BASE Biotechnol. Agron. Soc. Environ.201620(S1),
AreviewontheuseofsensorstomonitorcattlejawmovementsandbehaviorwhengrazingAndriamasinoroLalainaHerinainaAndriamandroso(1,2,3)*,JérômeBindelle(3)*,BenoîtMercatoris(2),FrédéricLebeau(2)(1)UniversitédeLiège-GemblouxAgro-BioTech.TERRA.AgricultureIsLife.PassagedesDéportés,2.BE-5030Gembloux(Belgium).E-mail:[email protected](2)UniversitédeLiège-GemblouxAgro-BioTech.PrecisionAgricultureUnit.PassagedesDéportés,2.BE-5030Gembloux(Belgium).(3)UniversitédeLiège-GemblouxAgro-BioTech.PrecisionLivestockandNutritionUnit.PassagedesDéportés,2.BE-5030Gembloux(Belgium).
*Theseauthorscontributedequallytothispaper.
ReceivedonApril1,2015;acceptedonMay24,2016.
Introduction.PrecisionLivestockFarming(PLF)isspreadingrapidlyinintensivecattlefarms.Itisbasedonthemonitoringofindividualsusingdifferentkindsofsensors.Appliedtograzinganimals,PLFismainlybasedontherecordingofthreeparameters: the location, the posture and themovements of the animal.Until now, several techniques have beenused todiscriminate grazing and ruminating behaviors with accuracies over 90% on average, when compared to observations,providingvaluabletoolstoimprovethemanagementofpastureandgrazinganimals.However,bitesandjawmovementsarestilloverlooked,eventhoughtheyareofutmostimportancetoassesstheanimalgrazingstrategiesforvariouspasturetypesanddevelopfuturetechniquesallowingbetterestimationoftheirintake.Literature. The goal of this review is to explore the possibility of monitoring the individual jaw movements and thedifferentiation of bites in grazing animals. For this purpose, (1) themechanisms of forage intake in cattle are explainedbrieflyinordertounderstandthemovementsperformedbythecow,especiallyduringgrazing,(2)thevarioussensorsthathavebeenproposedtomonitorjawmovementsofruminantssuchasmechanicalsensors(pressuresensors),acousticsensors(microphone)andelectromyographysensorsarecomparedand(3)finallytherelationshipbetweenjawmovements,bitingbehaviorandforageintakeisdiscussed.Conclusions.Thereviewclearlydemonstratedtheabilitiesofmechanical,acousticandelectromyographysensorstoclassifythe difference types of jawmovements.However, it also indicated awide range of accuracies and different observationwindowsrequiredtoreachtheseaccuracieswhencomparedtotheobservedmovement.Thisclassificationpurposecouldleadtoabetterdetectionofmorespecificbehavior,e.g.bitedetection,andtheirexactlocationonpasture.Keywords.Precisionagriculture,livestockmanagement,cattle,sensors,movements,forage.
Synthèse sur l’utilisation de capteurs pour le suivi des mouvements de mâchoire et du comportement de bovins au pâturageIntroduction.L’élevagedeprécisionse répandrapidementauniveaudesexploitationsbovinesde type intensif. Ilutilisedifférents capteurs pour suivre chaque individu présent dans le troupeau. Pour les ruminants au pâturage, le système estbasésurl’enregistrementdetroisparamètres:leurlocalisation,leurpostureetleursmouvements.Lestechniquesactuellespermettentdedétecterlescomportementsdepâturageetderuminationavecuneprécisionmoyennesupérieureouégaleà90%,comparéeauxobservations.Cestechniquespeuventfournirdesoutilsintéressantspouraméliorerlagestiondelaprairieetdesanimaux.Cependant,lacaractérisationdesmouvementsdelamâchoireetdesbouchéesrestesouventnégligée,sachantquecesparamètrespeuventêtretrèsimportantspourévaluerlesstratégiesdepâturagedesanimauxetpourespérerestimerlaquantitédefourrageingérée.Littérature.L’objectifdecette synthèseestdediscuterdes techniquesutiliséespour lacaractérisationet laclassificationdesmouvementsde lamâchoireainsiquedesbouchéeschez les ruminants.Pourcela, (1) lesmécanismesd’ingestiondefourragedesbovinssontd’abordbrièvementexpliqués,ensuite(2)lesdifférentstypesdecapteursutiliséspourdétecterlesmouvementsdelamâchoire,telsquelescapteursdepression,accéléromètre,microphonesetcapteursélectromyographiques,sontdécritsetcomparés,et(3)leséventuelsliensentrelesmouvementsdelamâchoire,labouchéeetlefourrageingérésontdiscutésensebasantsurlesrésultatsderecherchedéjàeffectuésdanscedomaine.
2 Biotechnol. Agron. Soc. Environ. 201620(S1), AndriamandrosoA.L.H.,BindelleJ.,MercatorisB.C.N.etal.
1. INTRODUCTION
Sensors enable the monitoring of many physicalvariables.Their use in Precision Livestock Farming(PLF)hasincreasedrapidlyoverthepastdecadeforresearch purposes and also in on-farm applications.Unlike more traditional livestock managementmethodswhichfocusontheherd,PLFisbasedon:–themonitoring of variables at the individual level(Hostiouetal.,2014)andatanappropriatefrequencywithreliablesensors;
–the development of predictive models describingtheanimal’sresponsestoenvironmentalstimuliforeachmeasuredvariable;
–thecomparisonofthepredictionmodelswithwhatisactuallymeasuredthroughthesensors.
Theultimategoalistosuggestmanagerialoptionsto the farmer. Measuring such variables requirestrade-offs between upstream data acquisition athigh frequency, while preserving battery life andconsideringmemory limitswhichare specific to thesensorsthatareused,anddownstreamoutputaccuracyobtainedusingadequatedatatreatmentmethods.
