1

Optimisingconfigurationofahyperspectralimagerforon-linefield1

measurementofwheatcanopy2

RebeccaLWhettona,TobyWWainea,andAbdulMMouazena,b*3

aCranfieldSoilandAgriFoodInstitute,CranfieldUniversity,BedfordshireMK430AL,UK.4

bDepartmentofSoilManagement,GhentUniversity,Coupure653,9000Gent,Belgium.5

E-mailofcorrespondingauthor:Abdul.Mouazen@UGent.be6

Abstract7

There is a lack of information on optimal measurement configuration of hyperspectral8

imagers for on-line measurement of a wheat canopy, this paper aims at identifying this9

configurationusingapassivesensor(400-750nm).Theindividualandinteractioneffectsof10

cameraheightandangle,sensorintegrationtimeandlightsourcedistanceandheightonthe11

spectra’s signal-to-noise ratio (SNR)wereevaluatedunder laboratory scanningconditions,12

fromwhichanoptimalconfigurationwasdefinedandtestedunderon-linefieldmeasurement13

conditions.Theinfluencesofsoiltotalnitrogen(TN)andmoisturecontent(MC)measured14

with an on-line visible and near infrared (vis-NIR) spectroscopy sensor on SNRwere also15

studied. Analysis of variance and principal component analysis (PCA) were applied to16

understand the effects of the laboratory considered factors and to identify the most17

influencingcomponentsonSNR.18

Results showed that integration time and camera height and angle are highly influential19

factorsaffectingSNR.Among integration timesof10,20and50ms, thehighestSNRwas20

obtainedwith1.2m,1.2mand10°valuesoflightheight,lightdistanceandcameraangle,21

respectively. The optimum integration time for on-line field measurement was 50 ms,22

2

obtainedatanoptimalcameraheightof0.3m.On-linemeasuredsoilTNandMCwerefound23

tohavesignificanteffectsontheSNRwithKappavaluesof0.56and0.75, respectively. In24

conclusion, an optimal configuration for a tractor mounted hyperspectral imager was25

establishedforthebestqualityofon-linespectracollectedforwheatcanopy.26

27

Keywords28

Hyperspectralimager,signal-to-noiseratio,wheatcanopy,principalcomponentanalysis,29

soilproperties.30

31

1 Introduction32

Advancedmethodsforearlydiseasedetectionincropsisvitalforimprovingtheefficacyof33

treatment, reducing infectionandminimising the losses toyieldandquality.Traditionally,34

diseasedetectioniscarriedoutmanually,whichiscostly,timeconsumingandrequiresspecial35

expertise (Schmale & Bergstrom, 2003; Bock et al., 2010). Developments in agricultural36

technologyhaveledtodemandsforanon-destructive,automatedapproachforcropdisease37

detection that should be ideally rapid, disease specific, and sensitive to early symptoms38

(Lópezet al., 2003).Optical sensingmethods arenon-destructive, allowing repeateddata39

acquisitionthroughoutthegrowingseasonwithoutinhibitingcropgrowth.Spectroscopyand40

imagingtechniqueshavebeenusedindiseaseandstressmonitoring(Hahn,2009).However,41

their in-situ application although established in other industries (e.g. health services,42

pharmacologyandfoodsafety)isstillratherlimited.BothLenketal.(2007)andSankaranet43

al. (2010) focused on implementing the technology in the field, as a mobile (on-line)44

3

application for mapping crop disease. Yuan et al. (2016) have used high spatial satellite45

imagery inthedetectionofpowderymildew.Remotespectralsensingfor identificationof46

weedsinwheatfieldshasbeentestedbymeansofgroundcollecteddata(Gómez-Caseroet47

al.,2009).Herrmannetal.(2013)haveappliedproximalhyperspectralimageryinthefieldfor48

weeddetection(e.g.,bothbroadleafandgrassweeds),reporting85%accuracy.Okamoto&49

Lee (2009) collected in situ hyperspectral images for the detection of green citrus fruits,50

reporting promising results for identification of citrus fruits from background objects. In51

contrast,non-mobile (off-line)and laboratorymethods fordiseaseclassificationandplant52

growingconditionshavebeenstudiedanddemonstrated(Roggoetal.,2003;Wuetal.,2008).53

Hahn (2009) claims that spectroscopic and imaging techniques could be integrated with54

