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Abstract— In this paper, a joint sensing and communication
system is presented for smart agriculture. The system integrates
an Ultra-compact Soil Moisture Sensor (UCSMS) for precise
sensing, along with a Pattern Reconfigurable Antenna (PRA) for
efficient transmission of information to the base station. A multi-
turn complementary spiral resonator (MCSR) is etched onto the
ground plane of a microstrip transmission line to achieve
miniaturization. The UCSMS operates at 180 MHz with a 3-turn
complementary spiral resonator (3-CSR), at 102 MHz with a 4-
turn complementary spiral resonator (4-CSR), and at 86 MHz
with a 5-turn complementary spiral resonator (5-CSR). Due to its
low resonance frequency, the proposed UCSMS is insensitive to
variations in the Volume Under Test (VUT) of soil. A probe-fed
circular patch antenna is designed in the Wireless Local Area
Network (WLAN) band (2.45 GHz) with a maximum measured
gain of 5.63 dBi. Additionally, four varactor diodes are integrated
across the slots on the bottom side of the substrate to achieve
pattern reconfiguration. Six different radiation patterns have been
achieved by using different bias conditions of the diodes. In
standby mode, PRA can serve as a means for Wireless Power
Transfer (WPT) or Energy Harvesting (EH) to store power in a
battery. This stored power can then be utilized to bias the varactor
diodes. The combination of UCSMS and PRA enables the
realization of a joint sensing and communication system. The
proposed system's planar and simple geometry, along with its high
sensitivity of 2.05 %, makes it suitable for smart agriculture
applications. Moreover, the sensor is adaptive and capable of
measuring the permittivity of various Material Under Test (MUT)
within the range of 1 to 23.
Index Terms— Complementary spiral resonator,
complementary split ring resonator, energy harvesting, frequency
domain reflectometry, joint sensing and communication,
microwave sensor, reconfigurable antenna, soil moisture sensor,
wireless power transfer
I. INTRODUCTION
CCURATE measurement of soil moisture levels in
agriculture is crucial for enhancing the quality and
quantity of crop yields. In the realm of smart agriculture, precise
monitoring and effective management of soil moisture play a
pivotal role in optimizing irrigation practices, addressing water
scarcity challenges, and maximizing agriculture productivity
1A. Raza, R. Keshavarz and N. Shariati are with RF and Communication
Technologies (RFCT) research laboratory, School of Electrical and Data
Engineering, Faculty of Engineering and IT, University of Technology Sydney,
Ultimo, NSW 2007, Australia. (e-mail: Ali.Raza-1@student.uts.edu.au)
[1, 2]. Conventional methods of soil moisture measurement are
labor-intensive and time-consuming. Therefore, the
deployment of soil moisture sensors in a wireless sensor
network (WSN) as a part of Internet-of-Things (IoT) has gained
significant attention in smart agriculture [3].
Microwave sensors have been widely presented for soil
moisture measurements in recent years [4-10]. Various
techniques have been utilized, including frequency domain
reflectometry (FDR) [4, 5, 11], time domain reflectometry
(TDR), time domain transmission (TDT) [6, 12, 13], and
capacitive sensing [7-10], to measure the permittivity of the
material under test (MUT). In capacitive sensors, the value of
capacitance is measured by using the discharge time of the
capacitor through a resistor, which is then used to determine the
permittivity of the MUT. However, variations in temperature
can affect the resistance, leading to inaccurate measurements.
The permittivity of the soil can be correlated to its moisture
content [14]. The volumetric water content (VWC) quantifies
the amount of water present in the soil and is calculated using
(1), where W1 and W2 represent the weights of the dry soil and
water, respectively.
𝑉𝑊𝐶
(
%
)
=
𝑊
𝑊
+
𝑊
×
100
(1)
Some previously reported sensors solely relied on the real
value of permittivity (𝜺′𝑚) to calculate the VWC, disregarding
the contribution of the imaginary part (𝜺′′𝒎) [4]. However, the
𝜺𝒎 of the soil varies from 0.05 to 3.5 at 130 MHz,
corresponding to a VWC range of 0–30 %. Neglecting the
contribution of 𝜺𝒎 can lead to considerable errors in soil
moisture measurement.
The theory behind low-frequency RF signals suggests that
they can penetrate deeper into the soil compared to high-
frequency signals [15]. Consequently, a low-frequency FDR-
based sensor can provide measurements of soil moisture at
greater depths, making it a more suitable choice for practical
environments where moisture distribution is uneven across the
soil. In high-temperature conditions, surface soil moisture tends
to be lower, while deeper soil layers retain higher moisture
2A. Raza and N. Shariati are also with Food Agility CRC Ltd, Sydney, NSW,
Australia 2000.
3A. Raza is also with the Department of Electrical, Electronics and
Telecommunication Engineering, University of Engineering and Technology
(UET), Lahore, Punjab, Pakistan.