In this respect, sensors can be used individuallyor in combination to track, detect, classify, manageand possibly control animal movements andbehaviors.Monitoringruminantbehavior isakey tounderstandinghowanimals fulfill their requirementsinpastoralsystemsbygrazingadynamicvegetationto achieve optimal plant production, animal forageintakeandperformances(Carvalho,2013).Traditionalmanagerial tools are limited to adjusting stockingrates and occupation times in rotational grazing,supplementingtheanimalsandcontrollingconcentrateintake at herd or individual level and indirectmonitoringofforageintakethroughmilkproduction,growth performance, or pasture disappearance(Holechek et al., 2011; Carvalho, 2013). PrecisionLivestock Farming opens a wide range of newperspectives in both intensive pasture and extensiverangelandmanagementbyfocusingontheindividualinsteadofthewholeherd.Forexample,Laca(2009)proposed a system inwhich both animal health andplant-animalinteractionsaremonitoredbycombininganimalpositionandbehaviordatatoremotelymanageindividualhealthandfeeding.
Controllingindividualanimalforagingbehavioronpasturemeansthemonitoringofgrazing,rumination
andrestingbehaviors,whichalltogetheroccupy90%to95%ofthedailytimebudget.Duringthelast5to10%, animals are busy displaying social behaviors,walking,drinking,eatingsupplements,etc.thatmightalso provide interesting insights in terms of animalmanagement(Walkeretal.,2008).Focusingonfeedingbehavior,individualmonitoringofgrazinganimalsisbasedontherecordingofthreemainparameters:–thelocationoftheanimal:whereitisinthepaddock,inordertoidentifygrazingstations;
–the posture of the animal, the static elementcomposing a behavior such as the position of theheadortheback;
–themovementof theanimal, thedynamicelementcomposingabehaviorsuchasmovinglegsorjaw.
Tracking location on pasture was made possibleby the large dissemination of Global PositioningSystem (GPS) sensors. Global Positioning Systemhasbeensuccessfullyusedtodetectstaticordynamicunitary behaviors differentiated through changes inpathspeeds:foragingorgrazing,restingandwalking(Anderson et al., 2012). Nonetheless, accuracies ofbehavior classification based on GPS sensors, withsampling frequency lower than 10Hz, remain poor,i.e. <80% when compared to visual observationsbasedontimewindowsof5mineach(Schlechtetal.,2004; Larson-Praplan et al., 2015). Posture analysishasbeenmorerecentlydevelopedthroughtheuseofaccelerometersandbasedmainlyonthepositionofthehead:upordown.This information, in combinationwithGPS-baseddata,alloweddiscriminationbetweenseveral kinds of feeding related behaviors forgrazinganimalswithhighaccuracies(>90%).Thoseaccuracieswereobtainedwithashorttimewindowof5to10swhilethedataacquisitionfromtheGPSandtheaccelerometerranbetween4Hzand10Hz(Duttaetal.,2015;Gonzálezetal.,2015).
Finally,monitoringofcattlemovementsismainlyobtained using accelerometers. Through diverseanalysis methods, accelerometers recording data at10Hz could be used to classify behaviors, as donefor example by Mangweth et al. (2012) when theyclassified lame and non-lame cows using a basicstatistical method, reaching an average accuracy of91%.Similarly,Martiskainenet al. (2009)classifiedmultiplebehaviorsusingamachine learningmethodwithaccuraciesrangingfrom29%to86%withsampleswindowedfor10sforallbehaviorclassifications.
Conclusions.Laconclusiondecettesynthèseestquelescapteursmécaniques,acoustiquesetélectromyographiquesontmontréleurcapacitéàclassifierlesdifférentstypesdemouvementsdelamâchoireavecdifférentesprécisionsetdifférentesfenêtresdetempsnécessairespourcetteclassification.Cetteclassificationpourraitmeneràunemeilleuredétectiondecomportementsplusprécistellequeladétectiondesbouchéesetleurlocalisationsurleparcours.Mots-clés.Agriculturedeprécision,conduited’élevage,bovins,capteurs,mouvements,fourrage.
Review:sensorsforcattlejawmovementsmonitoring 3
The online detection and classification of thebehaviors are essential for the development ofremoteandautomaticmonitoringofcattleonpasture.However, other components of animal movements,like jawmovements,arepresentlyoverlooked,whiletheyareofutmostimportancetoassessanimalgrazingstrategieswhengrazingvarioustypesofpasturesandtodevelopnewmethodstobetterestimatetheirintake.Nonetheless,bitemassandsubsequentintakeratearethemostvariablecomponentsofthefeedingbehavior,thusthemostdifficulttopredict.Bitemassandintakerateareinfluencedbyverylargerangewithswardheight,swardbulkdensity,botanicalfamilyandspecies,animalcharacteristicsandmotivationdurationofthepreviousstarvationperiod,grazing system,pasture allowance,concentrate and forage supplementation level, dailytimeofaccesstopasture,hourofday,etc.(Rooketal.,1994;Gibbetal.,1997;Barrettetal.,2001).Thereforerecordingbitemassfromjawmovementsseemsonlya dream today.Bonnet et al. (2015) recently studiedthepossibilityofdoingacontinuousbitemonitoringwith acoustic sensors coupled to direct observationwith trained observers. Combined with preliminaryestimatesofbitemassperformedbythehand-pluckingmethod (Bonnet et al., 2011), they could estimatebite mass with an accuracy ranging between 80%and 94%, in a short-term intake rate (approximately
10min).Although not applicable for PLF purposes,such methods suggest that combining informationprovided by different sensors, for example location,head position and acceleration, and jawmovements,mayhelpovercometheeverlastingchallengeofintakeestimationonpasture.
