10.1109@jsen.2015.2435516

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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/JSEN.2015.2435516, IEEE Sensors Journal
Sensors-12234-2015 1

Abstract—An automated irrigation sensor was designed and
implemented to use in agricultural crops. The sensor uses a
smartphone to capture and process digital images of the soil
nearby the root zone of the crop, and estimates optically the water
contents. The sensor is confined in a chamber under controlled
illumination and buried at the root level of the plants. An Android
App was developed in the smartphone to operate directly the
computing and connectivity components, such as the digital
camera and the Wi-Fi network. The mobile App wakes-up the
smartphone, activating the device with user-defined parameters.
Then, the built-in camera takes a picture of the soil through an
anti-reflective glass window and an RGB to gray process is
achieved to estimate the ratio between wet and dry area of the
image. After the Wi-Fi connection is enabled, the ratio is
transmitted via a router node to a gateway for control an irrigation
water pump. Finally, the App sets the smartphone into the sleep
mode to preserve its energy. The sensor is powered by
rechargeable batteries, charged by a photovoltaic panel. The
smartphone irrigation sensor was evaluated in a pumpkin crop
field along 45 days. The experimental results show that the use of
smartphones as an irrigation sensor could become a practical tool
for agriculture.
Index Terms—Automation irrigation, optical sensor,
smartphone, Android App, wireless sensor network.
I. INTRODUCTION
OBILE devices (e.g. Smartphones and Tablets) have
powerful computing, sensing, and connectivity
resources, and run Apps for multiple purposes. The device
characteristics commonly include a high performance processor
at low-power consumption, running frequencies of over 1 GHz,
and a vast memory, also contains a high-resolution touchscreen
with graphics capability. They are built with different sensors,
such as high-resolution CCDs, global positioning systems
(GPS), accelerometers, gyroscopes, and compasses among
others. These mobile devices have diverse connectivity options,
general packet radio service (GPRS), third- or fourth-
generation (3G/4G), Bluetooth, and Wi-Fi for Internet and local
access. They have a multi-tasking operating system for running
first- and third-party Apps, resulting an attractive developing
platforms for a specific applications in different domains.
Also with additional external sensors the mobile devices can
enable attractive sensing applications elsewhere, such as
environmental monitoring, healthcare, security and
transportation.
The authors are with the Engineering Group, Centro de Investigaciones
Biológicas del Noroeste, S.C., La Paz, México, 23097 (corresponding author to
provide phone: +52 612-1238421; e-mail: maporta@cibnor.mx).
Mobile devices have been used as external biosensor
readouts with on-board audio hardware, including automated
data processing by means of an App [1]. Other monitoring App
was designed for driver fatigue monitoring based on the driver
face image and a bio-signal sensor [2]. A mobile radiation
detector has been developed with a PIN photodiode connected
to a smartphone via a microphone input and uses the GPS and
networking capabilities for data sharing [3]. Another
application has been developed to measure pulsatile
photoplethysmograph signals from a fingertip using the built-in
camera lens and then use this data to detect atrial fibrillation,
which is the most common sustained arrhythmia [4].
Collaborative Apps predict the scheduled traffic signals and
monitor road conditions, using the smartphone cameras
mounted on the car windshields [5]. A mobile phone-based App
has been developed to recognize the people activity, and their
context in a picture, by means of the usage of the different
sensors, like “standing or playing” from the accelerometer,
“indoor or outdoor” from a photo device [6].
Mobile devices could be used in important economic sectors
-such as agriculture- embracing the value chain for diverse
purposes, from the farm logistics to the consumer, employing
diverse sensors and information communication technology
[7]. Some applications make usage of embedded resources of
the device, meanwhile other purposes requires the development
of software and hardware. Mobile devices, such as PDAs
(personal digital assistant) have used Apps to collect field data
for decision making in agricultural production traceability [8].
A mobile phone has been used to send dripper run time
scheduling advice via SMS from a water balance system,
whereas farmers sent back data about irrigations and rainfalls
to update the water balance [9]. The worker uses a GPRS
enabled handheld device to capture information on poultry
operations collected at a remote chicken farm and transmitted
to a back-end server in the main office [10]. A smartphone App
runs a web-based whole-farm simulator Simugan, oriented to
assist the beef cattle production systems, simulating a scenario
with initial values and conditional rules to manage a farm [11].
A mobile App employed in agroecosystems allows the farmers
perform nitrogen leaching simulations. This can be conducted
into the field and achieve an on-site analysis of nitrogen
management practices for environmental conservation [12]. By
Smartphone Irrigation Sensor
Joaquín Gutiérrez, Juan Francisco Villa-Medina, Aracely López-Guzmán, and Miguel Ángel Porta-
Gándara
M