Precision Agriculture: Ultra-Compact Sensor
and Reconfigurable Antenna for Joint Sensing
and Communication
Ali Raza1, 2, 3, Rasool Keshavarz1, Negin Shariati1, 2
A
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levels. Therefore, a high-frequency sensor may produce
inaccurate measurements due to its limited signal penetration
depth. However, using lower resonance frequencies for the
signal necessitates larger sensor structures, which can pose
handling challenges.
Metamaterial Transmission Lines (MTLs) have been widely
used to minimize the size of microwave devices, such as
antennas [16-18], power dividers [19], filters [20], and
resonators [21]. Within MTLs, split-ring resonators (SRRs) and
complementary split-ring resonators (CSRRs) have been
extensively adopted to achieve size reduction [22]. Various
SRR and CSRR-based microwave sensors are reported in the
literature [4, 23-28]. An FDR based differential sensor for soil
moisture measurements is presented in [4]. The sensor utilizes
both SRR and CSRR on opposite sides of the substrate to
measure the permittivity of the MUT. However, the sensor
exhibits low sensitivity, and its resonance frequency is high (4
GHz), limiting its coverage area for soil moisture
measurements. Another differential sensor based on SRR is
presented in [11]. The sensor operates at a high frequency (5.12
GHz), which makes it susceptible to variations in the Volume
Under Test (VUT). Due to these frequency variations with
different VUTs, the reported sensor can generate false
measurements. Low frequency FDR sensor are presented in [5,
29]. The sensors operate at 560 MHz and 1.017 GHz when
unloaded; however, the structures are large, and the sensors
have low sensitivity. Several microwave sensors have been
reported for the characterization of microfluids [24, 25, 27, 30].
These sensors operate at frequencies of 2.4 GHz, 2.45 GHz,
2.234 GHz, and 2.38 GHz, respectively. While these sensors
have the ability to measure high permittivity values as high as
70, they also exhibit very low sensitivities. Several high-
sensitivity CSRR-based sensors have also been proposed for
measuring the permittivity of the MUT [23, 26, 28]. However,
significant frequency variations occur for different heights of
the MUT, primarily because of the high resonance frequencies
of these sensors. Recently, both a permittivity sensor and an
antenna are integrated to enable sensing and communication
[31]. The antenna is designed to operate at 2.45 GHz Wireless
Local Area Network (WLAN) band, while the sensors’
resonance frequency is 4.7 GHz. A frequency-selective
multipath filter is utilized to measure the permittivity of the
MUT to characterize the material. However, the sensor has a
high resonance frequency and does not produce narrow
resonances, which can lead to false measurements. Designing a
sensor with low resonance frequency, compact size, and high
sensitivity to cover a large VUT is highly challenging.
For remote sensing systems in diverse geographical
structures, it is essential to integrate an antenna for transmitting
sensor data to the base station. This enables remote monitoring
of real-time soil moisture data for precise irrigation and
enhances resource management efficiency. Agricultural areas
would have different geographical structures and the
positioning of IoT equipment can vary. A large-coverage
antenna becomes particularly essential in agricultural areas
where geographical structures vary, and the positioning of IoT
equipment may differ. An omnidirectional antenna has the
ability to communicate in all directions and can be used for
communication with the base station regardless of the
positioning. However, the gain of an omnidirectional antenna is
very low, which makes it unsuitable for large agricultural fields.
Therefore, a directional antenna with pattern reconfiguration is
required for reliable long-distance communication in various
geographical structures. Various pattern reconfigurable
antennas are reported in the literature to switch the radiation
pattern of the antenna using radio-frequency
microelectromechanical system (RF MEMS) switches [32],
PIN diodes [33-42], and varactor diodes [43]. A slot-based
electronically steerable pattern reconfigurable array is
presented for IoT applications in [44]. It utilizes six pin diodes
to achieve pattern reconfiguration of a monopole antenna in the
WLAN band, but the geometry is complex and non-planar.
Another pattern reconfigurable antenna is presented in the
WLAN band for IoT applications [45]. The antenna consists of
4 wire patch antennas, and the reconfiguration is achieved by
using a single single-pole-four-throw (SP4T). However, the
design is non-planar and requires a microcontroller to control
the SP4T switch. A probe-fed varactor-loaded pattern
reconfigurable antenna is presented for continuous beam
steering, utilizing four varactor diodes [46]. However, the
antenna’s geometry is multi-layer.
Designing a sensor with a low resonance frequency to cover
a large VUT while maintaining a compact size and high
sensitivity is highly challenging. In the literature, various types
of FDR-based sensors have been reported for permittivity
measurement. Some sensors operate at low frequencies but
have large sizes, while others exhibit high sensitivities but with
limitations in measuring high permittivity values. For practical
remote sensing systems in diverse geographical structures, it is
also essential to integrate a pattern reconfigurable antenna for
transmitting sensor data to the base station. Thus, the objective
of this research was to develop a compact sensor with both low
resonance frequency and high sensitivity for precise sensing,
along with a pattern reconfigurable antenna for effective data
transmission in various geographical locations.