agricultural vehicles, providing non-invasive and reliable systems for the monitoring and55

mappingofcropdiseases,withfurtherpotentialforearlydiseasedetection.Moshouetal.56

(2005) have shown that hyperspectral imaging for the recognition of in-situ disease can57

provideidentificationwithahighdegreeofaccuracy.Dependingonthemethodofanalysis58

anddatafusion,anerrorbetween1-16.5%wasreported.59

Spectral reflectance in vegetation canopies is dependent on several factors including the60

illuminationangle, thecanopyarchitectureandtheradiativepropertiesof theplants.The61

reflectanceofcropcanopiesisnon-lambertianscattering,varyingwiththesunposition,view62

positionsandmeteorologicalconditionsincludingcloudcover(Pinter&Jackson,1985;Asner,63

1998). Plant species, maturity, phenology, level of foliage and nutrient status are plant64

properties affecting reflectance (Asner, 1998; Coops et al., 2003; Gnyp et al., 2014).65

Geometrical arrangement of objects can affect the spectral reflectance such as leaf66

orientation,whichcannotbecontrolledduringon-linemeasurements (Asner,1998;Coops67

etal.,2003).Thiscreatesproblemsassociatedwithreducedreflectionfromlightscattering.68

4

Shadowsatsmallscalecanbereducedbyadditionallightsourcesandthatopposinglighting69

canhelpreduceshadows(Barbedoetal.,2015).Obertietal.(2014)arguedthattheangle70

betweenthecanopyandcameraintherangebetween0°to60°affectsthesensitivityofa71

mountedon-line(mobile)sensorduetolightbackscattering,suggestingthepotentialofan72

obliquecameraangle,toreducetheimpactonsignal-to-noiseratio(SNR)variation.73

A tractormounted hyperspectral imager allows for on-line field crop canopy sensing and74

mapping,however,anoptimalconfigurationofthecamera,lightsourceandintegrationtime75

needstobeestablishedforoptimalqualityofimageryandspectratobecollected.Spectral76

quality ispredominantlyaffectedbysensor integrationtime,cameraorientation,andlight77

heightandanglefromtheobject(leaforcanopy).Integrationtimeistheperiodoverwhich78

thedetectorcollectsphotonsoflight.Thegreatertheintegrationtimeandlightintensity,the79

morereflectedlightisexpectedtobecapturedbythedetector,providingahigherSNRand80

pronunciationofthespectralpeaks.Thoughwhenrelyingonsunlighttheintensitycanbe81

variable. When applying a spectral technique to a forward moving platform (on-line82

measurement) longer integration times result in an average spectrumover a larger area,83

reducingthesensitivity.Furthermore,thegreaterthedistancebetweenthecameraandits84

subject, the larger the area observed and captured by a single pixel, reducing spatial85

resolution.Therefore,optimisingthemeasurementconfigurationisessentialbeforeon-line86

fieldmeasurementscanbesuccessfullycarriedout.Furthermore,backgroundsoilinfluences87

canopyspectra,andeffortshavebeenmadetoremovethisinfluence(Huete,1988).Based88

onremotesensingdataofthesurfacesoil,Demetriades-Shahetal.(1990)suggestedusinga89

secondorderderivativetoremovedeviationscausedbythesoilbackground.However,none90

ofthesestudieshaveinvestigatedtheinfluencesofon-linemeasured(atadepthof15-2091

5

cm)soilproperties[e.g.,moisturecontent(MC)andtotalnitrogen(TN)]onthequalityofcrop92

canopyspectra.93

Thispaperevaluates,under laboratoryconditions,theindividualandinteractioneffectsof94

cameraheightandangle,integrationtimeandlightdistanceandheightonthespectralSNR95

of a wheat plant canopy captured with a hyperspectral line imager. Furthermore, the96

influenceofon-linemeasuredsoilMCandTNonSNRofplantspectracollectedon-lineinthe97

field is also assessed. This was essential to inform optimal configuration and operational98

conditionsforon-linefieldmeasurementofcropcanopyanddiseases.99

100

Table1.Factorsincludedinconfiguringhyperspectralimager(multipleconfigurations101considered)102