The main goal of this review is to assess thetechnologiesavailableforthemonitoringofindividualjawmovementsincattleforresearchandPLFusesandto discuss the mechanisms of bite mass constitutionandtheirlinkswithjawmovements.Forthispurpose,wewill:–discussthemechanismsofforageintake,–detail various sensors that have been proposed tomonitorjawmovementsofruminants,
–outline the link between jaw movements, bitingbehavior and forage intake, focusing mainly oncattle.
2. MECHANISM OF FORAGE INTAKE FOR CATTLE
Grazing is a complex combination of variousmovements and activities performed at differenttemporalandspatialscalesasshowninfigure 1.Thesinglebiteistheelementarycomponentofthegrazing
Spatio-temporal scale
1 - 2 s
5 - 100 s
1 - 30 min
1 - 4 h
1 - 4 days to months
Bite area
Path width
Patch
Grazing site
Pasture
Animal intake level
Bite volume
Bite rate
Intake rate
Intake per meal
Daily intake
Bite
Feeding
station
Grazing bout
Grazing event or meal
Pasture or paddock
Figure 1. Spatio-temporal components of grazing behavior (adapted fromGibb, 1996;Gregorini et al., 2006; Carvalho,2013)—Composantes spatio-temporelles du comportement de pâturage (adapté de Gibb, 1996 ; Gregorini et al., 2006 ; Carvalho, 2013).
4 Biotechnol. Agron. Soc. Environ. 201620(S1), AndriamandrosoA.L.H.,BindelleJ.,MercatorisB.C.N.etal.
process (Ungar et al., 2006a; Carvalho, 2013). Itsfrequency ranges from 0.75 to 1.2bites per secondanditssizeismainlydeterminedbythemouthoftheanimalaswellassomevegetationcharacteristicssuchasswardheight,tensilestrengthanddensity(Griffithset al., 2003; Oudshoorn et al., 2013a; Oudshoornetal.,2013b).Severalbitesperformedinarowbyananimalonasinglefeedingstationwithoutinterruptioncomposeagrazingbout(Gibb,1996)thatwillcoverafewsquaremetersandlastbetween10to100seconds(Andriamandrosoetal.,2015).Severalgrazingboutsare performed during each grazing event or meal(Gibb,1996)thatoccurseachdayforseveralminutesto hours during which a significant portion of thepaddockisexplored.Finally,thepaddockisoccupiedfor somedays to severalmonthsandcoversanareathatisusuallyover1ha.Onlythetwohighestlevelsinfigure 1,i.e.grazingeventandpaddock,arediscussedinmostofresearchonthedetectionandclassificationofgrazingbehavior.
A detailed observation of cattle movementsat individual bite level (Figure 2) shows thatforagingrequiresmainly jawandaccessorily tonguemovementsthatcanbebrokendownintofourphases.Firstly, during prehension, cows surround a bunchof grass using their tongue and lips (Frames 1 and6,Figure 2)and take it into theirmouth (Frame11,Figure 2). Then the grass is grabbed between thelower jawand the gum (Frame16,Figure 2) and itis finally cutwith a suddenmovement of the lowerjawusuallyaccompaniedbyamovementofthewholehead to perform the proper defoliation (Frames 21and 25, Figure 2). This abrupt head movement ismarkedbyanupwardthrustof themouthvisiblebythe increase in distance between themouth and thebaseline and is usually considered as the actual bite(Gibb,1996) (Figure 2).Thewhole forage intake isendedbychewingandswallowingtheplantbiomass(Vallentine,2000).
Rumination jaw movements are more quiet andregular.Theyarecomposedofacyclicprocesswhichbegins with the regurgitation of a rumino-reticularbolusfollowedbysemi-circularjawmovementswitha specific frequency of 1.06± 0.06bites.s-1 duringmasticationandendswiththedeglutition.Deglutitionisdescribedasapausebetweentwoboutsofmasticationwhilethemasticationcyclelastsbetween15sand60s(Andriamandrosoetal.,2014).
In order to detect bites, various techniqueshave been developed to monitor jaw movements ofruminants(Figure 3).Beforetheearly1980’s,toolsforcountingjawmovementswerestrictlymechanical.Jawmovementswererecordedonpaperrollsordisks(e.g.Balch,1958)orcountedviabuilt-inelectricalcircuitsusedasrecorders(e.g.Stobbsetal.,1972).Howeversuchdevicescannotbeproperlyconsideredassensorssinceasensorisadevicethatdetectseventsormeasureschangesinaphysicalpropertysuchaslight,forceandsound in its environmentand transforms them intoausablesignal,usuallyanelectricaloutput, for furtheranalysis (Kenny, 2005). According to a literaturesurvey (Figure 3) pressure and microphone sensorsare the most used sensors for monitoring cattle jawmovements with 35 and 14 references, respectively.Whileonly4referencesmentionaccelerationsensors,their use is rising rapidly.Electromyography sensorsarealsosometimescited(2references),whilethetwolastreferencescompareddifferenttypesofsensors.
3. USE OF SENSORS FOR JAW MOVEMENTS DETECTION AND QUANTIFICATION IN CATTLE
Devicesdedicatedforjawmovementsdetectioncanbeclassifiedinfivegroups(Figure 3):–jawswitches,whereaswitchisactivatedateachjawmovement;
Base
line
Frame 1 Frame 6 Frame 11 Frame 16 Frame 21 Frame 250 s 1 s
Figure 2.Visualizationofcattlemouthmovements(25framespersecondvideo)—Mouvements effectués par la vache lors du pâturage (photographies tirées d’une vidéo à 25 images par seconde).
