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Abstract
Precision irrigation based on the “speaking plant” approach can save water and maximize
crop yield, but implementing irrigation control can be challenging in system integration and
decision making. In this paper we describe the design o an adaptable decision supportsystem and its integration with a wireless sensor!actuator network "#$A%& to implement
autonomous closed'loop zone'speci(c irrigation. )sing an ontology or de(ning the
application logic emphasizes system *exibility and adaptability and supports the application
o automatic inerential and validation mechanisms. +urthermore, a machine learning
process has been applied or inducing new rules by analyzing logged datasets or extracting
new knowledge and extending the system ontology in order to cope, or example, with a
sensor type ailure or to improve the accuracy o a plant state diagnosis. A deployment o
the system is presented or zone speci(c irrigation control in a greenhouse setting.
valuation o the developed system was perormed in terms o derivation o new rules by
the machine learning process, #$% perormance and mote lietime. -he eectiveness o the
developed system was validated by comparing its agronomic perormance to traditional
agricultural practices.
/eywords
• #ireless sensor!actuator network0
• I 123.45.6 standard0
• 7ule'based system0
• 8achine learning0
• Adaptive decision'making0
•
Plant'based irrigation
4. Introduction
9iven the advancements in the (eld o wireless sensor networks "#$%s& as well as in the
miniaturization o such sensor systems, new trends have emerged in the (eld o precision
agriculture ":hang et al., 3223 and $rinivasan, 322;&. 7eviews o wireless sensor
technologies and applications in agriculture and ood industry have been given by #ang et
al. "322;& and by 7uiz'9arcia et al. "322
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temperature in strawberries, or sugar'levels in grapes, or the photosynthetic activity o the
crop plant, to provide location'speci(c data could also prove to be very eective.
In particular, the use o #$% technology to optimize irrigation in agriculture is o bene(t to
both the armers and the environment. According to recent reports, agriculture irrigation
accounts or 52>;2? o reshwater usage rom sources in the natural environment and up to
more than
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+ig. 4.
igh'level system architecture.
+igure options
-he unctionality o the backend system is supported by the ollowing main
componentsD Ontology ,Decision Support System "DSS&, and Machine Learning "ML&. -he
ontology speci(es all the rules that support the decision'making process in the orm o a
knowledge base. -he J$$ provides all the synthetic inormation, acGuired rom the analysis
o the stored data, needed to make operative decisions or the plant growth management.
-he purpose o the 8K component is to analyze the structured inormation using machinelearning and data mining techniGues in order to (nd interesting new correlations. A number
o tools have also been developed to support the application development.
3.3. #$A% platorm and sensor!actuator interacing
-he hardware platorm used is the 35 mm mote developed at -yndall "Lellis et al.,
3225 and -yndall, 3246&. -he hardware platorm is analogous to a KegoM'like
35 mm N 35 mm stackable system "+ig. 3&. -he module contains an Atmel A-8ega431K 1'bit
microcontroller and a @hipcon @@3632 :igLee 7+ chip both o which are combined on one
layer. -he microcontroller is eGuipped with 431 /L in'system *ash memory and can be
programmed to handle analogue to digital conversion "AJ@& o sensor data and the
communication networking protocols or interacing with the 7+ transceiver to achieve
communication with other nodes. -he @@3632 transceiver used is 123.45.6 compliant > and
as such can cover 4; channels in the 3.6 9z band. @urrent consumption is very low with
transmit and receive currents typically 4F.6 mA and 4
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35 mm module. n the sotware side, the microcontroller runs a tailored version o -iny$,
an optimised operating system that allows ast con(guration o the sensor nodes. -he power
layer may include batteries or other energy supply or power harvesting mechanisms, i.e.,
solar cells or piezo electric power generation mechanisms. An 35 mm Ki'ion battery layer is
also provided with built'in )$L charger capability. In an early version o the system the
authors have investigated an alternative con(guration o the -yndall35 mote regarding the
communication layer "%ordic K$I n7+3624 3.6 9z 7+ transceiver& and the network
topology "peer'to'peer& "9oumopoulos et al., 322F&.
+ig. 3.
-yndall35 mote modular platorm.