103

2 Materialsandmethods104

2.1Hyperspectralconfigurationinthelaboratory105

WinterwheatTriticumSativum(Solsticevariety)wasgrownoutdoorsin600x400mmtrays106

(depthof120mm)with100seedsevenlysownandspacedin5parallellines.Afterseeding107

thetrayswerepredominantlyrainfed,toreduceinputofexcesssaltsfromtreatedtapwater.108

Camera angle,

deg �

Light height,

m

Light distance,

m

Camera height,

m

Integration

time, ms

0 0.90 0.60 0.15 10

5 1.2 0.90 0.30 20

10 - 1.2 0.45 50

- - - 0.60 1000

6

Apushbroomhyperspectralimager(spectrograph)(HSspectralcameramodelfromGilden109

PhotonicsLtd.,UK)wasusedtocapturehigh-resolutionlineimageswitharesolutionof1,608110

pixelsover1second,usingadiodearraydetector.Itisa12bitBaslerpiA1600-35gmcamera,111

with Schneider-Kreuznach XNP1.4/23 lens and has a pixel pitch of 7.4 μm112

interpolated/averagedto0.6nmreadingswithaspectralrangeof400-750nm.Thereflected113

light from the target travels through the lens, past an entrance slit through a series of114

inspector optics in the spectrograph and then split by the prism dispersing element into115

differentwavelengths. This sensorwas chosen for its potential for being applied to crop116

canopymeasurements,andwasoflowpricecomparedtocomparablesensors,commercially117

availableinthemarket.118

Thedatacapturedisintheformofalinearray,witheachpixelcontainingaspectrumand119

onedetectorperpixelacrosstheswath.Inordertocompileafullimage,everylineacrossa120

target must be captured (Gilden Photonics Ltd, Glasgow, UK). When configured on a121

consistentmovingplatform,theimagersweepsacrossanareatobuildupanimage.Dueto122

practical restraintsofapplyingaconsistentmovingplatformthespectraSENSv3.3 (Gilden123

Photonics Ltd, Glasgow, UK) software was adapted to record a single line array, which124

requiredanadditionalRGBphototakenbya5megapixelcamerawitha3.85mmf/2.8lens125

atthesametimeofimagecapture,sothatthescannedareacouldbecomprehended.Two126

laserpointerswereaddedateachsideofthehyperspectralimagertoindicatetheareaofthe127

canopytobescanned.Thelaserpointerswereshutoffwhenthespectralimagewascaptured128

to remove any interference. Before data analysis, the collected scans were corrected by129

meansofadarkandwhitereference,whichwerecollectedjustbeforespectralcapture,and130

at 10 minute intervals until scanning was completed. The white reference used was a131

7

commerciallyavailableSpectralonTeflonwhitecalibrationpanelwith99.9%whitereflectance132

value.133

Factorialanalysiswasundertakentounderstandandquantifytheinfluenceofconfiguration134

parametersonSNR.Thewheatwasatgrowthstage47accordingtoZadoksscale(Zadokset135

al.,1974)(whilsttheheadwasbooting)atthetimeofscanning.Thestudiedconfiguration136

parametersareshowninTable1.Thesameareawasscannedintriplicateforthedifferent137

combinationofconfigurations.Thelaboratory(simulated-field)measurementconfiguration138

is shown in Figure1. For the indoorenvironment, two500wattdiffusedbroad spectrum139

halogen lamps were positioned at either end of the crop sample tray. The additional140

illuminationusedinthecurrentworkwasshownbyexperiencetoreducetheinfluenceof141

shadowwithinthecomplexandnon-homogenouscanopystructure.Imagerydatawasthen142

capturedatdifferentcameraandlightheights,lightdistances,cameraanglesandintegration143

times(measuredinmilliseconds(ms)),asillustratedinTable1.Lightanglewaskeptconstant144

at45°,whichisdebatedastheoptimalangletoprovidethestrongestresponse(Huadong,145

2001).Additionalopposinglightingwasusedtoreduceshadows(Barbedoetal.,2015).Four146

integrationtimesof10,20,50and1000mswereadoptedasthesecoverthemostpractical147

ranges.The1000msintegrationtimeillustratesthehighestpotentialtime,duringwhichthe148

systemwillabsorbthereflectedlighthence;thisisexpectedtogivethesmoothestspectra.149