Review:sensorsforcattlejawmovementsmonitoring 5
Figure 3. Principaltoolsusedtocharacterizejawmovementsofcattle:referencesusedinthisfigurecamefromresearchinScopus(www.scopus.com,Elsevier,TheNetherlands,31/07/2015)usingcombinationofthreegroupsofkeywords(totalof57references):a:“bite”OR“chewing”OR“mastication”OR“jawmovements”OR“jaw”;b:“cattle”OR“cows”OR“heifer”OR“heifers”OR“calves”;c:“sensor”OR“electronicdevice”OR“tool”—Principaux outils utilisés pour caractériser les mouvements de mâchoire des vaches. Les références utilisées dans cette figure sont issues de recherches bibliographiques effectuées dans Scopus (www.scopus.com, Elsevier, The Netherlands, 31/07/2015) selon les groupes de mots-clés suivants (total : 57 références) : a : « bite » OR « chewing » OR « mastication » OR « jawmovements » OR « jaw » ; b : « cattle » OR « cows » OR « heifer » OR « heifers » OR « calves » ; c : « sensor » OR « electronicdevice » OR « tool ».
Jaw switches
Pressuresensors
Tools for jawmovementsdetection
Microphones
Accelerometers/Pendulum
Electromyography
Grazing durationNumber of bites
Behavior detection (grazing or not)
Duckworth et al., 1955;Stobbs et al., 1972;Chambers et al., 1981
Balch, 1958Ink record on paper
Electrical impulse
Pressure
Vibration
IGER + GRAZE software
ART-MSR Rumination sensor
Number of bitesBites intensityBite rateBites duration
Detection of chewing activityDetection, duration andnumber of ruminations
Number of bitesEstimation of intakeBehavior detection
Jaw movementsdetectionBite rateBite durationDiscrimination of jawmovements (bite, chew, chew-bites)
Jaw movements detection
Jaw movementsclassification andcountingBehavior detection(eating vs ruminating)
Jaw movements detectionDiscrimination of jawmovements (bite, chew,chew-bites)Behavior detection(eating, ruminating)
Bite rate
Behavior duration(grazing, ruminating, idling)Bite detectionNumber of bitesBite rateBehavior detection
Penning, 1983;Luginbuhl et al., 1987;Beauchemin et al., 1989
Rutter et al., 1997;Rutter, 2000
Nydegger et al., 2010
Ruckebusch et al., 1973;Lawrence et al., 1997
Delagarde et al., 1999;Unger et al., 2006b
Oudshoorn et al., 2013a
Umemura et al., 2009
Rus et al., 2013
6 Biotechnol. Agron. Soc. Environ. 201620(S1), AndriamandrosoA.L.H.,BindelleJ.,MercatorisB.C.N.etal.
–pressuresensors,whereajawmovementcorrespondstoachangeinpressureorlengthinatubearoundthenose;
–microphones, where sound patterns allow jawmovementdetection;
–accelerometers,todetectmovementsoperatedduringajawmovement;
–electromyography, transducing a jawmovement toanelectricalsignalfromthemovementofthemuscle.
Inthefollowingsection,onlythelastfoursensorswillbediscussed,becausejawswitchesarenotactualsensors as previously explained, since the collectedinformationisprinteddirectlyandisnottransformedinto a digital or electrical signal output (e.g. Balch,1958).
3.1. Pressure sensors
Theuseofpressuresensorsinbitemonitoringbeganwith the pioneering works of Penning (1983). Hisinstrument was composed of a halter fitted witha silicon noseband connected to two electrodes.When thenosebandstretched froma jawmovement,it changed the electrical resistance between theelectrodesplacedatbothendsofthetube.Thisinduceda change in voltage which produced a signal whichwas proportional to the extent of the jawmovementandwaving(Harman,2005).Itwasusedsuccessfullytodifferentiategrazingandruminatingbehaviorsandtomeasuretimespentgrazing,ruminatingandidling,achieving a 95%agreementwith visual observationsoveratimewindowof3min(Penning,1983).Severalvariationsof themethodexist, including initially theuseofa rubber tubeorballoonplaced justunder thelowerjaw(Luginbuhletal.,1987)butenhancedlater
as a tube encircling the nose (Penning, 1983;Rutteretal.,1997;Nydeggeretal.,2010)orunder the jaw(Dadoetal.,1993).
Building on this technology, two systems weredevelopedwhichwereusedexclusivelyinresearchandnot applied inPLF (Figure 4).The IGERBehaviourRecorder (Institute of Grassland and EnvironmentalResearch, Okehampton, UK, Rutter et al., 1997)and the ART-MSR pressure sensor (AgroscopeReckenholz-TänikonARTResearchinstitute,ModularSignal RecorderMSR145,MSR Electronics GmbH,Switzerland,Nydeggeretal.,2010)weredesignedforpasture and for stable use respectively.Both devicesareabletomakea24-hourscontinuousdatarecording,with a maximum of 40h for ART-MSR (Nydeggeretal.,2010).TheIGERBehaviourRecorderconsistsofanosebandandanelectronicinterfaceincludingarechargeable battery connected to a computer boardallowingamemorycardtorecord,analyzeandstoredataat20Hz.Ajawmovementisrecognizedasapressurepeakthroughthetransmissionofthemovementtothehalterandthechangeinthetubepressure.Thesoftwareinstalledonthecomputerboard(Rutter,2000)isableto classify jawmovements (bites or chews), identifyjaw movement bouts and determine the behaviorassociated with each bout with defined thresholdsbased on the analysis of the peaks shape. Peaks areconsideredasbiteswhentheyareacombinationofamajorlongpeakfollowedbyasmallersub-peakoranon-symmetricalpeakintheabsenceofthesub-peak(Nadinetal.,2012).Conversely,achewcontainsonlyonepeakofsymmetricalshape(Championetal.,1997).Thedetectionaccuracyreachesanoverallconcordanceof91%with95%foreatingand93%forruminatingwhen compared to the observations on a 5-min timewindow-basiswithamaximalsamplingrateof20Hz
Figure 4.Comparisonofthetwomost-usedpressuresensorstomonitorjawmovementsofcattle—Comparaison des deux capteurs de pression les plus utilisés pour le monitoring des mouvements de la mâchoire des vaches.