+igure options
2.2.1. Communication protocol an topology
-he communication protocol used in our case is based on the I 123.45.6 standard "I
123.45.6 $tandard, 322;& which speci(es the physical layer and the 8edium Access @ontrol
"8A@& layer o the protocol. I 123.45.6 combined with the :igLee open speci(cation
speciy a protocol stack or the development o short'range and low power communications
or #ireless Personal Area %etworks "#PA%s&. -he basic con(guration o the I 123.45.6
permits a transer rate o 352 /bps to a distance range o 42>422 m in the 3.6 9z
reGuency band depending on the antenna, the environment and the power consumption
permitted by a given application. -he I 123.45.6 standard supports two addressing
schemes, either short "4; bit& or long addresses "I ;6 bit& so theoretical network size is
up to ;55C; or 3;6 nodes. A maximum rame size o 43F bytes is supported with a payload o
up to 446 bytes "assuming short addresses&. An I 123.45.6 network consists o one PA%
coordinator and a set o devices which are classi(ed as reduced unctionality devices "7+J&
and ull unctionality devices "++J&. -he interconnection o these devices allow the creationo three types o topologiesD star "the PA% coordinator is in the transmission range o all
other devices resulting in single'hop communication&, mesh or peer'to'peer "a node may
communicate with any neighbor enabling multi'hop communication& and cluster'tree "a
combination o the previous topologies where the PA% coordinator is the root o the tree and
all the non'lea devices are de(ned as coordinators with the ability to orward the packets
to!rom the root&.
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-he physical layer o I 123.45.6 uses @arrier $ense 8ultiple Access "@$8A& with @ollision
Avoidance "@A& to access the radio channel "I 123.45.6 $tandard, 322;&. -he 8A@ layer
enables two dierent operational modesD non beacon'enabled mode and beacon enabled
mode. In the ormer case the access control is governed by non'slotted @$8A!@A, where as
in the latter case the network coordinator broadcasts a special rame "a beacon& periodically
that permits the synchronization o the associated devices.
In our current system we use the non'beacon mode and thus our network topology is a star
topology. $electing the non'beacon mode was mandatory due to the unavailability o a
beacon'mode implementation or our #$A% platorm. In the star topology the coordinator
and the actuator controlling motes are powered rom the mains source, where as the sensor
motes are battery powered. 9iven that the plant processes we want to monitor and control
"e.g. plant dehydration& are slow, the use o low sampling intervals "in the order o 5>C2 min&
is acceptable to save energy. In addition, the data to be transmitted is o low complexity
resulting in limited payloads on the I 123.45.6 data rames. A sampling rate o 5 min with
a payload o only 41 bytes "total data rame size CC bytes& gives a sampling rate o 2.11 bps
which is very low compared to the medium transmission rate "352 /bps&. -his low samplingrate aects the collision probability and allows achieving a high successul packet delivery
rate or a suHcient number o nodes provided that nodes can awake in a random manner
during the speci(ed sampling interval to take measurements and transmit their values, as
we will explain in the evaluation part o the sensor network. -he protected environment o
greenhouses provides also the possibility o using a range o acilities like mains power or
certain devices "e.g. controlling o water pumps&. Ly using the star topology we have
avoided well known complications that are related with the use o the beacon'enabled mode
such as clock drits between coordinators in cluster tree topologies, dynamic network
resynchronization in the case a cluster =oins!leaves the network and the need or dynamic
rearrangement o duty cycles in the case o a coordinator ailure.
2.2.2. Sensors
-he interacing o commercial'o'the'shel "@-$& sensors generally reGuires special
hardware or each sensor. -his is because dierent sensors may have dierent power
reGuirements and output type and range. -hree types o sensors had to be interaced or the
-yndall35 moteD soil moisture sensors, humidity sensors and thermistors or determining
lea!air temperature. -he main properties o these sensors are summarized in -able 4.
-able 4.
$ensors interaced to the 35 mm mote.$ensor model @ @'42 $-44 8+5442CC
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$ensor model @ @'42 $-44 8+5442CCC2 s 4 s
@urrent
@onsumption
3 mA measuring 2.55 mA measuring 2.21 mA measuring
2.C OA sleep
$upply voltage
range
3.5>5.5 3.6>5.5 3.5>5.5
utput type oltage "42>62? o excitation
voltage&
Jigital "3'wire& 7esistance "414.F>2.;F /V&
@ost 422 W 32 W 5 W "including conditioning
circuitry&
)7K httpD!!www.decagon.com httpD!!www.sensirion.com httpD!!www.cantherm.com
-able options
2.2.2.1. Soil moisture sensor
$oil moisture can be measured by electromagnetic sensors which determine volumetric
water content "#@& and occasionally electrical conductivity in the soil under consideration.