Having determined a suitable configuration in the laboratory the next experiments were150

designedtoapply theconfigurations toa fieldenvironment,andassess the impactof the151

environmental factors;e.g., soilmoistureand totalnitrogen,onSNR.Fieldmeasurements152

wereconductedina9hafieldatDuckEndfarm,Wilstead,Bedfordshire,UK(52°05'46.3"N153

0°26'41.4"W),withanaverageannualrainfallof598mm.Thefarmhasacroprotationof154

8

barley, wheat and oil seed rape. Wheat was cultivated during the experiment in 2013155

croppingseason.Thedominantsoiltypeinthefieldisaclayloam,buthasasandfractiondue156

tounderlyinggraveldeposits.157

158

Figure1.Schematicillustrationofthelaboratory(simulatedfield)configurationsusedandthe159variablesimplementedtoobtainthehyperspectraldatawiththehighestsignal-to-noiseratio160(SNR).Thehyperspectralimagerisapassivesensor,buthasbeenappliedwithanexternal161halogenlightsource.TheLaserpointersallowtopreciselypositionthehyperspectralimager162overthetarget.163

2.2On-linesoilandcropmeasurementsinthefield164

Theon-linefieldmeasurementsincludedcropspectraandsoilMCandTN.Thereasonwhy165

MCandTNweretheselectedsoilproperties,isthattheformeraffectsthesoilphysicaland166

mechanicalconditions,influencingthesoildynamicbehaviourbelowthetractortyresduring167

theon-linemeasurement,whereasbothmaywellbelinkedwithcropgrowth(assumingthat168

TNisdirectlylinkedtomineralnitrogen).Itisworthmentioningthatmineralnitrogen(e.g.169

nitrateandammonia)cannotbemeasuredwithvisible(400-780nm)andnearinfrared(780-170

2200nm)(vis-NIR)spectroscopy(Kuangetal.,2012).Furthermore,theliteratureconfirmed171

9

thepotentialofthevis-NIRtomeasureMCandTN,whichisattributedtothedirectspectral172

responseofthesepropertiesintheNIRrange(Stenbergetal.,2010;Kuangetal.,2012).The173

SNRvaluesfromthecanopieshyperspectraldatawascomparedagainsttheon-linemeasured174

soil properties at the same location via Kappa statistics and visual comparisons. Thiswas175

essentialtoevaluatewhetherornottheoptimalmeasurementconfigurationestablishedin176

thelaboratoryisapplicableinthefield,andwhethermodificationsshouldbeconsidered.177

Anon-linevis-NIRsoilsensordevelopedbyMouazen(2006)wasusedinthisstudytomeasure178

soilMCandTN,withtheobjectiveofmappingthespatialvariabilityofthesetwoselectedsoil179

properties. The system consists of a subsoiler, opening a smooth trench at 15 cm depth180

(Mouazen et al., 2005). The sensor was mounted on a three-point linkage of a tractor181

travellingataspeedof3kmh-1andcollectingspectralsoildataat10mparallelintervals.In182

order tomeasure soil spectraanAgroSpecmobile, fibre type, vis–NIR spectrophotometer183

(Tec5TechnologyforSpectroscopy,Oberursel,Germany),withameasurementrangeof305–184

2200nmandalightsourceof20Wtungstenhalogenlampwereused(Kuang&Mouazen,185

2013).Adifferentialglobalpositioningsystem(DGPS)(EZ-Guide250,Trimble,California,USA)186

recordedthepositionoftheon-linespectrawithsub-meteraccuracy.Thecollectionofsoil187

spectraandDGPSreadingstookplaceat1secsamplingresolutionusingAgroSpecsoftware188

(Tec5TechnologyforSpectroscopy,Oberursel,Germany).189

The same hyperspectral imager (Gilden Photonics Ltd, Glasgow, UK) as that used in the190

laboratorytooptimisemeasurementconfigurationwasusedforon-linemeasurementthe191

wheat canopy in the field. The following hyperspectral measurement configuration was192

considered: an integration time of 50ms, a camera height of 0.3m and light height and193