In this paper, we propose a sensing and communication
system for smart agriculture. The proposed system comprises
an Ultra-compact Soil Moisture Sensor (UCSMS) and a
radiation Pattern Reconfigurable Antenna (PRA) for both
sensing and communication purposes. The proposed UCSMS
operates at a low resonance frequency to cover a larger VUT
and provide in-depth soil moisture sensing. The sensor is
designed using a multi-turn complementary spiral resonator
(MCSR) in the ground plane of a microstrip transmission line
to realize a miniaturized and planar structure. The UCSMS
operates at a low frequency of 86 MHz with a 5-turn
complementary spiral resonator (5-CSR). This results in a
sensor with a compact size (0.028×0.028 𝝀𝟎𝟐), low resonance
frequency, and high sensitivity, making it well-suited for soil
moisture measurements. A directional antenna with pattern
reconfiguration is designed at 2.45 GHz WLAN band for
reliable long-distance joint communication for diverse
geographical structures. The proposed PRA is designed using
CSRR and U-shaped slots and to achieve pattern
reconfiguration, four varactor diodes are integrated with the
CSRRs. By changing the biasing voltage of the diodes, six
different radiation patterns can be achieved. Moreover, this
design also enables the utilization of the proposed PRA for
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wireless power transfer (WPT) or energy harvesting (EH) in the
2.45 GHz WLAN band to store power in a battery. This stored
power can then be utilized to power the varactor diodes. The
precise sensing capability and multi-direction communication
feature of the proposed system make it suitable for precision
agriculture applications.
The major contributions of this paper are summarized as
follows:
1. The proposed UCSMS operates at a low 170 MHz
frequency with a 3-CSR, offering high sensitivity. This
makes it suitable for in-depth sensing and for covering a
large volume of soil, ensuring accurate soil moisture
measurements. Covering a larger volume reduces the
need for numerous sensors on a large-scale farm,
resulting in cost savings for farmers and simpler
implementation.
2. Based on the UCSMS's performance in an
Environmental Testing Chamber (Temperature and
Humidity), it is well-suited for accurate measurements
in real-world agricultural settings with variable
temperature and humidity.
3. In this article, the UCSMS is primarily used for soil
moisture sensing. However, the proposed UCSMS is
adaptive and has the ability to measure material
permittivity within the range of 1–23.
4. The proposed PRA has six different radiation modes,
enabling the generation of six radiation patterns with
different biasing conditions. The proposed PRA
operates at 2.45 GHz Wireless Local Area Network
(WLAN) band and offers a broad coverage area along
with high gain. Consequently, the same antenna can
serve as a receiving antenna for EH or WPT to store
power in a battery.
5. The integration of UCSMS and RPA makes the
proposed system suitable for smart agriculture. By
sensing complex permittivity to achieve precise VWC
measurements, it can then transmit this data to the base
station, thus enabling remote monitoring of soil
moisture.
The organization of this paper is as follows: Section II covers
the working principle, design, and theoretical analysis of the
UCSMS, as well as the PRA design. Section III presents the
simulated and measured results of the UCSMS and PRA.
Finally, Section IV presents the conclusion.
II. DESIGN METHODOLOGY
The working principle, design, and theory of the proposed
system are discussed in the following subsections:
A. Working Principle of the System
The proposed joint sensing and communication system
comprises a UCSMS and a PRA designed for smart agriculture.
Fig. 1 illustrates the working principle of the proposed system.
A practical setup for sensing and communication is shown in
Fig. 1(a), where a continuous wave RF signal is transmitted to
the UCSMS through a circulator, and the reflected signal is then
(a)
(b)
Fig. 1. Working principle of the proposed system, (a) working principle of the
combined system, (b) laboratory setup for testing of the UCSMS.
measured at port-3 of the circulator using a power detector. The
frequency of the reflected signal is used to measure the complex
permittivity of the MUT, which corresponds to a specific VWC.
This information is transmitted to the base station using a
directional pattern reconfigurable antenna. The PRA is
designed with the capability to change the directions of the
radiation pattern for various geographical structures. In standby
mode, the PRA can be utilized for WPT or EH. In the
experimental setup shown in Fig. 1(b), a vector network
analyzer (VNA) is utilized to measure the frequency responses
for different VWC levels using UCSMS. This proposed
UCSMS can be utilized to measure the permittivity of various
materials by configuring the system as depicted in Fig. 1.
B. UCSMS Design
The proposed ultra-compact soil moisture sensor (UCSMS) is
designed using an FR-4 substrate (𝜀= 4.3, 𝑡𝑎𝑛𝛿 =
0.025, ℎ = 1.6 mm) and has dimensions of 50 × 50 mm as
shown in Fig. 2. The proposed sensor incorporates an MCSR
and a microstrip feed line for exciting the resonator. An open
circuit stub of 5 mm length is connected at a distance of 16 mm
with the transmission line to achieve a better impedance
matching for a wide range of permittivity (𝜀) values. The
reflection coefficients of the proposed structure with and
without the open circuit stub are shown in Fig. 3.