Jaw movements detection
Jaw movementsclassification
Behavior detection
Jaw movements detection
Behavior detection
IGER Behaviour Recorder (Rutter et al., 1997; Rutter, 2000)
ART-MSR(Nydegger et al., 2010)
Pressure sensors for jawmovements monitoring
Designed for pasture
Designed for stable
Average accuracy: 91%
Average accuracy: 100%
BiteChew
EatingRuminating
Other
EatingGrazingRuminatingIdling
Review:sensorsforcattlejawmovementsmonitoring 7
(Rutter et al., 1997). Those accuracies decreased forcattle grazing a tropical pasture and confronted withmore heterogeneous grazing conditions, as it wasdifficult to clearly differentiate biting fromother jawmovements(Nadinetal.,2012).
Several studies (12 references) used the IGERBehaviourRecordertoinvestigatetheeffectofdifferentfactorssuchasthetimeofgrazing(Abrahamseetal.,2009), the sward height (Gibb et al., 1999; Fonsecaetal.,2013),thephysiologicalstateoftheanimal(Gibbetal.,1999)orthemilkingfrequency(O’Driscolletal.,2010) on grazing behavior or on grazing intake.Thelatest study was done by Fonseca et al. (2013) whousedgrazingbitesdetectedandcountedby theIGERBehaviourRecordertoestimatebitemassandbiteratefordeterminingtheeffectofswardheightandlevelofherbagedepletiononthesebitefeatures.
In theART-MSR system (Nydegger et al., 2010),thetubeencirclingthemouthisfilledwithoilandthesensor records the pressure change when the jaw ismoving.Whenthecowopenshermouth,an increaseinpressurealterstheelectricalresistance,resultinginasignalinthesensorcorrespondingtoonechew(Braunetal.,2014).Apart from the jawmovements featuresas described for the IGER Behaviour Recorder, itis also possible to estimate the feed intake from thenumber of chews and duration of eating time withreasonable correlation coefficients (R²= 0.6 to 0.9)(Pahletal.,2016).Accuraciesindetectingeatingandruminatingactivitiesapproached100%,whilecountingjawmovementsperformedduringeachbehaviorgavedisagreement rates of 12% and 0.24% respectivelycompared to observations performed over 5min(Nydeggeretal.,2010).Thus,thistoolismoreaccuratewhen counting ruminating jawmovements, probablyduetotheregularpatternofthisbehaviorcomparedtoeating.
Toconclude,pressuresensorsderivetheirpowerindetectingjawmovementsbyidentifyingpatternsthataredifferentduringeatingandrumination.Thesilicontubeencirclesthenoseasanormalhalter,soitdoesnotalterthenormalbehaviorof the cow.Misclassifications inthismethodusuallyarisefrompracticalconsiderations.Variation in the tighteningof thehalteron individualanimals can generate different pressure values,modifying discrimination thresholds. The analysis oftheoutputwavesignalbasedonthepeakdetectioniscompromisedifthehalterismountedtootightlyortooloosely(Nydeggeretal.,2010).Moreover,automatedtransmissionofdataisnotyetdeveloped.
3.2. Acoustic monitoring of jaw movements using microphones
Theminiaturizationandaccessibilityofdifferentkindsof sensors increased the use of microphones for the
detectionandcharacterizationofcattlejawmovements.Inamicrophone,soundsaregoingthroughaflexiblediaphragmandcausevibrations.Theoutputelectricalsignalisproportionaltotheintensityofthesevibrationsaswellastheirfrequencies.
Microphones used for recording jaw sounds of agrazingruminantcanbeusedtodiscriminatebitesorchews.Thisallowstheclassificationbetweengrazingorruminatingbehaviortobeachievedovertimewitha succession of bites or chews (Navon et al., 2013;Benvenuttietal.,2015).
Acoustic analysis allows differentiation of threetypesofjawmovements:chew,biteandchew-bite.Bitereferstoarippingsoundwhilechewreferstoagrindingsound, easing the differentiation between these twotypesofjawmovements.Chew-bitecorrespondstoanintermediatebetweenchewandbitesound,i.e.duringthisjawmovementtheherbagealreadyinthemouthischewedandsimultaneouslyanewmouthfulofherbageissevered;thesetwomovementsareperformedwithinasinglejawmovement(Ungaretal.,2006b).