distanceof1.2mandacameraangleof10°.Thehyperspectral imagerwasmountedona194

10

tractorboom(Fig.2)travelingatapproximately4kmh-1.Thedirectionandangleoftheimager195

waskeptconsistent,andadaywithuniformlyovercastweather(completecloudcover)was196

selected,whichhelpedpreventissuesofmovingshadowsfromlateralsunmovementonthe197

data(Westetal.,2003).Nevertheless,ahandheldLUXmeter(RS180–7133,RSComponents198

&Controls,India)wasutilizedtocheckthesunlightandreadingsrangedbetween1950and199

2000,indicatingnosignificantdifference.Thehyperspectralcamerawasmountedtotheside200

ofthetractor.Itcapturesimagesof1608pixelsperline,overaone-secondinterval,whichis201

subsequentlyloggedandgeo-locatedusingaDGPS.Thecollectedscanswerecorrectedby202

meansofadarkandawhitereference(spectralon99%whitereflectancepanel).Thelatter203

wasusedbeforespectralcapture,andatamaximumof30minuteintervalsuntilscanning204

wascompleted.205

206

Figure 2. Illustrates the on-line field hyperspectral measurement using hyperspectral207measurementconfiguration.208

2.3Dataanalyses209

2.1.1 Spectrographspectraldataprocessingandevaluation210

Quality of the wheat canopy spectra (measured both in the laboratory and field) was211

evaluatedbyanalysingSNR.TheSNRisdefinedhereasaratioofthesignalstrengthtothatof212

11

unwanted interference. A strong signal devoid of interference is the desired outcome in213

measurements. If the data is too noisy it can hide key features of the spectrum, and data pre-214

processingsuchassmoothingcanresultinthembeingremoved(Dasu&Johnson,2003).Anoisy215

spectrumcanresultinpoorcalibrationmodels,duetonoisebeingconsideredasafeature.There216

aremanyapplicationsforestimatingtheSNRfromsourcessuchaselectrical,chemical,and217

spectral.DifferentmethodsareoftenappliedtoestimatetheSNRvalue,dependingonthe218

datainput.Curran&Dungan(1989)usedabrighthomogenoussurfacetoestimatetheSNR219

andproducedamethodtermedthegeostatisticalmethodforremovalofperiodicnoisein220

images. Van derMeer (2000) used amethod outlined by Lee (1990) for remote spectral221

sensing, to asses SNR of Landsat Thematic imagery. Analysis of image SNR has also been222

conductedthroughproductionofhistogramsofanimage(Ramamurthyetal.,2004).Asimilar223

approachwasoutlinedearlierbysmith(1999),wherespectrawerecollectedfromgrayscale224

images.Smith(1999)exampledaSNRrangebetween0.5and2.0,statingthatthereisonly225

anissueiftheSNRvaluedropsbelow1.0.Wehaveusedacropcanopyinthisstudyinstead226

ofawhiteorgreyreferencepaneltocalculateSNR,sincetheintensionwastousetheoptimal227

configurationforon-linemeasurementofcropcanopyinthefield,wherevariationsincanopy228

architectureandleaforientationareforeseen.Similartothecurrentwork,Daumardetal.229

(2010)reliedoncropcanopyspectratomaximiseSNR,althoughdetailsofthecalculationof230

SNRwerenotprovided.However,theyconsideredcentralpixelsonly intheircalculations,231

whilstwehaveconsideredallpixelsinalineimageafterremovingnon-cropcanopyspectra.232

Thefollowingassumptionsweremadeinthecurrentstudytojustifytheselectionofacrop233

canopyforachievinganoptimalmeasurementsetup:234

1-Non-cropcontaminatedspectraincludingsoilandsoil-plant,etc.canbeexcludedfromthe235

analysis.Thiswasdonebycalculatingnormaliseddifferentialvegetationindex(NDVI)ofall236