The resonance frequency of the spiral resonator can be
calculated using equivalent inductance and capacitance (2).
Increasing the number of turns in the resonator results in a
corresponding increase in the equivalent inductance while
reducing the width of the turns increases the equivalent
capacitance [22]. To analyze different configurations of the
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(a) (b)
Fig. 2. Geometry of the proposed UCSMS, (a) top view, (b) bottom view, (all
dimensions are in mm).
Fig. 3. Reflection coefficient of the proposed UCSMS without and with open
stub.
spiral resonator, simulations were conducted by varying the
number of turns and the width of the turns. Fig. 4 illustrates
three different configurations of the spiral resonator, along with
their corresponding equivalent circuits. It can be observed from
the equivalent circuits that increasing the number of turns
and/or decreasing the width of the turns will increase the
equivalent inductance and/or capacitance, resulting in the
reduction of the resonance frequency. However, the practical
limitation of the fabrication equipment makes it challenging to
reduce the width and spacing beyond a certain limit. A
comparison between the 3-turn complementary spiral resonator
(3-CSR) with a turn width (𝑤) of 1 mm, the 4-turn
complementary spiral resonator (4-CSR) with a 𝑤 of 0.5 mm,
and 5-turn complementary spiral resonator (5-CSR) with a 𝑤
of 0.5 mm is presented in Fig. 5. The simulated resonance
frequencies of 3-CSR, 4-CSR, and 5-CSR are 180 MHz, 102
MHz and 86 MHz, respectively, which validates the
aforementioned theory. For this study and to prove the concept,
the 3-CSR was selected, fabricated, and tested as a soil moisture
sensor.
𝑓
=
1
2
𝜋
𝐿
𝐶
(2)
(a)
(b)
(c)
Fig. 4. Geometry and equivalent circuits of different complementary spiral
resonators, (a) 3-CSR, (b) 4-CSR, (c) 5-CSR.
Fig. 5. Comparison of different complementary spiral resonators.
The equivalent circuit of a spiral resonator is an LC tank
circuit as illustrated in Fig. 4. For instance, the equivalent
circuit of 5-CSR consists of four inductances and a capacitance
connected in series. The inductance value of the resonator can
be calculated using (3) [47], where Z0 is the line impedance, 𝜀
is the effective permittivity, c is the speed of light, and l is the
line length.
𝐿
=
𝑍
𝜀
𝑐
𝑙
(3)
The line impedance (Z0) and the effective permittivity (𝜀)
can be calculated using (4) and (5) [48], where h represents the
5
20
2
2
5
0
50
44
3
1
1
1
1
100 150 200 250 300
Frequency (MHz)
-15
-10
-5
0
W/O Stub
With Stub
L
0
L
0
C
c
L
0
L
0
Reflection Coefficient (dB)
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5
thickness of the substrate and 𝑤 is the width of a single turn.
The thickness of the substrate is 1.6 mm.
𝑤𝑡<ℎ:
𝜀
=
𝜀
+
1
2
+
𝜀
+
1
2
⎣
⎢
⎢
⎡
1
1
+
12
ℎ
𝑤
𝑡
+
0
.
04
1
−
𝑤
𝑡
ℎ
⎦
⎥
⎥
⎤
(4.a)
𝑍
=
60
𝜀
𝑙𝑜𝑔
8
ℎ
𝑤
𝑡
+
0
.
25
𝑤
𝑡
ℎ
(4.b)
𝑤𝑡>ℎ:
𝜀
=
𝜀
+
1
2
+
𝜀
+
1
2
⎣
⎢
⎢
⎡
1
1
+
12
ℎ
𝑤
𝑡
⎦
⎥
⎥
⎤
(5.a)
𝑍
=
120
𝜋
𝜀
𝑤
𝑡
ℎ
+
1
.
393
+
2
3
𝑙𝑜𝑔
𝑤
𝑡
ℎ
+
1
.
444
(5.b)
C. PRA Design
After measuring the soil moisture using UCSMS, an antenna
is required to transmit this information to the base station to
realize smart agriculture. A directional pattern reconfiguration
antenna is designed for reliable long-distance communication
in various geographical structures.
The proposed PRA is designed on a Rogers RO4003C
substrate (εr = 3.55, tanδ = 0.0027). The PRA geometry consists
of a coaxial probe-fed circular patch, two CSRRs, and two U-
shaped slots. Each CSRR slot has a commentary split-ring
resonator and a coplanar waveguide (CPW) stub. The circular
patch is positioned on the top side of the substrate, while the
CSRRs and U-shaped slots are located on the bottom side. The
structure is symmetrical along the x-axis and y-axis and the size
of the substrate is 𝐿×𝑊× 𝐻 mm. Both left and right CPW-
based CSRRs are responsible for the radiation pattern in the xz-
plane, while top and bottom U-shaped slots are employed for
the radiation pattern in the yz-plane. To achieve pattern
reconfiguration in the xz-plane while maintaining the same yz
pattern, two varactor diodes are integrated across each
symmetrical CPW-based CSRR. The PRA is excited using a
coaxial probe at a distance of L4 to achieve better impedance
matching.