-he correlation between the electromagnetic signals measured and #@ is attributed to the
high permittivity o water which can be inerred by the sensors through various means "e.g.,
time, reGuency and capacitance&. In our case the @ @'42 soil moisture probe by
Jecagon was selected. It uses the capacitance techniGue to measure the dielectric
permittivity o the surrounding medium which can then be related to the #@ o the soil.
-he @ @'42 sensor provides ast measurements with very low power consumption,
giving the ability to take many measurements over a long period o time "e.g., a growingseason& with minimal battery usage. 7egarding the accuracy o the measured #@ the
manuacturer recommends establishing soil'speci(c calibration unctions "@ampbell, 3226&.
-he soil used in our applications was a peat substrate. A series o soil!water mixtures were
used and the corresponding sensor responses were recorded. -he process was perormed or
dierent sensors and the average values o C repetitions were used to obtain the ollowing
eGuationD
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eGuation"4&
-urn 8athXaxon
$oil moisture sensor outputs could be input directly into the analogue to digital converter
because they are in the voltage range o the microcontroller.
2.2.2.2. Humidity sensor
+or measuring humidity we have selected the $-44 component rom $ensirion which also
provides temperature measurements. A capacitive sensor element is used or measuring
relative humidity while temperature is measured by a band'gap sensor. -he device also
integrates signal processing and provides a ully calibrated digital output. -o obtain the
relative humidity we used the accuracy enhancement ormula that is provided by the
manuacturer "$ensirion, 3244&D
eGuation"3&
-urn 8athXaxon
where SO!" is the humidity readout value "43'bit length&, C4 Y Q3.26;1, C3 Y 2.2C;F
andCC Y Q4.5
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a nominal resistance o 42 kV at 35 R@ "@antherm, 322;&. -he measurement system we
have designed achieves a temperature resolution o 2.23 R@ and an accuracy o U2.25 R@
over a temperature range o 5>65 R@. -he resolution and accuracy o temperature
measurement is critical or the precision irrigation application we developed and thereore
we have careully designed the corresponding system. 8oreover, higher resolution and
accuracy are reGuired or the machine learning experiments that will be described later in
the paper.
A characteristic o thermistors is the non'linear relationship between thermistors resistance
"!th& measured in ohms, and temperature "# & measured in /elvin, as given by the well'known
$teinhart>art thermistor eGuation or the simpler L'parameter eGuationD
eGuation"C&
-urn 8athXaxon
where !2 is the resistance value at reerence temperature # 2 "typically 35 R@!3# curve "the higher the Leta
value the greater the change in resistance per degree @&. Loth !2 and $ are speci(ed in the
sensor data sheet. ![ is the thermistor resistance as the temperature approaches in(nity.
G. "C& solved or # is written asD
eGuation"6&
-urn 8athXaxon
-he measurement system provides temperature values in three stepsD
4.
8easurement o the conditioning circuit output voltage by means o the AJ@ module.
3.
@alculation o the thermistor resistance rom the AJ@ value.
C.
@alculation o the temperature using G. "6&.
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In order to obtain both high precision and high resolution temperature values by the
measurement system several design choices have to be made in the above three steps. A
conditioning circuit is used to interace thermistors to the AJ@ module o the -yndall35 mote
working with an external reerence voltage o 3.5 "% re& & or 43'bit AJ@. -he conditioning
circuit includes our high precision resistors "2.4? tolerance& and a high precision
operational ampli(er "op'amp& with low noise and low oset voltage that scales and shits
the analog signal o the thermistor voltage in order to correctly map the reGuired thermistor
resistance range to the voltage range "2>3.5 & supported by the AJ@ module. -o overcome
the impact o the circuit component uncertainties and get highly accurate measurements
careul calibration was perormed, matching the output o the measuring system to a set o
known reerence values. Jetails o this calibration process are omitted due to lack o
space. +ig. C shows a plot o the calibration data and also the thermistor resistance variation
compared to the temperature variation. -he (gure shows also the linear (t o the curves that
could be used or applications that can aord lower accuracy measurements.
+ig. C.
@alibration data plot.
+igure options
2.2.'. (rrigation system
+ig. 6 shows the schematic layout o the irrigation distribution unit. In principle, the moteBs
controlling sotware, via a transistor switch, activates a relay that in turn activates the pumpdevice. -he interace layer sits on top o the -yndall35 mote as additional layers cannot be
placed above it due to the size o the connectors attached to it. In order to toggle the
external relay, which is part o the actuator portion, 43 must be supplied at the interace
layer output. -he heart o the actuator portion o the interace layer is a simple transistor
switch that is controlled by the microcontroller. #hen the sotware running on the
microcontroller toggles the appropriate output a voltage is supplied to the base o the
transistor and the switch is turned on and 43 is supplied to the relay which is connected to
a arwin 8C2';42 connector. -he 43 supply voltage is used not only or the actuators but
also to power all possibly connected sensors and the entire -yndall35 mote. -he interace
layer contains a -orex \@;323 voltage regulator to regulate the 43 supply down to C.C inorder to power the mote and connected sensors.