12

pixels.SpectraofpixelswithNDVIvaluessmallerthan0.3wereremovedfromthecalculation237

ofSNR.ThismethodisusedbyBravoetal.(2004)andRouseetal.(1974).238

2-Spectraofremainingpixelscontainbothnoiseandactualcanopysignal,theintensityof239

whichdependsonthepixelpositionwithinacomplexcanopystructureofthecrop.Thiswill240

allowsmimickingthemeasurementofactualcanopy.241

3-Variationinpositionofpixelsforaseriesofscanscanbeminimisedbyfixingtheposition242

of crop trays, so that line images are collected from the same target area for different243

measurementconfigurations.Inthiscase,whilstuniformintensitycannotbeachievedacross244

thespectruminonescan,eachpixelhasalmostthesametargetobjectthroughoutallthe245

scans.246

ThecalculationoftheSNRwasdoneinthisstudyfollowingasimilarapproachadoptedby247

Ramamurthyetal.(2014)anddescribedearlierbySmith(1999).Asthedatacollectedissingle248

line 2-dimensional captures, the data was assessed as spectra rather than images. This249

methodofSNRwasselectedasitcouldbeusedasabasisofcomparisonbetweenindividual250

spectraldatacaptures.WecalculatedtheSNRofindividualwavelength(SNRw)asfollows:251

252

SNRw=Mw/SDw (1)253

254

Where:Mwisthemeanreflectancevalueofindividualwavelengththroughallthepixels,and255

SDwisthestandarddeviationofindividualwavelengthofallpixels(Fig.3).Itisworthnoting256

thatallpixelswereconsideredimportantinthecurrentworktocalculateSNR.Analternative257

13

approachtothismethodwouldhavebeentoselectcentralpixelsonlytocalculateSNR,a258

methodappliedbyDaumardetal.(2010).259

Themeanspectralsignaldescribeswhatisbeingmeasured,whereasthestandarddeviation260

representsnoiseandotherinterferenceforeachpixel(Smith,1999).Mwvaluesfordifferent261

wavelengthswerecalculatedonremaining967wavebands,afterremovingthespectralrange262

outsideofthe400to750nmrange,sincetheywerefoundtobenoisy.OnceMwandSDw263

arecalculatedforeachindividualwavelength,theSNRforaspectrum(SNRs)wascalculated264

asfollows(seeFig.3):265

266

SNRs=Ms/SDs (2)267

268

Where:Ms ismean reflectanceofallwavelengths ina spectrum (a scan)andSDs ismean269

standarddeviationofallwavelengthsinaspectrum.270

The SNRswas used in this study to evaluate the strength of scans, hence, the quality of271

spectralsignal.ItisworthnotingthatSDisnotimportantinitself,butonlyincomparisonto272

themean.Whileconvenientandstraightforward,thedeviationisastatisticdoesn'tfitwell273

with the physics of how signals operate (Smith, 1999). Therefore, a strong SNR with274

pronouncedabsorptionpeaksprovidesincreasedrecognitionfortheassociationofspectral275

signaturestoasubject.276

A principal component analysis (PCA) was also undertaken on laboratory measured277

hyperspectraldataonlyusingStatistica software (StatSoft inc.,OklahomaUSA) to identify278

14

parameterswiththegreatestimpactonSNR.PCAisastatisticalmethod,whichanalysesthe279

distribution of data inmultidimensional space (principal components) or similaritymaps,280

where similarities (within groups of variable) and differences between groups can be281

evaluated(Dytham,2011).Inthisinstance,SNR,integrationtime,lightheightanddistance,282

andcameraangleandheightwereusedasinputvariablesforthePCAanalysis.Additionally,283

a two-way analysis of variance (ANOVA) was carried out with RStudio software (RStudio284

Boston,MA)toestimatesignificantinfluencesofindividualvariablesandinteractionbetween285

variablesonSNR(Webster,2007;Dytham,2011).286

287

2.1.2 On-linesoilsensorcalibration288

LaboratorymeasurementsofMCandTNwerecarriedoutusingstandardreferencemethod.289

SoilMCwasmeasuredwith oven drying of samples at 105ºC for 24 h, whereas TNwas290

measuredwithaTrusSpecCNSspectrometer(LECOCorporation,St.Joseph,MI,USA),using291

theDumascombustionmethod(Dumas,1826ascitedbyBuckee,1994).Theon-linecollected292

soilspectraweresubjectedtopre-processingbeforemodelling.Pre-processingincludednoise293

cutbyremovingwavelengthssmallerthan400nmandlargerthan1900nm.Noisecutwas294

followedsuccessivelybymaximumnormalisation,firstderivativeandsmoothing.Partialleast295

