IEEE Consumer Electronics Magazine - May/June 2022 - 54

Special Section on Consumer Technologies for Smart Agriculture
to the detected pressure. We used the following
model: Easy Press II, with a maximum pressure of
10 bar and a flow of 12 000 l/hr. The expansion
tank has a volume of 24 liters and a maximum
pressure of 8 bars. The aim of the expansion tank
is to reduce the water pumpON-OFF switching rate.
This is accomplished by accumulating up to 8
bars of pressure inside it. By limiting the ON-OFFswitching
rate, the cost is not only reduced, due
to power consumption, but the life of the water
pump is increased as well. The irrigation controller
will ideally be controlled in turn by a master
hub. While both are powered by Raspberry Pi
boards, the controller is a terminal node in the
network and is usually placed close to the valves.
The master hub is typically installed in a central
location as it serves as the nexus for managing all
other interconnected devices. Even though there
are various boards and platforms that could be
used for this purpose,17 our choice was the Raspberry
Pi board. The reason for choosing Raspberry
Pi boards, other than the low price, was the
energy efficiency, the programming facility
(Raspberry Pi uses Python, a programming language
with relatively fewer lines of code and less
complexity), small hardware footprint (RAM and
CPU), the fact that it is future proof, the many
available GPIOs, and its several roles in the IoT
system, at the same time. Moreover, most of the
software and projects done on Raspberry Pi are
open source and are maintained by online user
communities, always excited about new challenges.
The humidity sensors chosen in this project,
for the sensor-based scenario, are the
popular YL69 from SparkFun and the microcontroller
is the ESP8266 chip, a low-price Wi-Fi module
perfectly suited for projects in the IoT field.
The humidity sensors are connected to the irrigation
controller and continuously send values for
the relative humidity of the soil. These values are
then grouped by zone and used to compute the
min/max and average humidities for each zone.
The automation consists in deciding how much
of the total allocated watering time will be used
for the respective zone. The proposed irrigation
system can be monitored and controlled very
easily, using a mobile app, the qToggle app. Using
the qToggle app, users will be able to monitor
and control the irrigation system from any part of
the world. An important metric related to an
54
irrigation system is the adequate quantity of
water used for irrigation. There are various factors
that influence the calculation of this amount:
the geographical area, the season/weather, the
type of soil, the type of crops, the type of sprinklers
(each of them having their own range of precipitation
rates and flow, depending on the
choice of nozzle), water pressure, etc. Each type
of nozzle has its own operating specifications,
given by datasheets, for example, pressure (in
bar), radius (in m) or flow rate (in l/hr). Here we
employ nozzles with producer specifications as
in Li et al.5 Lawns usually need 15 to 25 mm of
water per week, either from natural or artificial
sources, usually a combination of both. To estimate
potential water usage, we employ a simple
but effective approximation: one liter of water
will cover one square meter to a depth of one millimeter.
Thus, to apply 15 mm of irrigation
weekly, 15 liters of water are needed for each
square meter. The necessary watering time can
be computed as: t ¼ QnA
F , where Qn ¼ 15 liters
needed for each square meter, A is the area in
square meters, corresponding to each zone, F is
the total flow (measured in cubic meters per
hour) calculated as a sum of flows corresponding
to all sprinklers in a zone. These calculations are
no longer needed when using moisture sensors.
The type of sensors used in this project are
YL69,18 a simple, low cost sensor used for measuring
moisture in different types of materials.
The output voltage for this sensor ranges
between 3.3 and 5 V and the output values are
related to soil moisture: 0 to 300 for a dry soil and
300 to 700 for a humid soil, respectively. Each
sensor is connected to the analog digital converter
(ADC) input of a microcontroller (ESP8266
chip in this case). The ADC converts the input
voltage into digital data that can be processed by
the qToggle server running on top of the Raspberry
Pi. In the end, the qToggle server is provided
with five input values representing the soil
humidity percentages for each of the five zones.
A SPECIFIC CASE STUDY
A suite of irrigation systems is described in
the literature or already available on the market.
The combination of technology and setup difficulty
associated with each on them varies, as do
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