+ig. 6.
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$chematic layout o the irrigation distribution unit.
+igure options
In order or this system to work the mote has to take commands rom the coordinator node
so as to know what section to irrigate and or how long. -he coordinator sends a command
via its radio link to the associated node controlling the actuator. -his command is sent to themodule in the irrigation distribution unit that converts the signal to a voltage level that the
microcontroller can read. nce the microcontroller receives a valid command according to
the decision making output it will actuate the pump valve and open a particular solenoid in
order to irrigate a speci(c section o the crop layout. -he solenoid operated water valves
distribute the watering supply in our distinct zones o plants. A (th water valve provides
humidity control. -he pumps used in the system are the #hale #hisper*o )P2145 pressure
pump with a *ow rate o 1 K minQ4. -he solenoids are manuactured by Lermad, model $'
C
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exploits the Xess "Xava xpert $ystem $hell& rule engine "httpD!!herzberg.ca.sandia.gov!=ess!&.
-he execution o this module starts based on the initial acts and the rules stored in the rule
base using @KIP$ ormat. -he concepts that appear in the rules have emerged rom the
ontology. ssentially, this is an approach o building rules on top o ontologies. -hereore, the
reasoning is based on the de(nition o the ontology, by using (rst'order predicate calculus.
-he user, however, can de(ne or update existing rules using a ront'end tool and expressing
the rules with simple i'then'else logic.
A number o tools have been developed to acilitate the development, con(guration and
monitoring o applications. +or each node in the #$A% we provide a driver operator
con(guration sotware that speci(es the actions to setup the properties o the #$A% in
order to make it unctional or the agricultural application. -his con(guration provides also
the de(nition o certain parameters that will allow the proper interpretation o the data
received by the device operator. +ig. ; illustrates the interace de(ned or con(guring the
-yndall35'based #$A%. ptions include setting the mote to be con(gured, its reGuency
channel and position in the (eld, the active AJ@s and the type o the associated
sensor!actuator.
+ig. ;.
-yndall35'based #$A% driver con(guration sotware.
+igure options
+ig. F shows the design o the ^eat $tressB calculation rule or the irrigation application
using a simple rule editing tool targeted to the domain expert, which provides a visualinterace based on a node connection model. -he rule consists o three conditions combined
with a logical A%J node. An expression builder acilitates the de(nition o the condition
relying on concepts stored in the ontology. -he rule, as designed, states that when all
conditions are met then the heat stress state o the 7@ area must be set to active
"eat$tress A@-I&. -he $upervisor Kogic and Jata AcGuisition "$KAJA& tool is another tool
used to view knowledge represented into the ontology, monitor and log plant!environmental
parameters and manage dynamically the rules taking part in the decision'making process in
co'operation with the rule editor. -he rule editor and the $KAJA tool are described in detail
in 9oumopoulos et al. "322F&.
+ig. F.
)sing the rule editor to compose a rule or the irrigation application.
+igure options
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3.6. 8achine learning
In the context o precision agriculture it is easible to extract new knowledge rom stored
data in the orm o models that are in an easy to manage orm by the decision making
system and understandable to the domain expert involved in the crop production process
"Jimitriadis and 9oumopoulos, 3221&. laborating on our previous research work, we usedclassi(cation algorithms, which produce a classi(er as a set o rules or decision trees that
can be then exploited to predict the classi(cation o new data cases and can insert new rules
to the domain model. Leore that, the application o clustering algorithms based on
particular proximity criteria'attributes can create hypotheses about the relationships that
can be ound in the dataset and identiy the natural groupings in the input data.