Sensors-12234-2015 2
pointing a mobile device to barcodes or near field
communication (NFC) tags, a viticulturist may download or
upload data of climate, disease, and pest incidence of a grape
field [13].
Other applications for the agriculture sector using mobile
devices have been developed; for calculating leaf area with
image processing techniques [14], for estimating the leaf area
index (LAI) by two indirect methods [15], for monitoring
farmland air and soil conditions in real time [16], for
implementing a Munsell soil-colour sensor for the examination,
description, and classification of soils [17], and for detecting
pests and plant diseases on leaves by converting the mobile
device into a digital microscope [18].
In this work, an automated agricultural irrigation sensor is
described. The sensor is implemented on a mobile device to
estimate optically the water contents of the soil nearby the root
of the crop through an image processing App. When the water
contents drops at an established figure, the required amount of
water is delivered to the crops. The irrigation sensor was
developed employing an Android smartphone exploiting their
built-in components. This sensor was linked by a router node as
a new wireless sensor unit to the Automated Irrigation System
[19], and tested in a pumpkin (Cucurbitaceae pepo) crop field.
II. IRRIGATION SENSOR
The irrigation sensor is based on an embedded camera of a
smartphone, enclosed in a waterproof and light-tight buried
chamber. The camera with a controlled illumination source
takes an image to estimate the water contents of the soil. The
dark and light pixels are differentiated by means of a gray scale
analysis, corresponding to the soil wet-dry sectors. A router
node is used to forward the contents value to a gateway, which
drives a livewell pump to provide automatically the water needs
in a crop field. A developed irrigation App uses the smartphone
computing capability and connectivity, including their
microprocessor, the built-in digital camera, the Wi-Fi radio
modem, the liquid crystal display (LCD), and the external
memory.
The App wakes-up the smartphone from the standby mode at
a given programmable time, activating the mobile device with
a specific set of parameters such as image resolution, screen
rotation, turn-on timer, and LCD brightness. The built-in
camera is activated to take an RGB picture of the soil through
an anti-reflective glass window inside the chamber (Fig. 1). To
take the picture of the dark environment in the underground
chamber, the region of the soil is lighted by means of a white
ultra-bright LED, located on a pole, which is turned on
employing an automatic illumination circuit developed with a
microcontroller, through the sense of a variation in a voltage
divider with a photoresistor to detect the brightness of the LCD.
The LED is turned off after the picture is taken, to preserve
energy.
The picture is transformed to a gray scale image and a
Relative Wet Soil (RWS) percentage is estimated. The App
enables the Wi-Fi connection of the smartphone creating an
access point, allowing the transmission of the percentage to a
router node, in order to increase the smartphone network
coverage. This node is linked using the ZigBee communication
protocol [20] to a gateway that drives the irrigation pump if the
value is suitable (Fig. 2). The App sets the smartphone into the
standby mode to preserve its power, waiting for the next image
to be acquired.
A. Relative Wet Soil Estimation
The irrigation sensor is based on the pixel differentiation of
a grayscale image produced by diverse water contents in the
soil. To estimate this differentiation a set of images were taken
and their histograms were analyzed in the grayscale from 0 to
Fig. 1. Smartphone irrigation sensor.
Fig. 2. Irrigation sensor linked to the Automated Irrigation System.