Methodsusingsoundrecordingsforjawmovementsclassification differ according to the location of themicrophone,theprocessingoftheacousticsignalandtheaimofclassification(Table 1).Somemethodsareonlyabletodetectjawmovements.Forexample,basedon10minofgrazingsessionrecordedbyacamera,thesimpledetectionof jawmovementsusing amachinelearningtechniquereachesanaccuracyof94%whencomparedtotheauralanalysisofsoundsbyatrainedoperator (Navon et al., 2013).Themachine learningalgorithm uses four properties of the signal pattern:the shape to determine jaw movements interval,the intensity of each jaw movement represented bya peak in the time domain, the duration and theirintegration in a sequence of behavior (Navon et al.,2013).Claphametal.(2011)usedsimilarparameters(frequency, intensity, duration and time betweenevents) calculated during sound segments of 1 to5mintodetectandanalyzebites,reachinganoverallbehavior classification accuracy of 94%. Using adiscriminant function, bite and chew could also bedifferentiated with an accuracy of 94% (Claphametal.,2011).Thediscriminantanalysisisbasedhereonthreeparametersdeterminedfromthesignalpatternofthesoundproducedduringbitingandchewinginsidea1kHzsoundwindow:peakfrequency,peakintensity,average intensity and their duration (Laca et al.,2000).Finally,detectionandclassificationofthethreetypes of jawmovements (bite, chew and chew-bite)are possible using the Hidden Markov model. Thismodelestimatessequencesofbitesorchewsorchew-bites, called hidden states, which are not observabledirectly, using their acoustic spectrum characteristicsi.e. theenergyproduced, indecibels,byeach sound.Usingdifferentframelengths(20to80milliseconds)
8 Biotechnol. Agron. Soc. Environ. 201620(S1), AndriamandrosoA.L.H.,BindelleJ.,MercatorisB.C.N.etal.Ta
ble
1. M
ethodsforjawm
ovem
entsclassificationbasedonacousticrecordings,typeofjawm
ovem
entsdifferentiated(B:b
ites;C:chews;CB:chew-bites)and
classificationaccuracy(C
A,%
)orerrorrate(E
R,%
)overrequiredsamplingtim
ewindow—
Les
mét
hode
s de
cla
ssifi
catio
n de
s m
ouve
men
ts d
e la
mâc
hoire
bas
ées
sur
les
capt
eurs
aco
ustiq
ues,
les
type
s de
mou
vem
ents
de
la m
âcho
ire e
nreg
istr
és (
B :
bouc
hées
; C
: m
astic
atio
n ;
CB
: m
astic
atio
n-bo
uché
es)
et la
pré
cisi
on d
es
clas
sific
atio
ns (C
A, %
) ou
le ta
ux d
’err
eur (
ER, %
) sel
on la
dur
ée d
e la
fenê
tre d
’éch
antil
lonn
age.
Type
of
mic
roph
one
Loca
tion
Aud
io si
gnal
pr
oces
sing
befo
re
clas
sifica
tion
Jaw
mov
emen
tscl
assifi
catio
n m
etho
dC
ondi
tions
Acc
urac
y Ti
me
win
dow
R
efer
ence
ShureVPwireless
microphone(Shure
Incorporated,
Niles,USA
)
Forehead
None
Quadraticdiscriminant
functionusing3setsof
variable
Seta
ria
lute
scen
spasture
(tall
vssh
ortturves),
4Herefordcross-bred
steers
ForC
andB,C
A=94%
notreported
Lacaetal.,
2000
ShureVPwireless
microphone(Shure
Incorporated,
Niles,IL,U
SA)
Forehead
Transformation
intofrequency
domain(FFT
)+
normalization+
extractionof6
features
Discriminantanalysis
Loliu
m p
eren
neand
Trifo
lium
repe
nspasture,
6cows
Totalnum
berofpeaks
(CA=67%
)Totalnum
berofpeaks+
frequencybandwidth+
frequencyskew
ness
(CA=81.8%
)notreported
Ungaretal.,
2007
Logisticregression
CA=86.7%
Neuralnetworks
B:C
A=25-63%
C:C
A=65-80%
CB:C
A=70-90%
Lavalier
microphone
(SennheiserM
E2-US,Wedem
ark,
Germany)
Mouth
Highpassfilterw
ith
cutofffrequencyof
600H
z
SIGNALsoftw
are:de-
tectbitesw
ithfrequency,
intensity,durationand
timebetweenthose
events
Fest
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arun
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cea,
Dac
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glo
mer
ata,
Poa
prat
ensi
sand
Trifo
lium
repe
ns
pasture,Angussteers
B:C
A=95%
Autom
atedbitecounts:
ER=9.1%
1-5min
Clapham
etal.,
2011
Nadymicrophone
(Nady151VR,
NadySystem
s,Oakland,U
SA)
Forehead
None
1-featureextraction
usingHam
mingwindow,
2-HiddenMarkovModel
Fest
uca
arun
dina
cea
and
Med
icag
o sa
tiva
(tall
vssh
ort),2Holstein
cows
B:C
A=76%
-90%
C:C
A=88%
-99%
CB:C
A=61%
-94%
13min
Miloneetal.,
2012
ShureVPwireless
microphone(Shure
Incorporated,
Niles,USA
)
Forehead
None
Machinelearningfrom
tim
edomainfeatures:
shape,sequence,
intensity,duration
Loliu
m p
eren
neand
Trifo
lium
repe
nspasture,
12Holstein-Friesian
cows
Jaw-movem
ents
identification:CA=94%
(tolerance=0.2s)
10min
Navonetal.,
2013
Review:sensorsforcattlejawmovementsmonitoring 9
correct classification of those three jaw movementtypesrangedbetween61%and99%andisinfluencedby the pasture type and grass height (Milone et al.,2012).Usingdiscriminantanalysis,logisticregressionand neural networks as classification methodologiesyielded67%to82%,87%and25%to90%ofcorrectclassifications respectively, while the time windowusedinthecalculationswasnotreported(Ungaretal.,2007).
In addition, Nadin et al. (2012) showed nosignificant differences between visual observationsand microphone-based detection methods in studiesdemonstratingtheperformanceofacousticsensorstomonitorgrazing jawmovementsof cattle.Moreover,sincegrazingcorrespondstoasuccessionofbitesandchews with accessorily chew-bites and ruminationcorrespondstoasuccessionofchews,itisalsopossibleto differentiate those behaviors using microphones(Navonetal.,2013).
To summarize, microphone-based methods reachgoodaccuracyforjawmovementsdetection.Inaddition,theyareabletodifferentiatethreedifferentkindsofjawmovements:twovisiblemovementscorrespondingtochewandbite,andoneintermediatedifficulttodetectvisually (chew-bite). However, outdoor applicationsare disturbed by environmental noises, so extendingsoundrecordingandinterpretationtechniquesacquiredunderidealexperimentalconditionstoanon-farmleveltoolrecordingandanalyzingsoundsautomaticallyforPLFapplicationsstillrequiressignificantdevelopment.