#e devised a machine learning process model that guides machine learning
experimentation with an aim to incorporate derived rules and attributes into the decision'
making mechanism "+ig. 1&. -his process is an expanded orm o the process model or
machine learning application in agriculture that was created by the #aikato nvironment or
/nowledge Analysis "#/A& group in )niversity o #aikato in %ew :ealand "#itten and
+rank, 3225&. -he close cooperation between the data mining expert and the plant science
(eld expert is reGuired in several phases. In the pre'processing and analysis phases, their
collaboration will shape the datasets upon which the machine learning algorithms shall
operate. -he responsibility o the (eld expert is to review and interpret the appropriateness
o the data and suggest possible transormations and!or relaxations. -he responsibility o the
data mining expert is to guarantee that the inerred rules correspond satisactorily to the
evaluated measures "e.g., overall success rate and alse positive rate& rom the machine
learning perspective. 7egarding the data mining process, algorithms provided by the #/A
workbench are used. At the post'processing phase the (eld expert could indicate which
subset o the derived rules establishes new valuable knowledge, and which part describes
common knowledge. -o estimate the perormance o classi(ers generated rom the entire
data set o example cases, the 42'old cross validation approach to training and testing was
used.
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+ig. 1.
8achine learning process model.
+igure options
C. xperiments and results
$trawberry plants "+ragaria ananassa& have been selected or system validation and
evaluation because the delivered technology can be relevant to the commercial production
o this crop, while on a practical level, the size o the leaves enables easy attachment o
sensors. In addition, irrigation control on strawberry plants is important as they have a
shallow root system making them particularly sensitive to water stress. n the other hand,
controlling excessive irrigation is signi(cant or avoiding nutrient leaching and disease
development that aect negatively the crop yield. -he most eective balance needs to be
achieved between these two reGuirements. -he irrigation treatments, controlled by our
system, were imposed rom the beginning o the *owering to the end o the ruit maturity
rom early Xune to late Xuly in a greenhouse establishment at the )niversity @ollege @ork in
Ireland "+ig.
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+ig.
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sending brie pulses o light to a plant. A healthy plant responds to this light very Guickly
"within microseconds& by re'emitting some o the light energy as *uorescence which is
detected by the *uorometer. @+ measurements are taken using a standalone sensor device
"Xunior PA84&, whereas P-, A-, and $8 measurements are taken by the #$A%.
C.3. $ystem deployment
-he experimental setup consists o an array o
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between character transmissions, whereas a delay o up to 42 s is necessary between
commands.
-able 3 contains the application rules or the 7@ zone with thresholds specialized or the
reproductive phase o strawberries. It is possible the threshold values to vary depending on
the growing phase o the crop. In the strawberry ruiting stage, or example, more irrigationis reGuired or Guality production. In -able 3 we have shown a separate rule that de(nes the
variable !CSM#hreshol to the proper threshold value during the reproductive phase o the
crop. $imilarly, another rule de(nes the variable (rrigation#hresholwith the proper value in
the same growing phase. %ote that the $8 threshold can be de(ned to be both zone'speci(c
and growing phase'speci(c, where as the irrigation threshold is de(ned to be the same or
all zones. xisting knowledge rom the horticulture literature can be easily integrated into
our system through the ontology and rule editing tools discussed earlier. -he uncertainty o
the decisions can be modeled with con(dence actors that can be integrated into the rules
" 9oumopoulos et al., 322F&.
-able 3.
Application rules.
7ule Lody
7@Jrought$tres
s
I+ 7@Kea-emperature > 7@Ambient-emperature _ 2.< R@
-% 7@Jrought$tress ` -7) K$ 7@Jrought$tress ` +AK$
7@eat$tress I+ 7@Jrought$tress A%J 7@$oil8oisture _ 7@$8-hreshold
-% 7@eat$tress ` -7) K$ 7@eat$tress ` +AK$
7@%eedIrrigation
I+ 7@Jrought$tress A%J %- 7@eat$tress
-% 7@%eedIrrigation ` -7) K$ 7@%eedIrrigation ` +AK$
7@%eed8isting I+ 7@Jrought$tress A%J 7@eat$tress
-% 7@%eed8isting ` -7) K$ 7@%eed8isting ` +AK$
7@$8-hreshold I+ 9rowingPhase Y 7P7J)@-I
-% 7@$8-hreshold ` 2.;
Irrigation-hresh
old
I+ 9rowingPhase Y 7P7J)@-I
-% Irrigation-hreshold ` 4122 s
-able options
-wo additional parameters must be de(ned or the prototype to be properly workingD the
duration o irrigation!misting and an idle time, which speci(es the amount o time the rules
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should be disabled, ater the action is perormed. -his is to allow the ecosystem to absorb
the changes. -he values used or the example application were C2 min!4 min and 6 h
respectively. -he irrigation system uses a reservoir, a pump system, standard pipe work,
nipples and drippers which emit water directly into the plant pot. +low rate depends on the
pump pressure. -he emitter *ow rate per pot is regulated at 4 K hQ4 with an application
eHciency o
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be in a ully operational orm. -o alleviate this speci(c issue we need to con=ugate the
condition part o rules 6 and 5 with the term “$8 _ ;2” in order to dierentiate between
J$ and $ assessment.