Sensors-12234-2015 3
255, using Matlab R2014a (Fig. 3), between an image when the
soil is completely dry (Fig. 3.1) and other when is saturated with
300 ml of water (Fig. 3.6). These two images represent the
limits of the dynamic range of the system, which depends of the
physical characteristics of the soil: sand, loam, and clay
percentages. Other images were acquired adding 60, 120, 180
and 240 ml of water respectively (Fig. 3.2-3.5). The histograms
shown a slight difference between dry and wet pixels. In order
to enhance their differences, a lightfield image of a super-white
paper was taken with the same background of the illumination
provided by the LED and subtracted from the set of images. The
resulting images and their histograms are shown (Fig. 4). Then,
the wet and dry pixels can be distinguished. The figures from
4.2 to 4.5, shown two differentiated regions, where the value of
around 200 is the limit between the wet and dry pixels. As can
be seen the number of wet pixels is increased directly
proportional to the added water. Therefore, the RWS is
calculated as the ratio between the number of wet and total
pixels.
B. Irrigation Sensor Components
1) Smartphone
To implement the irrigation sensor, the basic smartphone
ZTE-V791 was selected, which integrates an ARM Cortex-
A9 processor with 512 MB of RAM and 4 GB of internal
memory, runs at 1GHz on Android 2.3.6 Gingerbread with
application programming interface level 10. A touchscreen
of 3.5” is provided, with 320 x 480 pixels, with a standard
Li-Ion battery of 1200 m Ah. Other features include
GSM/GPRS and EDGE bands, Wi-Fi 802.11 b/g/n,
Hotspot, WAP 2.0 and a 3.0 megapixel rear-facing camera
with 2048 x 1536 pixels.
2) Illumination circuit
The controlled illumination circuit is integrated by the high-
brightness white LED-P3W200-120/41 (SiLed, DF,
Mexico) powered at 3.3 V through a voltage regulator
ADP122AUJZ-3.3-R7 (Analog Devices, Norwood, MA),
which is enabled by the low power consumption
microcontroller PIC24FJ64GB004 (Microchip
Technologies, Chandler, AZ) that monitors continuously
the light-dark condition of the smartphone LCD, by means
of a voltage divider using a 5 MΩ photoresistor in series
with a 100KΩ resistor, turning on and off the LED
respectively. All these electronics components are mounted
on a designed PCB. The power supply consists of four series
connected AA (Ni-MH, 1.2 V, and 2000-mAh) batteries
maintained by a 0.225 W photovoltaic panel MPT4.8-75
(PowerFilm Solar, Ames, IN). This provides full energy
autonomy, the smartphone included.
3) Chamber
The smartphone and the controlled illumination circuit are
enclosed in the chamber, which is made of rigid PVC plastic
with a rectangular cuboid profile of 0.30 x 0.40 x 0.26 m (W
x L x H) dimensions and weighing 2 kg. The front chamber
face has a window of anti-reflective glass, which
dimensions are 0.20 x 0.18 m (L x H) and located at 0.04 m
above the bottom edge and 0.03 m from the left edge.
4) Router Node
The wireless router node was developed by means of an
XBee Wi-Fi radiomodem (Digi International, Eden Prairie,
MN), linked with the Wi-Fi access point of the smartphone
and an XBee-PRO S2 radiomodem to link the node to the
gateway. Both radiomodems are interfaced using a
microcontroller to transfer a data packet that includes the
router node identifier, the RWS percentage, date, and time.
The energy is provided with a similar power supply
employed for the illumination circuit.
C. Irrigation App
The App was programmed by means of the Android Studio
SDK, which allows the development of multiplatform
applications. In addition, the ZTE-V791 driver was installed to
emulate and debug the App. The irrigation App was developed
in Java (Fig. 5). Initially, the algorithm requests for a user
defined time to start a periodically process. This loop,
Fig. 3. Images of the soil with different water contents. Fig. 4. Enhanced images of the soil with different water contents.