The detailed geometry of the proposed PRA is shown in Fig.
6, with Fig. 6(a) depicting the top view, Fig. 6(b) showing the
bottom view, and Fig. 6(c) providing a magnified view. Four
varactor diodes are integrated across both symmetrical CPW-
based CSRRs at an optimized position to achieve pattern
reconfiguration in the xz-plane. A biasing network is designed
and connected to the varactor diodes using vias, as shown in
Fig. 6(a). The antenna is designed for the 2.45 GHz WLAN
band, and the optimized dimensions of the structure are
provided in TABLE 1. By selecting suitable biasing voltages, six
different radiation patterns have been generated in various
directions, named as ‘front‘, ‘back‘, ‘upper left‘, ‘left‘,
(a)
(b)
(c)
Fig. 6. Geometry of the proposed antenna, (a) top view, (b) bottom view, (c)
magnified view of areas A, B, and C.
TABLE 1. DIMENSIONS OF THE PROPOSED PATTERN RECONFIGURABLE
ANTENNA (ALL UNITS ARE IN MM)
L
S
W
S
R
P
L1
W1
W2
L2
81.175
85
18.7
21.25
29.75
1.7
29.75
L3
L4
W2
W3
W4
L5
W5
16.8
7.53
23.8
13
18.7
10.625
16.15
L6
W6
L7
H
0.85
9.35
15.47
1.524
(a) (b)
(c) (d)
(e) (f)
Fig. 7. Radiation patterns of the proposed PRA at 2.45 GHz: (a) front, (b) back,
(c) left, (d) right, (e) upper left, (f) upper right.
R
L
Via
Via Via
D1
D2
D3
D4
X
Z
A A
X
Y
A
X
Y
B C
B C
A
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TABLE 2. BIASING VOLTAGES AND CAPACITANCE VALUES OF VARACTOR
DIODES FOR DIFFERENT RADIATION PATTERNS
Varactor
Diodes
(D1, D2)
Varactor
Diodes
(D3, D4)
Radiation
Pattern
Bias
Voltage
(V)
Capacitance
(pF)
Bias
Voltage
(V)
Capacitance
(pF)
-
0 2.35 0 2.35 Front
0 2.35 3 0.970 Upper left
0 2.35 15 0.466 Left
3 0.970 0 2.35 Upper
right
15 0.466 0 2.35 Right
15 0.466 15 0.466 Back
(a) (b)
Fig. 8. Current distribution of the PRA, (a) left, (b) right.
‘upper right‘, and ‘right‘, as shown in Fig. 7. TABLE 2
summarizes the biasing voltages and corresponding capacitance
values for the six different radiation patterns of the proposed
PRA. In the simulation, the varactor diode is modeled as a non-
linear circuit and a parametric sweep is utilized to change the
value of the capacitance.
The surface current distributions for the ‘left’ and ‘right’
directions are shown in Fig. 8. For the ‘left’ case, the diodes D1
and D2 have a capacitance value of 2.35 pF, resulting in low
reactance at 2.45 GHz. Due to this low reactance value, the
current bypasses the right CSRR and instead travels through the
diodes as shown in Fig. 8(a). However, the diodes D3 and D4
have a capacitance value of 0.466 pF, resulting in a higher
reactance value for the current at 2.45 GHz. As a result, the
current flows through the left CSRR and produces the ‘left‘
radiation pattern. For the ‘right‘ case, diodes D1 and D2 have a
capacitance value of 0.466 pF, while diodes D3 and D4 have a
capacitance value of 2.35 pF. This configuration results in the
generation of the 'right' radiation pattern.
III. SIMULATION AND MEASUREMENT RESULTS
A. Simulated and Measured Results of UCSMS:
To showcase the performance of the proposed UCSMS, the
MCSR sensor is designed and simulated using CST MWS
2019. Furthermore, the optimized UCSMS with 3-CSR is
fabricated on an FR-4 substrate, and the prototype is shown in
Fig. 9. The simulated and measured reflection coefficients of
the unloaded UCSMS with 3-CSR are shown in Fig. 10. The
measured resonance frequency of the sensor is 170 MHz with a
narrow bandwidth, and a close agreement between the
simulated and measured results indicates the validation of the
structure.
Fig. 9. Fabricated prototype of the proposed UCSMS on FR4 substrate (𝜀=
4.3, 𝑡𝑎𝑛𝛿 = 0.025, ℎ = 1.6 mm).
Fig. 10. Reflection coefficients of the unloaded UCSMS with 3-CSR.