-able C.
Part o the rules derived by running machine learning algorithms.
Inerred rule
@orrectly
classi(ed "?&
4 IF "(n&)A! f C4
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instances belonging to cluster C reGuested irrigation and C? reGuested misting. All other
instances were stated as healthy.
+ig. 44.
@lustering o the “8ultiParameter@orrelation” dataset.
+igure options
C.6. #$% perormance
In this section we analyze the #$% perormance ocusing mainly on the estimation o the
packet loss rate in terms o the number o motes and the duty cycle o the system. -he duty
cycle is de(ned as the ratio o the time reGuired to sense and transmit a sample o the
sensors attached to the mote to the sampling period. #e assume a star topology ormed bya set o sensor motes and a coordinator powered rom the main source. #e assume a steady
state network where child nodes have already been associated to the coordinator node. -he
unslotted @$8A!@A protocol is used or the channel access, brie*y described next. -wo
variables are used or each transmission attemptD number o backo retries "N$& and backo
exponent "$&. In the beginning these variables are initialized as N$ Y 2
and $ Y macM(N$ "deault value is C&. N$ represents the number o backo retries beore
assuming a channel access ailure having a range between 2 and 5. -he 123.45.6 8A@ layer
uses $ to choose a random backo between 2 and 3L > 4 to delay the @lear @hannel
Assessment "@@A& phase. $ has a range between 2 and 5 "setting $ to 2 means @$8A!@A
is switched o&. Ater the backo period the 8A@ layer issues a @@A and i the channel isclear transmission can start. I the channel is ound to be busy, both N$ and $ are
incremented by 4 and a new backo is attempted. I N$ exceeds the maximum threshold
"macMa4CSMA$acko5s Y 5& then transmission is aborted. Ater the transmission the node
waits or an ack rame. I the ack is successully received, the transmission is considered
successul.
ur analysis is based on the analytical models developed by -immons and $canlon "3226& to
describe the perormance o the I 123.45.6 protocol ocusing on the estimation o the
lietime o an 123.45.6 network o sensors in a star topology. +or a network o n sensors the
probability that the channel is clear in the @@A phase ater the (rst backo interval is given
by the ollowing ormulaDeGuation"5&
P @ @Y"4'G& n ' 4
-urn 8athXaxon
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where 6 is the probability o a node transmitting at any time. -his probability depends in our
case on thesampling perio inter7al "S)( & de(ned or taking measurements and the payload
size o the data rame. #e do not take into account the polling messages rom a sensor node
to the network coordinator since these messages are very sparse "a pooling message is sent
every measurements, approximately three times a day&. -he total size o the data
rame is CC bytes "41 bytes payload, < bytes 8A@ headers and ; bytes P headers&. $o 6 is
calculated as ollowsD
eGuation";&
-urn 8athXaxon
where # D+ is the data rame transmission time.
-o estimate the packet loss rate we need to take into account the cumulative probability to
(nd the channel clear so that packet transmission can happen ")tr & by taking into account up
to (ve backo intervals as de(ned in the 123.45.6 @$8A!@A protocol and the probability o
packet collision due to the act that two or more nodes can select the same delay and
transmit simultaneously. )tr is de(ned as ollows " -immons and $canlon, 3226&D
eGuation"F&
-urn 8athXaxon
9iven that the probability o selecting the same backo delay leading to collision is we
can approximate the packet loss rate as ollowsD
eGuation"1&
-urn 8athXaxon
-he analysis shows that or a large number o sensors "up to 4222& with a low duty cycle
"2.26? corresponding to an S)( o 5 min& both the probabilities o the channel being ree or
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transmission and o not having a collision are greater than
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$ e n s eYj I c j t s
-urn 8athXaxon
where % is the battery voltage "C.C &, (c is the current consumption to sample a sensor
and t s is the total time to sample a sensor during a time interval # . Assuming a sampling
period interval S)( then where t m is the measurement time or each sampling.
+or each sensor the (c and t m values are de(ned in -able 4. Assuming S)( Y 5 min -able
6 summarizes the energy consumption or the mote. In every cycle the sensing and the
transmission operations are taking place only once. -he duty cycle "DC& o the service is
measured at 2.26? and the average power consumption is approximated by the ollowing
eGuationD
eGuation"44&
P a v gY P a c t i v e jDC P s l e e p "4'DC&
-urn 8athXaxon
where )acti7e is the sensing and transmission power consumption and )sleep is the sleep mode
power consumption. +rom -able 6 the average power consumption is estimated to 454 O#.