Sensors-12234-2015 4
customizes the camera to a specific resolution, enables Wi-Fi
network to create a WLAN hotspot, enables power manager and
turns on the LCD touchscreen to activate the white LED,
illuminating the interior of the chamber. After, the algorithm
takes a RGB image and is converted to grayscale, a sector of
this image is selected to eliminate the edges and is employed to
calculate the RWS percentage and transmitted to the
microcontroller-based gateway via the router node. The image,
percentage, date, and time are stored in the smartphone memory
to create a log file. The smartphone goes into sleep-mode. When
the user-defined-time is elapsed, the loop starts again.
The RWS is estimated according to:
1) RGB to gray
The RGB components R(i, j), G(i, j), and B(i, j), where i and
j denote the spatial coordinates of the pixels, are converted to a
gray scale matrix I(i, j), according to [21], using the equation:
       jiBjiGjiRjiI ,1140.0,5870.0,2989.0, 
2) Pixel differentiation
The gray image I(i, j) is subtracted from a lightfield matrix
L(i, j), to enhance the image. The dark and light pixels that
correspond to the wet and dry ones is differentiated, comparing
them to an established ε limit:
 
   
   







],,[0
],,[1
,
jiLjiIif
jiLjiIif
jiH
 

n
i
m
j
jiHk
1 1
),(
where k is the number of wet pixels, meanwhile n and m
represents the size of the digital image.
3) RWS
The percentage of the ratio between wet (k) and total (n × m)
pixels represent the relative wet soil value, given by:
    mnkRWS 100%
This percentage is truncated at integer values, so the
resolution is one unit.
III. IRRIGATION SENSOR OPERATION
To test the smartphone irrigation sensor, cucurbitaceae seeds
were planted in the field, because its rapid growth of about 40
days. The field was located in a 20 x 30 m greenhouse in
Fig. 7. Chamber, router node and gateway location in the crop field.
Fig. 5. Smartphone irrigation App.
Fig. 6. Buried chamber located parallel the drippers.

Sensors-12234-2015 5
Comitan, Baja California Sur, Mexico (24° 7.933’ N, 110°
25.416´ W). The composition of the soil is loamy sand (sand
85%, loam 13%, and clay 2%). The irrigation field was set to
10 production beds and watering tubes 20 m long with drippers
every 0.2 m. Two seeds were planted in basins beneath each
dripper, except for three consecutive drippers, which basins
were maintained without seeds to prevent that the roots of the
crop interfere with the image. The chamber was placed parallel
between its front face and those three drippers -aligning the
midpoint of the glass with the middle dripper- separated 0.05 m
and buried 0.1 m from the ground level (Fig.6), without soil
disturbance.
The system was placed into the greenhouse, because the
chamber is buried the LAN signal of the smartphone is
attenuated, the transmission range was tested successfully until
25 m. To prevent data loss, the router node was located at 20 m
from the chamber to assure coverage of the smartphone Wi-Fi
link between them. The gateway was located outside the
greenhouse at 10 m from the router node (Fig. 7). The router
node through the XBee-PRO S2 radiomodem can be linked up
to 1.6 km.
After preparing the field, the crops were irrigated manually
with 0.6 liters per dripper/day during two weeks until sprout
occurs. This mass flow was obtained due the irrigation pump
capacity and the watering tubes resistance and was measured in
two different drippers by means of a 1000 ml glass beakers. The
irrigation was performed by the automated irrigation system
using the smartphone irrigation sensor. With empirical
information, a RWS irrigation threshold of 45% was selected
for this crop, due the water needs and sowing season. A sector
of 1100 x 1100 pixels of the image was taken every 0.25 h,
established in the smartphone App (Fig. 8), and when the
percentage was equal or less than the threshold, the pump
irrigates automatically the field during 10 minutes
corresponding to about 0.1 liters per dripper. A restrictive
condition was established to avoid consecutive irrigation
periods preventing excess of water. Subsequently, the three
next soil-images after a trigger irrigation were skipped. After
that if the next image complies with the threshold another
irrigation period is applied. This condition guarantees enough
time for the water to be distributed beneath the soil and appears
in the glass of the sensor.
The RWS value along 24 h for the first days, is shown in Fig.
9, when the irrigation sensor was placed. When the RWS value
reached below the 43% threshold, an irrigation period was
triggered at 13:00 h, then can be noticed that the next value at
13:15 h do not trigger the irrigation period. Several values of
45% were measured until 15:15 h and none of them trigger the
water pump. Daily percentage fluctuation of the RWS during
15 days and the number of irrigation periods are shown in figure
10. The increment for the irrigation periods along the days is
due to plant growth, and an increase of ambient temperature,
approaching the spring season. The irrigation sensor was tested
during 45 days, a total of 157 irrigation periods were applied
giving about 16 liters of water per dripper. The cucurbitaceae
crop was harvested in two occasions, producing 10 kg of
biomass per cultivation bed.
IV. CONCLUSION
A developed smartphone irrigation sensor complied with the
conceived concept of an optically triggered automated
Fig. 8. Android smartphone irrigation App.
Fig. 9. RWS along 24 hours.
03:00 06:00 09:00 12:00 15:00 18:00 21:00
30
35
40
45
50
55
60
Time (h)
RelativeWetSoil(%)
Image 13:00 
Image 13:15