3.3. Acceleration sensors
An accelerometer is an electronic sensortransforming physical acceleration from motion orgravity into waveform voltage signal output. It canmeasure both static acceleration due to gravity, thelow-frequencycomponentoftheaccelerationandthedynamicaccelerationduetomovementsimprintedbytheanimal(Almeidaetal.,2013;Brownetal.,2013).
Despitethedepthofliteraturesurveyed,onlyfourreferences used this type of sensor to identify andclassifyjawmovements,amongwhichthreedescribeddifferentmethodsofclassification.Instables,Tanietal.(2013)coupleda1-axisaccelerometertoamicrophoneto classify cattle chewing activities by matching1minute segment waveform patterns to observedeating and ruminating behaviors. Intake chewingactivitieswerehighlydistinguishedat90%, reaching99%whenthesensorwasattachedtothecow’shorn.
On pasture, a 3-axis accelerometer was usedby Oudshoorn et al. (2013b) to record cow bites.A visualization of recorded signals from the threeindividualorthogonalaxes(x,y,z)wasdonefirst, inorder to determinewhich onematched bestwith theobserved bites. To determine each bite, a series of
thresholds were tested to determine the peak whichhadthebestcorrelationtotheobservation.Theaveragecorrelationcoefficientwas0.65indicatingthedifficultytocountbitesusinganaccelerometerthisway.Finally,Umemura et al. (2009)modified a pedometer into apendulumunder the lower jaw tomonitor cattle jawmovements.Thedatacouldbewirelesslydownloadedfromthesensorwhichhadalifespanofoneyear.Thissystem was able to count jaw movements with anaccuracyof90%comparedwithmanualcountsover10minsegments.Thisbitecountwascorrelatedwithacoefficientof0.7topasturedisappearanceestimatedindirectlybyarisingplatemeter.
Accelerometer sensors thus provided interestingoptionstoautomaticallycountcattlejawmovements.As for soundsensors, interferencemaybepresent inthesignalrecordedbythesensor.Bitesaretheresultofjawandheadmovementswhilechewimprintsmostlyjaw movements. The sensitivity of accelerometerscould provide undesirable signals during recordingsessionsduetoearmovementsorsuddenheadturnstodrivefliesorotherinsectsaway.Thus,apre-processingofthesignalisprobablyrequiredtoisolatethesignalrelative to the jaw movements in order to considerthismethodforactualPLFusesonfarms.Intermsofmaterial,theuseofaninertialmeasurementunit(IMU)whichisacombinationofaccelerometers,gyroscope,magnetometerandlocationsensorsmightofferarealadvantage knowing that all these variables can berecordedsimultaneouslyatahighsamplingfrequency(100Hz).Forexample,Andriamandrosoet al. (2015)used smartphone IMUs to count thenumberof bitesthroughfrequencypatternof1-axisaccelerationdata.Asforpressuresensors,iftheaccelerometerismountedinahalter,thetighteningalsoplaysanimportantroleinthetransmissionofthemovementtothesensor.
3.4. System based on electromyography
While the noseband pressure sensor quantifieschanges in tube pressure and translates it into anelectricalimpulse,electromyographicsensorsquantifytheelectricalpotentialofmasticatorymusclesduringcontractions (Rus et al., 2013). Two electrodes arefixed on a halter and measure electrical signalsoccurringduringajawmovementwithacontractionoftheMassetermuscle.Thissensor,coupledtoa3-axisaccelerometer and awireless transmission of data inreal time, constitutes the DairyCheck sensor (Rusetal.,2013).Thissystemisabletodetectruminatingand feeding behavior on the basis of the regularityand irregularity of signal pattern respectively. TheDairyChecksystemyieldedanoverallconcordanceof87%compared to visual observations over 1minute,when detecting feeding time and rumination time(Bücheletal.,2014).
10 Biotechnol. Agron. Soc. Environ. 201620(S1), AndriamandrosoA.L.H.,BindelleJ.,MercatorisB.C.N.etal.
Finally, when the different above-mentionedtechniques are compared, noseband pressure sensorsandmicrophonesarebestabletodetectjawmovementswithhighaccuracy(over94%)anddifferentiatebitingandchewingjawmovementswithfairaccuracy(61%to 95% for microphone) (Table 2). Accelerometerscan identify jaw movements with less than 90% ofaccuracy but discrimination of the different typesof jaw movements is not mentioned yet. Authorsusing theelectromyographymethoddidnotgiveanyinformation about the accuracy of the detection ofspecificjawmovements.
4. ESTIMATION OF GRAZING INTAKE THROUGH BITES QUANTIFICATION
Formany years, variousmethods have been used toquantifyforageintakeofgrazingherbivores,includingthemeasurementofpasturebiomassbeforeandaftergrazing, changes in animal bodyweight, digestivemarkers,orfecalnear-infraredreflectancespectroscopy(reviewed by Decruyenaere et al., 2009) (Figure 5).
Amongthesetechniques,animalbehaviorcanalsobeusedasameasurementofgrazingintakebycombininggrazingduration,biting rateandbitemass.Knowingthatabiteistheelementaryandindivisibleunitofthewholegrazingprocess,thistechniquestressestheneedtoproperlyquantifybites,toestimatetheintakealongwiththemassofeachindividualbiteortheiraveragemass.
