Automated zone speciﬁc irrigation with wireless sensor actuator network and adaptable decision support.doc

8/15/2019 Automated zone specific irrigation with wireless sensor actuator network and adaptable decision support.doc

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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

http://www.decagon.com/http://www.sensirion.com/http://www.cantherm.com/http://www.sciencedirect.com/science/article/pii/S0168169914000829http://www.sciencedirect.com/science/article/pii/S0168169914000829#b0015http://www.decagon.com/http://www.sensirion.com/http://www.cantherm.com/http://www.sciencedirect.com/science/article/pii/S0168169914000829http://www.sciencedirect.com/science/article/pii/S0168169914000829#b0015


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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


24/28

-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 ;


25/28

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


26/28

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


27/28

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



28/28

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

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Automated zone speciﬁc irrigation with wireless sensor actuator network and adaptable decision support.doc