Kietime o the mote can be computed as where C$A## is the battery capacity
"expressed in mAh& and is the average energy given in -able 6. )sing a C.C Ki'ion battery
o 522 mAh the lietime o the mote will be 4.3 years whereas a battery o 4322 mAh will
achieve a 3.< years lietime. -hereore, the mote lietime can easily satisy the ull crop
agronomic cycle o strawberries which is 322 days.
-able 6.
nergy consumption measurements.
nergy "mX& Power "m#& -ime "ms& I "mA&
$ense ;.5F 1
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-able options
C.;. Agronomic impact
n the agronomic part o the experiment the instrumentation o the strawberry (eld with the
#$% and the plant'driven irrigation resulted in a notable reduction in water consumption"32?& with respect to traditional agricultural practices involving user de(ned timed
irrigation based on rules o thumb "twice or thrice a week or 4>3 h&. -he latter was applied
in a parallel setup or the same growing period o the crop. -he irrigation treatments were
imposed rom the beginning o the *owering to the end o the ruit maturity rom early Xune
to late Xuly.
$ince the ob=ective o any crop cultivation is to achieve the highest yield, crop yield is the
most eective way to evaluate the bene(ts o crop growth systems. -hereore we compared
the plant'driven irrigation system with the traditional irrigation scheme, regarding
strawberry growth, in terms o crop yield, water use eHciency and other yield parameters. In
our experiment, the strawberry harvesting stage started about 31 days ater *owering andthere were ten harvests that took place every C days. -he highest Guality o the crop was
noticed in the (rst rounds. At each round o the harvest period the yield, the number o
strawberries and the average weight per strawberry were recorded. -he dry weight o
strawberries was also determined by drying them at 12 R@ in an oven. #ater use eHciency
was also determined or each case as the ratio between yield and the water amount used.
$tatistical analysis o the collected data was perormed by standard analysis o variance
"A%A& with the $tatistical Package or the $ocial $ciences or #indows. -he irrigation
treatments were run as one'way A%A.
-able 5 summarizes the comparison o the two irrigation treatments. -here was no
statistically signi(cant dierence between the yields achieved by the two irrigation
treatments. -raditional irrigation produced a slightly higher yield than the plant'driven
irrigation, which can be explained by the excess amount o water provided. Jry strawberry
yields were similar as were the average number and average weight o strawberries. n the
other hand, the water use eHciency o the plant'driven approach was signi(cantly better
than o the traditional approach. -hereore the plant'driven approach with the system
con(guration discussed in this paper was successul in providing the proper amount o water
or the physiological growth o the crop producing similar crop yield with the traditional
irrigation system.
-able 5.
@rop Guality indicators or the dierent irrigation treatments.
Irrigation
treatment ield "g!plant&
Jry weight
"g!plant&
#ater amount
"lt!plant&
Avg berry weight
"g&
#ater use eHciency
"g!lt&
-raditional ;
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Note8 8eans ollowed by the same letter are not signi(cantly dierent at the 2.25 probability level.
-able options
C.F. 7elated work and discussion
ver the years there has been a tremendous progress on the application o #$% technologyin the precision agriculture domain at various orms. In the work o Peres et al. "3244&, or
example, an Intelligent Precision Agriculture 9ateway was developed that can provide
middleware services between on site deployed #$%s and remote locations. Jata are
gathered by :igLee operated #$%s that eed a local database which can be then Gueried by
authorized remote clients. )sability and scalability issues are in the ocus o their research.
As another example, in the work o 9arcia'$anchez et al. "3244& an integrated #$%'based
system or crop monitoring and video surveillance was developed in a distributed
environment using cost eective communication technology "I 123.45.6&. nergy
consumption, end'end transmission delays and network synchronization are some o the
main eatures that have been addressed during system design by the authors. Irrigation
scheduling based on #$%s has been proposed by ellidis et al. "3221&, by /im et al.
"3221& and by Pardossi et al. "322
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prototype presents common eatures with the system we have developed but also has
signi(cant limitations regarding the size o deployment, the accuracy o measurements "no
calibration o the used sensors is perormed&, the lack o supporting tools or application
development and the lack o an overall system evaluation.