Thismethodisbasedonthedeterminationofbitemass(quantity, ingrams,ofgrasstakenineachbite)andbite rate (numberofbitesperminute)asper thefollowingformula(Vallentine,2000):
Forageintake(g.day-1)=Bitemass(g.bite-1)xBiterate(bite.min-1)xGrazingactivityduration(min.day-1)
Toefficientlyusethisformula,accuratedetectionsofgrazingbehaviorandindividualbitesareessential.As already mentioned, several sensors are able toquantify these parameters with various accuracies.Oudshoornetal.(2013a)correlatedintakeforgrazingcattlefromgrazingtimeestimatedbyanaccelerometerand bite frequency with a prediction precision of
Table 2.Comparisonofdifferenttypesofsensorstodetectandclassifyjawmovements—Comparaison des différents types de capteurs utilisés pour détecter et classifier les mouvements de la mâchoire.Type of sensor Jaw movements
detection accuracy1 (%)Jaw movementsclassification (accuracy1in%)
Required sampling time window (min)
References
Nosebandpressuresensors
91-95 BiteChew 5min Rutteretal,1997;
Nydeggeretal.,2010Microphones 94-95 Bite(76-95)
Chew(88-94)Chew-bite(61-94)
1-13minUngaretal.,2007;Claphametal.,2011;Miloneetal.,2012
Accelerometers 65-90 (datanotprovided) 10min Oudshoornetal.,2013a1:Comparisonwithvisualobservations—comparaison par observations visuelles.
Figure 5.Listoftechniquesforgrazingintakemeasurement(fromDecruyenaereetal.,2009)—Liste des techniques utilisées pour la mesure de l’ingestion lors du pâturage (selon Decruyenaere et al., 2009).
Method of difference: before and after grazingCutting method: grazing simulationLive weight difference
Indigestible plant compound
Markers Chemical marker
Radio technique: forage digestibility + fecal outputAnimal behavior: grazing time, biting rate, bite massEmpirical technique: modelsAnalysis of plant residues in digestive tractNIRS
Direct measurements
Indirect measurements
Measurement of grazing intake
Review:sensorsforcattlejawmovementsmonitoring 11
less than 1.4kg of dry matter per cow per day. Inthis experiment, grass intake was initially measuredusing an indigestible marker and the difference innet energy balance between energies offered by thegrassandrequiredforanimalneeds.Thispapershowsthat prediction of intake from grazing behavior andbites counts is still beyond reach due to the lack ofaccuratebitemassestimation.Indeed,beyondbiterate,forage intake of grazing animals depends on pasturecharacteristics (sward height and bulk density) asexpressed by the bitemass formula (Carvalho et al.,2015):
Bite mass (g.bite-1) = Bite area (cm²) x Bite depth(cm)xBulkdensity(g.m-3)
Inthisformula,thebulkdensityandthebiteareaarecalculatedfromempiricalmodelsusing,swardheightandrelativebitedepth(swardheight/2),tillerdensityandforagebiomassperareaunit,and thesizeof thedentalarcade.Suchpredictivemodelsyieldacceptablebitemassestimatesinshort-termexperimentswithveryhomogenousvegetationcharacteristics(Carvalhoetal.,2015),butfailinmorecomplexvegetationunits.Untilnow,thebestmethodavailableremainshand-plucking.Itsimulatesabitebymimickinggrassprehensionbyhand and bite mass estimation accuracies can be ashighas95%forcowsandgoatswithtrainedoperators.Thisaccuracycorrespondedtothecorrelationbetweentheamountofgrasstakenbytheanimalperbiteandthose pluckedmanually (Bonnet et al., 2011).Whilethismethodseemsuseful tocalibratebitemassesforintakemeasurementsforresearchpurposes,itistime-consumingandnosensorscanperformasimilartask,henceitseemsuselessfromaPLFperspective.
Therefore,monitoringintakeingrazingruminantsusingPLFapproachesisstilloutofreach.Nonetheless,from a research perspective, the bite mass formulaprovides interesting directions for future research onthequantificationofbitemassusingacombinationofpromisingtechnologiessuchas:–accuratemonitoringofthepasturewithforexampledistancemetersand/ortime-of-flightcameras,
–animal positioning with wifi triangulation orcentimeter-accurateGPS,
–rigid body attitude estimation from accelerometerdatatoreconstructheadmovement,
–jaw monitoring using sensor-based technologiesdescribedinthispaper.
5. CONCLUSIONS
Mostsensorsmentionedinthisreviewwereprimarilydesigned for research. Although some sensors suchas accelerometers (e.g. SensOor®, CowManager,
Utrecht, The Netherlands) are already used forbehaviorclassificationinfarmsituations,thereuseasonfarmtoolsforjawmovementsmonitoringofgrazinganimals still requires significant hard- and softwaredevelopments, especially regarding the automationprocessandreal-timedataacquisition,aswellaseaseof installation and use. For example, whether basedonmechanical(pressureoracceleration),electrical,oracousticsignals,mostsensorsrequiretheuseofahalter,andthewayitismountedisextremelyimportantintherecordingofjawmovements.Apre-processingofthesignalmayalsoberequiredtoeliminateexistingnoisesaroundtheanimalorduringthemovement.Combiningdifferent sensors, for example accelerometers tomicrophones,maybeasolutionforabettermonitoringof bites. Dedicated signal processing also requiressignificantdevelopment.Forexample,usingfrequencydomain signalprocessingapproachesonaccelerationdata might provide useful progress. Accuraciesmentionedinthisdocumentwereobtainedusingshorttimewindowsanddifferentcalculationmethodswhichcould affect the percentage values. Longer time stepshouldbeconsidered tocoveroneorseveraldays inordertoshowifthedetectionaccuracywouldchangesignificantlyornot.Finally,PLFrequiresthesystemtoberobustandadaptabletoawiderangeofsituations.Most techniques presented here were applied understrict controlled conditions for research and theirimplementationinthefarmswouldalsorequiresomeability for auto-calibration of the device or tools toovercome differences in individual physiologicalstates,morphologies or grazing conditions accordingtotheseasonandpasture.
Acknowledgements
ThisreviewisinvolvedinaresearchfundedbytheCAREAgricultureIsLife(TERRAResearchUnit,GemblouxAgro-BioTech,UniversitédeLiège).The authors specially acknowledge Jeffrey Peter Corfield(Corfield Consultants, Australia) for corrections andsuggestionshegavetoourreview.
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