To analyze the performance of the UCSMS, a measurement
setup, as depicted in Fig. 11, was utilized. Pure washed fine
sand from Bagged Product Supplies [49] was used to measure
the frequency responses for different VWCs. Various types of
soil exhibit different permittivity values at different VWCs, and
the presence of various materials can also alter the permittivity
of soil. For instance, the concentration of potassium chloride
can increase the soil's permittivity. The proposed system
requires calibration each time for different soil types and
various material concentrations to establish the relationship
between permittivity and VWC for each specific case. After
calibration, the proposed UCSMS can measure VWC on a
specific soil type with constant material concentration.
Calibration of the system was conducted using pure sand
without any impurities, as outlined in TABLE 3. The frequency
responses of the proposed sensor were measured using a 4-port
R&S VNA (VNA-ZVA40) and a standard SOLT (short, open,
load, and through) calibration was performed before conducting
each test. A cube of size 40 × 40 × 40 mm is 3D printed and
utilized as a soil container. The simulated and measured
frequency responses of the proposed UCSMS are shown in Fig.
12. As the VWC in the soil increases, the frequency response of
the UCSMS shifts leftwards with a significant frequency
difference. The frequency of the sensor shifts from 158 MHz to
115 MHz as the VWC increases from 0 to 30 %. This frequency
transition is used to measure the permittivity of the soil, which
can be correlated to the VWC. The complex permittivity of the
soil at 130 MHz for different VWCs is provided in TABLE 3. A
close agreement between simulation and measurement results
can be observed, validating the design of the proposed UCSMS.
100 150 200 250 300
Frequency (MHz)
-25
-20
-15
-10
-5
0
Measured
Simulated
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(a)
(b)
(c)
Fig. 11. Experimental setup to measure soil moisture, (a) measurement setup,
(b) unloaded UCSMS, (c) loaded UCSMS with soil.
(a)
(b)
Fig. 12. Frequency responses of the proposed UCSMS with 3-CSR at different
VWCs in the soil, (a) simulated, (b) measured.
TABLE 3. PERMITTIVITY OF SOIL FOR DIFFERENT VWCS AT 130 MHZ [14]
VWC
(
%
)
0 5 10 15 20 25 30
𝜺
𝒓
2.5 6 8 14.5 18 21 23
𝜺
𝒓
0.05
0.5
0.9
1.8
2.5
3.1
3.5
Three tests are performed separately to evaluate the precision
of the measurement and frequency shifts have been measured
with a maximum variation of ±5 MHz. The relationship
between the frequency shift (∆𝑓) and the complex permittivity
is shown in Fig. 13, indicating a linear change.
(a)
(b)
Fig. 13. Frequency shift vs permittivity, (a) real value, (b) imaginary value.
The proposed UCSMS is also analyzed with different heights
of soil as depicted in Fig. 14(a) and corresponding frequency
responses are measured. The frequency responses of the
UCSMS for different heights are shown in Fig. 14(b), clearly
indicating that the proposed UCSMS is not sensitive to the
variation in the height of the soil under test. Hence, the
proposed sensor can cover a larger VUT of the soil. To analyze
the sensor's accuracy, two reference devices, Dielectric
Assessment Kit (DAK 12) [50] and TDR-315L [51], were used
to measure the permittivity and VWC of the soil. Fig. 15
displays the soil measurements using the DAK 12 and TDR-
315L sensor.
Reflection Coefficient (dB)
Reflection Coefficient (dB)
0 5 10 15 20 25
Real Value of Permittivity
10
20
30
40
50
60
Measurement-1
Measurement-2
Measurement-3
0 0.5 1 1.5 2 2.5 3 3.5
Imaginary Value of Permittivity
10
20
30
40
50
60
Measurement-1
Measurement-2
Measurement-3
Unloaded
L
oaded
with soil
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8
(a)
(b)
Fig. 14. Frequency Analysis for different soil heights, (a) different soil
thicknesses, (b) frequency responses.
(a)
(b)
(c)
Fig. 15. Soil measurements using reference devices, (a) TDR-315L
measurement, (b) DAK 12 calibration, (c) DAK 12 measurement.
Reflection Coefficient (dB)
20
mm
30
mm
40
mm
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9
(a)
(b)
Fig. 16. Sensor performance analysis in the climate chamber, (a) test setup, (b)
loaded UCSMS inside chamber.