7ule'based systems or irrigation management have been proposed in the (eld o
agriculture in the orm o expert systems " -homson and 7oss, 4
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error'prone process o acGuiring knowledge rom the gathered data. alidation o the
inerred knowledge, however, will reGuire an iterative approach where the situation observed
is repeated and thus a sae conclusion can be reached. $econdly, there is a need o a two'
way interaction between the domain expert and the domain modeler. -heir collaboration is
crucial to transorm the raw data received rom the sensors into the (nal datasets to be used
by the machine learning algorithms. Additionally, new rules inerred must be validated or
their relevance by the domain expert and checked or consistency and redundancy with the
existing ones. In principle, part o the consistency and integrity checks could be managed
automatically through the rule'based reasoning engine by de(ning special con*ict detection
rules.
An upgrade o the agronomic evaluation perormed would be a comparison o our system
with modern irrigation scheduling practices based on the estimation o crop
evapotranspiration "-c.&. ere, the main idea is to balance the amount o water taken away
through evapotranspiration with the amount o water to be applied. -c. can be calculated
using various weather parameters obtained by a weather station, statistical data and models
developed or predicting -c. $uch models are integrated in relevant decision support
systems or the daily management o irrigation. A more simpli(ed approach to calculate
-c. is by using a @lass A evaporation pan which relates the measured evaporation to crop
water use. In that case, an appropriate crop coeHcient "/ c& must be applied to determine
-c. Accurate / c values, however, depend on various site'speci(c parameters "e.g., soil
characteristics, crop physiology, development stage, etc.& and are oten diHcult to establish.
-he additional complexity o developing an -c.'based irrigation scheduling system rom
scratch prevented us rom making such a comparison.
Plant'based methods or irrigation scheduling do not indicate directly the amount o water to
be applied and experimentation is reGuired to determine control thresholds " Xones, 3226&.
Accordingly, it is not possible to use the plant temperature to stop irrigation due to the time
lag between applying the irrigation, the permeation through the substrate and the
subseGuent uptake o the water by the plant. -o alleviate this shortcoming, plant'based
sensors are combined with soil moisture measurement sensors that can indicate when to
stop the irrigation. -hresholds then are established through experimentation where irrigation
treatments are tested over a range o sensor values to identiy the best case. In such
experiments the thermistors were used to commence the irrigation, and the soil moisture
probes were used to determine when adeGuate water had been added to the substrate. Ater
experimentation the average irrigation time to reach the ideal ;2? o water content ater
the identi(cation o a water stress was ound to be C2 min.
6. @onclusions
#e have been involved with a acet o precision agriculture that concentrates on plant'
driven crop management. Ly monitoring soil, crop and climate in a (eld and providing a
decision support system that is able to learn, it is possible to deliver treatments, such as
irrigation, to speci(c parts o a (eld in real time and proactively. #e have presented in this
paper an integrated ramework consisting o hardware and sotware components as well as
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tools that support eHciently the development o an autonomous #$A%'based system or
precision irrigation in greenhouses.
+ertilizer and pesticide treatments are other examples o applications in agriculture that
could bene(t rom such proactive approaches.-he integration o a chlorophyll content meter
sensor would allow system upgrading to incorporate ertigation "supply o ertilizer via theirrigation system&. In that case the system ontology will need to be updated with appropriate
rules to determine where!when exactly a ertilizer "e.g. nitrogen& is reGuired. -he availability
o sensors or the detection o plant'emitted volatile organic compounds "@s& including
ethylene "which signals general stress&, esters o =asmonic acid "which signal pest attack&,
and esters o salicylate "which signal pathogen attack& would allow the issue o inection
alerts and actuate a pathogen or pest'speci(c response as appropriate. Ly integrating to our
platorm electronic nose technology it is possible that volatile sensor arrays could detect the
presence o speci(c pathogens or pests.
8oving our research towards a more autonomous system with sel'adaptation and sel'
learning characteristics, we have been exploring ways o incorporating learning capabilitiesin the system. ur experiments have shown that machine learning algorithms can be used
or inducing new rules by analyzing logged datasets to determine accurately signi(cant
thresholds o plant'based parameters and or extracting new knowledge and extending the
system ontology.
-o deal with the uncertainty o data, work is in progress to de(ne a model describing the
uncertainty aspects. uality indicators can be speci(ed so that the end'user "either an
application or a person& can make =udgements on the con(dence level that the inormation
entails. )ncertain context mechanisms such as probabilistic logic, uzzy logic and Layesian
networks can be evaluated and applied accordingly.
Acknowledgments
Part o the research described in this paper was conducted in the PKA%-$ pro=ect "I$- +-
pen I$-'3224'C1