will depend on

the functionalities and accuracy desired for the design

of the crop monitoring system.

The AP devices need to gather information from

the number of sensor devices shown in the previous

figure. The number of AP devices needed to cover the

field for the emitter height of 0.5m and the three

options of AP heights is presented in Figure 6. As it

can be seen, the number of necessary AP devices of

these are located on the ground increases substantially

as the area increases. However, for both heights of

WINSYS 2022 - 19th International Conference on Wireless Networks and Mobile Systems

112

0.5m and 1m, the device requirement is considerably

less.

Figure 5: Number of nodes per area of the field.

Figure 6: Number of APs per area of the field for the emitter

at 0.5 m.

Figure 7: Number of APs per area of the field for the emitter

at 1 m.

The results for the number of AP devices

necessary when the emitter is placed at a height of 1

m are presented in Figure 7. As can be seen, there is

an increase in the number of devices needed to

provide coverage compared to the previous case.

Furthermore, there is a slight difference between the

AP heights of 0.5 m and 1 m, with the first one being

the best option.

Figure 8 presents the results for the emitter height

of 1.5 m. The number of necessary APs to provide

coverage is increased once again compared to the

previous case. Furthermore, the difference between

the number of devices needed with the AP located at

a height of 0.5 m or 1 m is more differentiated as well,

with the first one being the best option.

Lastly, Figure 9 presents the results for the emitter

height of 2 m and the different configurations of AP

height. For the AP height of 0m, the results are similar

to those of the previous case. However, for the higher

AP heights, the number of necessary devices is

increased. Though the difference between the 0.5 m

and 1 m AP heights is reduced compared to the

previous figure.

Figure 8: Number of APs per area of the field for the emitter

at 1.5 m.

Figure 9: Number of APs per area of the field for the emitter

at 2 m.

25000

50000

75000

100000

125000

150000

175000

200000

0 20406080100

Number of nodes

Area of the field (hm

)

1 node per 5 m^2 1 node per 10 m^2

1 node per 50 m^2 1 node per 100 m^2

100

150

200

250

0 20406080100

Number of APs

Area of the field (hm

)

0m 0.5m 1m

100

150

200

250

300

0 20406080100

Number of APs

Area of the field (hm

)

0m 0.5m 1m

100

200

300

400

500

600

700

020406080100

Number of APs

Area of the fields (hm

)

0m 0.5m 1m

100

200

300

400

500

600

700

0 20406080100

Number of APs

Area of the fields (hm

)

0m 0.5m 1m

Practical Design of a WiFi-based Wireless Sensor Network for Precision Agriculture in Citrus Crops

113

6 CONCLUSION AND FUTURE

WORK

Agriculture is a key element to the economy and well-

being of the population in the world. WSNs were

incorporated into the agricultural fields as a way of

improving both the produce and the amount of the

utilized resources. In this paper, a practical design for

WSNs in citrus plots based on WiFi wireless

technology is presented. The mathematical model for

different configurations of the heights of the APs and

the sensor nodes is provided. Furthermore, the results

of the number of devices needed to monitor the field

and to provide enough coverage were obtained as

well for each configuration.

In future work, tests with deployments of LoRa-

based WSNs for agriculture will be performed to

provide the tools for the design of this type of

network.

ACKNOWLEDGEMENTS

This work has been funded by the “Ministerio de

Ciencia e Innovación” through the Project PID2020-

114467RR-C33 and by "Ministerio de Agricultura,

Pesca y Alimentación” through the “proyectos de

innovación de interés general por grupos operativos

de la Asociación Europea para la Innovación en

materia de productividad y sostenibilidad agrícolas

(AEI-Agri)", project GO TECNOGAR.

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sustainabledevelopment/sustainable-development-

goals/ (accessed on 10 May 2022)

WINSYS 2022 - 19th International Conference on Wireless Networks and Mobile Systems

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