To evaluate the sensor's performance in real-world
conditions, additional tests have been conducted in a controlled
vötschtechnik Environmental Testing Chamber (Temperature
and Humidity) to measure the frequency responses under
various environmental conditions. The laboratory setup for
analyzing the sensor's performance under different
environmental conditions is shown in Fig. 16. In a real-world
environment, temperatures can drop as low as 0 °C during the
nighttime and rise as high as 45 °C in summer, while humidity
levels can vary from 40 % to 70 % at different times. To
characterize the sensor in real-world conditions, temperature (0
to 45°C) and humidity (40 to 70%) are varied in the chamber to
create extreme weather scenarios for 15 % and 30 % VWC
values. For temperature variations, a constant humidity of 50 %
is used, and for humidity measurements, a constant temperature
of 25 °C is maintained. In the case of 30 % VWC, the measured
results as depicted in Fig. 17, indicate that there is no significant
difference in the frequency response readings with varying
humidity values, as the sand is already saturated. However, for
15% VWC, the resonance frequency shifts towards the left side,
indicating higher soil moisture with increased humidity. At 0
°C, where water exists in both liquid and solid forms, the VWC
decreases due to the formation of ice, causing the resonance
frequency to shift to the right. The frequency variation due to
temperature is higher for 30 % VWC compared to 15% VWC.
Similarly, at a high temperature of 45 °C, soil moisture
decreases over time due to water vaporization, thereby
validating the accuracy of the sensor. It should be noted that
there is no significant difference in the resonance frequency
between 10 and 30 °C.
(a)
(b)
(c)
(d)
Fig. 17. Frequency response variations for different climate conditions in the
environmental chamber, (a) 15 % VWC at 25 0C, (b) (a) 30 % VWC at 25 0C,
(c) 15 % VWC at 50 % humidity, (d) 30 % VWC at 50 % humidity.
Reflection Coefficient (dB)
100 150 200 250 300
Frequency (MHz)
-25
-20
-15
-10
-5
0
Reflection Coefficient (dB)
Open Environment
0°C
15°C
30°C
45°C
100 150 200 250 300
Frequency (MHz)
-25
-20
-15
-10
-5
0
Reflection Coefficient (dB)
Open Environment
0°C
15°C
30°C
45°C
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10
Fig. 18. Fabricated prototype of the proposed PRA on Rogers substrate (𝜀=
3.55, 𝑡𝑎𝑛𝛿 = 0.0027, ℎ = 1.524 mm).
B. Simulated and Measured Results of PRA:
The PRA is designed and simulated using CST MWS 2019.
The optimized structure is fabricated on a Rogers RO4003C
substrate, and the fabricated prototype is illustrated in Fig. 18.
The Skyworks SMV1231-079LF varactor diode is utilized,
offering a capacitance range of 0.466 − 2.35 pF. In the
simulation, a non-linear diode model has been utilized and
various capacitance values have been achieved
using a parametric sweep. A biasing network is designed to bias
the diodes which consists of two RF chokes, each with a value
of 390 nH, and two resistors with a resistance of 1.2 kΩ,
connected in series with the varactor diode.
(a)
(b)
Fig. 19. Reflection coefficients of the proposed PRA, (a) simulated, (b)
measured.
The simulated and measured reflection coefficients of the
proposed PRA are shown in Fig. 19. The antenna resonates at
2.45 GHz, with a minimum measured bandwidth of 20 MHz for
the ‘upper left‘ and ‘upper right‘ configurations. There is a
slight variation in the resonance frequency of the PRA under
different bias conditions of varactor diodes, with a maximum
difference of 20 MHz between the ‘front‘ and ‘back‘
resonances. The simulation and measurement results exhibit
good agreement, validating the successful realization of the
antenna.
To analyze the far-field parameters, 2D radiation patterns of
the proposed PRA are measured in an anechoic chamber at
different basing voltages. A DC power supply was used to bias
the varactor diodes to achieve various biasing conditions, and a
standard gain horn antenna was utilized to measure the gain of
the proposed PRA. The measurement setup for evaluating the
far-field characteristics is shown in Fig. 20 and the simulated
and measured 2D radiation patterns of the proposed PRA at
2.45 GHz are displayed in Fig. 21. Due to the presence of a DC
power supply and biasing leads connected to the antenna for
varactor diodes biasing, noise is introduced in the 'left' and
'right' radiation patterns. Nevertheless, for other biasing
conditions, Fig. 21 illustrates a close agreement between the
simulated and measured radiation patterns. Six different
radiation patterns have been achieved with different biasing
voltages across the diodes. Due to the symmetry of the structure
in the x-axis, the patterns are symmetrical in the xz-plane as
shown in Fig. 21. The patterns in the yz-plane are directional,
with a constant 00 lobe for all biasing conditions, as there are no
varactor diodes across the U-shaped slots. The gains and
directions of the maximum lobe in the xz-plane are summarized
in TABLE 4. The antenna achieves a maximum measured gain
of 5.63 dBi in the ‘front‘ case. The directions of the maximum
lobes are 60, 1850, 100, 250, 3400, and 2540 for ‘front‘, ‘back‘,
‘left‘, ‘upper left‘, ‘right‘, and ‘upper right‘, respectively.
Fig. 20. Measurement setup to measure the far-field characteristics.