Triggered Irrigation

Fig. 10. RWS fluctuation and irrigation events along several days.
20 21 22 23 24 25 26 27 28 01 02 03 04 05
0
10
20
30
40
50
60
70
80
90
100
Time (day)
RelativeWetSoil(%)

Sensors-12234-2015 6
irrigation using a soil imaging process. Due to rapid growth of
smartphone appliances at affordable prices, this App
represented a simple and practical implementation. The sensor
installation in the field can be done simultaneously with the
preparation of the cultivation beds and irrigation tubes, so there
is no significant additional labor, nevertheless compared with
traditional sensors, the installation in the field requires more
effort and time.
The irrigation sensor has an inherent advantage over other kind
of soil moisture sensors for irrigation purposes. The outcome of
others depend of soil characteristics like: density, compaction,
gravimetry or mixture of their components among others. The
irrigation sensor is of non-contact type, requiring only an in situ
calibration to acquire the dynamic range for any soil type. This
is performed using a dry soil image and another water saturated.
This procedure may represent a disadvantage respect to other
kind of sensors.
The irrigation sensor is a low power consumption standalone
device that can be maintained operative with a small solar panel
and a rechargeable batteries in order to operate for the whole
cultivation period, without the usage of cables or external wired
connections.
The incorporation of a Wi-Fi router node, besides the range
increase of the LAN from the smartphone, allows to connect
other Wi-Fi devices, such as other sensors to increase the
sampling points in the field and by means of the XBee-PRO S2
radiomodem, the range can be extended up to 1.6 km.
The sensor can be used creating networks for large fields or for
uneven cultivation terrains, in such a way that several places
have to be monitored for different RWS values. Also if needed
there are other communication capabilities such as Bluetooth or
directly through a SIM card via SMS linked directly to a URL
site or other smartphone, integrating several versatile possible
applications. If a gateway is not required, the irrigation sensor
can be used alone to trigger remotely an irrigation pump.
ACKNOWLEDGMENT
We are very grateful to Mr. Pedro Luna, Mr. Jorge Cobos,
and Mr. Alfonso Alvarez for their support in the preparation of
the field, the harvest and the construction of the chamber and
router node, with all the electronic components attached.
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Sensors-12234-2015 7
Joaquín Gutiérrez Jagüey received the
Ph.D. degree in Artificial Intelligence from
the Instituto Tecnológico y de Estudios
Superiores de Monterrey, México, in 2004.
He is a Researcher at the Centro de
Investigaciones Biológicas del Noroeste,
S.C. (CIBNOR), La Paz, BCS, México. His
current research interests include the
development and experimental validation of robotic systems for
biological research applications.
Juan Francisco Villa-Medina received
the M.S. degree in computational
engineering from the Instituto Tecnológico
de La Paz, México, in 2013. He is a
technician at CIBNOR. His current
research interests include the development
of engineering systems.
Aracely Lopéz Guzmán received the B.T.
degree in computational engineering from
the Instituto Tecnológico de La Paz,
México, in 2014
Miguel Ángel Porta-Gándara received
the Ph.D. degree in Engineering from the
Universidad Nacional Autónoma de
México, México, in 1997. He is a
Researcher of The Engineering Group at
CIBNOR. His current research interests
include the development of engineering
systems.

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