squaresregression(PLSR)analysiswithleave-one-outfullcross-validationwascarriedoutto296

establishcorrelationsbetweensoilspectraandlaboratorymeasuredMCandTN.Spectrapre-297

processingandPLSRanalysiswerecarriedoutusingUnscrambler7.8software(CamoInc.;298

Oslo,Norway).299

300

15

2.4Mapping301

Maps for on-line vis-NIR predicted MC and TN and on-line spectrograph measured crop302

canopyofwheatweredevelopedusedArcGIS10(ESRI,California,USA)software.Krigingwas303

usedtodevelopmaps,assumingthatthedistanceordirectionbetweensamplepointsreflects304

aspatialcorrelationthatcanbeusedtoexplainspatialvariations.Theadvancedparameters305

option inArcGIS10software(ESRI,California,USA)allowedcontrolofthesemi-variogram306

usedforkriging,selectingsphericalasthebestfit.Thesemi-variogramvalueswerecalculated307

inRStudio(RStudio,Boston,MA).308

Thesimilarityassessmentbetweenmapscanbeperformedbyvisualinspectionandstatistical309

tests(Tekinetal.,2013).Thesimplestwayofcomparingbetweenmapsisbyvisualinspection,310

toconcludeonsimilaritiesthatmayexistornot.However,thisisinsufficient,asquantitative311

estimation of similarity is a more robust approach to adopt. To compare statistical312

relationship of pairs ofmaps, Kappa statistics (Cohen, 1960) analyseswere performed to313

calculateKappavalue(κ),usingSPSS(StatisticalPackagefortheSocialSciences,IBM,Armonk,314

New York, USA). However, before running Kappa statistics data was subjected to raster315

analyses to have the same 5 m by 5 m grid size for all maps, after which the data was316

normalised.317

16

318

Figure3.Schematicillustrationoutlininghowmean,standarddeviationandsignal-to-noise319ratio for wavelength (Mw, SDw, SNRw, respectively) and a spectrum (Ms, SDs, SNRs,320respectively)werecalculated.321

3 ResultsandDiscussion322

3.1Spectralqualityinthelaboratory323

Typical crop canopy spectra can be observed in Figure 4, which shows clear, noisy and324

saturatedspectra.Theclearandsaturatedspectracanbeobservedtobemorepronounced,325

whereasaweakabsorptioninthespectralsignatureandinterferenceinthenoisyspectrum326

leads to reduced quality, low SNR, masking detail in the signature and causing a loss of327

importantspectralinformationthroughtheentirespectralrangestudied.Theclearspectrum328

isthebestquality,andthetargettobeobtained.Thenoisyspectrumiscausedbythelow329

integration times, and greater distance of the Halogen light source. A strong SNR with330

pronounced absorption peaks would allow for a greater success in analysis of crop331

assessments and disease presence. Although, pre-processing of spectral data includes332

techniquessuchassmoothing, iftheprocessofcleaningthedatais intensiveduetonoisy333

spectraitcanleadtothelossofimportantspectralfeatures,andthusimpactonthesuccess334

ofanalysis(Dasu&Johnson,2003).335

17

Saturation predominantly occurs within the central pixels associated with the highest336

reflectance,causingdata inthepeaksoftheelectromagneticspectrumtobelost(starting337

around650nm).LeafreflectanceintheNIRrangeisaffectedbythestructureoftheplant338

leaves(Gatesetal.,1965),andcanberelatedtotheleafwaxcoating(Cameron1970).Inthe339

case of spectral saturation the data becomes unusable. In the remaining parts of the340

spectrum,however,particularlythevisiblerange(400–700nm),thereisalowerreflectance341

duetoalargerabsorptionofthelight,whichisattributedtothephotosyntheticpigmentsof342

theplantsleaves,(Gatesetal.,1965).343

When analysing the entire spectral signature saturation causes a reduction in sensitivity.344

Therefore,sensorconfigurationsleadingtosaturatedspectraldatawereremovedfromthe345

analysisofSNR.Allsaturateddatawasobtainedwithintegrationtimeof1000ms,thisisin346

line with the literature, where larger integration times can cause saturation of the data347