C. Figure of Merit
To compare the performance of the proposed sensor, the
sensitivity of the UCSMS is calculated using (6),
𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦
=
𝑆
=
𝑓
−
𝑓
𝑓
(
𝜀
−
𝜀
)
×
100
(6)
where 𝑓 is the resonance frequency of the unloaded sensor, 𝑓
and 𝑓 are the resonance frequencies due to different materials,
𝜀 and 𝜀 represent relative permittivity of materials at 𝑓 and
W
S
L
S
Top View Bottom View
Reflection Coefficient (dB)Reflection Coefficient (dB)
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11
xz-plane yz-plane
(a)
(b)
(c)
(d)
(e)
(f)
Fig. 21. Simulated and measured 2D radiation patterns at 2.45 GHz, (a) front,
(b) left, (c) upper left, (d) right, (e) upper right, (f) back (solid line represents
measured and the dotted line represents simulated plots).
TABLE 4. FAR-FIELD CHARACTERISTICS OF THE PROPOSED PRA
Pattern
Type
Gain
(dBi)
Maximum Lobe
Direction (
𝜽
𝒎
)
in the xz
plane
simulated
measured
simulated
measured
Front
6.17
5
.63
0
0
6
0
Back
4.45
4.14
180
0
1
8
5
0
Left
2.7
2.54
132
0
10
0
Upper l
eft
5.
117
5.
08
15
0
25
0
Right
2.73
2
.
62
220
0
340
0
Upper r
ight
5.08
4.923
34
5
0
354
0
𝑓, respectively. Additionally, a figure of merit (FOM) is
defined in (7) taking into account the sensitivity (S), maximum
electrical length of the sensor (l), and maximum measurable
permittivity (𝜀).
𝐹𝑖𝑔𝑢𝑟𝑒
𝑜𝑓
𝑀𝑒𝑟𝑖𝑡
=
𝑆
×
𝜀
𝑙
(7)
Based on these evaluations, a comparison of the proposed
UCSMS with the reported sensors is summarized in TABLE 5.
Furthermore, the proposed system possesses the ability to
communicate with the base station to transfer information.
IV. CONCLUSION
In this paper, we present a joint sensing and communication
system for smart agriculture. The proposed system consists of
an ultra-compact sensor for soil moisture measurement and a
PRA for communication. An MCSR is used with a microstrip
transmission line to achieve miniaturization. The proposed
UCSMS operates at low frequencies, 180 MHz for 3-CSR, 102
MHz for 4-CSR, and 86 MHz for 5-CSR, making it suitable for
covering a large volume of soil. The PRA operates at the 2.45
GHz WLAN band, facilitating the transmission of information
to the base station. Integration of four varactor diodes with the
communication antenna enables pattern reconfiguration,
leading to the generation of six distinct radiation patterns with
different bias conditions. This feature makes the system suitable
for smart agriculture across diverse geographical landscapes. In
standby mode, the PRA can also be utilized for WPT and EH
applications to store power in a battery. This stored power can
be utilized to bias the diodes to achieve reconfiguration. Both
the UCSMS with 3-CSR and the PRA have been fabricated and
measured, demonstrating a close agreement between the
simulated and measured results. The sensor is adaptive and
capable of measuring the permittivity of various materials
within the range of 1–23.
ACKNOWLEDGMENT
This project was supported by funding from Food Agility
Cooperative Research Centre (CRC) Ltd, funded under the
Commonwealth Government CRC Program. The CRC
Program supports industry-led collaborations between industry,
researchers and the community.
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12
TABLE 5. COMPARISON OF THE UCSMS (ULTRA-COMPACT SOIL MOISTURE SENSOR) WITH REPORTED SENSORS
Ref. Size at fu
(
𝝀
𝟎
𝟐
)
fu
(GHz)
Number of
Sensing
Bands
Measurement
Technique
Sensitivity
@
𝒎𝒂𝒙
(
𝜺
𝒓
)
(
%
)
Max
Measured
Permittivity
FOM
𝟏
𝝀
𝟎
This
Work
0.028×0.028 0.170 1 MCSR (3-CSR) 2.05 23 1683.92
[4] 0.67
×
0.13 4 2 SRR and CSRR 0.9 16.7 22.43
[23] 0.35× – 2.67 3
CSRR 1.6 9.2 42.05
[24]
–
2.4
1
CSRR
0.19
79.5
-
[25] 0.32
×
0.2 2.45 1 M-CSRR 0.2 70 43.75
[26] 0.36
×
– 2.7 1 CSRR 1.7 10.2 48.167
[29] 0.34
×
0.034 1.017 1 Shorted-Dipole 0.614 19 34.31
[31] 0.42×0.44 4.7 1
Frequency
Selective
Multipath Filter
0.214 26 12.65
[30] 0.198
×
0.198 2.38 1 EBG Resonator 0.224 70 79.19
[27] 0.184
×
0.372 2.234 1 SRR 0.04476 70 8.42
[28] 0.6×0.4 3.49 1
Complementary
Curved Ring
Resonator
4.47 4.4 32.78
[5] 0.38×0.38 0.56 1 Metamaterial
Absorber
0.109 19.1 5.47
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