(Dell’Endice,2008).348

349

Figure4.Examplesofsmooth(thegrey linewith1.6signal-to-noiseratio(SNR),noisy(the350blacklinewith1.2SNR),andsaturated(thedottedlinewith1.8SNR)spectraofwheatcanopy.351

18

Thesaturatedspectrumflattensoffat680-750nmandimportantinformationislostonthe352peakaround700nmthatisotherwiseillustratedbytheclearspectra.353

3.2Hyperspectralimagesconfigurationparameters;laboratory354

TheintegrationtimeconsiderablyaffectedtheSNR,expressedasaverageandSDvalues(Eq.355

1),asshowninTable2.Thelongertheintegrationtimethegreatertheopportunityformore356

energy to be captured by the spectrograph. The 1000ms configuration was the highest357

potential integration time and was trialled (under field simulations) to see the potential358

highestSNR,whilstthemanufacturerrecommendedaround20msforlaboratoryuse.The359

resultsfromthe1000msintegrationtimeprovidedahighpercentageofsaturatedresults,360

thenon-saturatedrecordingswereofthesameSNRvaluestothatofthe50msintegration361

times, so integration times higher than 50mswere not trialled further.With varying the362

integrationtime,parametershaveconstantvaluesapartfromthecameraheight.Theoptimal363

cameraheightdecreaseswiththeintegrationtime,thecloserthecameratotheobjectthe364

shortertheintegrationtimerequired.Thismaymeanthatthemostinfluencingparameters365

ontheSNRaretheintegrationtimeandcameraheight.Otherstudiesdemonstratedthatthe366

movement(adjustments,bounceorvibration)betweenthe imagerandthesubjectduring367

integrationtimecancauseacompiledimagetobewarpedornoisy(Zhongetal.,2011).For368

on-linemeasurementstheeasiestvariabletocontrolisintegrationtime,asangleandheight369

can alter slightly as ground is uneven, crop stands vary and unavoidable movement in370

mountings. It becomes necessary to have the optimal configurations set initially but to371

understand therewouldbe slightdeviations.ANADIR cameraanglewas selected,due to372

reportsfromObertietal.(2014)andVanBeeketal.(2013).373

ANOVA showed that all individual variables and interactions of variables have significant374

influencesonSNRat the (0.005)95%confidence level,except for the interactionsof light375

19

distanceandintegrationtime(0.001),andlightdistance,cameraheightandintegrationtime376

(0.01). Therefore, the null hypothesise ‘that variables and subsequent interactions of377

variableswillhavenosignificanteffectonSNR’canberejected.378

Figure5illustratesthesimilaritymapsofprincipalcomponent(PC)1and2(a),andPC1and3379

(b)accountingfor47.62%and42.26%ofvariance,respectively.ExaminingtheplotofPC1vs380

PC2onecanobservethattheintegrationtime,SNRandcameraheightaregatheredinone381

group, which explains these to be closely related. Integration time shows the strongest382

influenceandcorrelationwithSNR,whereas,cameraheightdemonstratesthesecondclosest383

corresponding variable on SNR with the latter having a weaker influence (Fig. 5a). Light384

distance,lightheightandcameraangleseemtohaveonlyminorinfluencesonSNR,asthey385

make a separate group associated with PC2 with minor variance associated with PC1.386

Disregardingothervariables, itbecomesclearthata longer integrationtimeandasmaller387

cameraheightresultinahigherSNR.388

AlthoughthelightdistanceandheightarestronglyassociatedinthePCAsimilaritymaps(Fig.389

5a&b),theyhaveanegligibleinfluenceontheSNR.Thisdoesnotmeanthattheabsenceof390

lightwouldhavenoeffect.Aslongastherewasamplediffusedlight,inorderforthedetector391

tocollectphotons,lightvariablesappearedtohavelittleimpact.Althoughtheinfluenceof392

cameraheightwasthesecondlargestinthePC1-PC2similaritymap,inthePC1-PC3similarity393

map,thecameraheighthassmallinfluenceonSNRsimilartothelightvariables.Furthermore,394

the close correlation between camera height and integration time shown in the PC1-PC2395

similaritymapisnotobservedinthePC1-PC3similaritymap.Inthelattersimilaritymap(Fig.396

5b),thecameraanglehasthesecondlargestinfluenceonSNRaftertheintegrationtime.This397

means that both camera height and angle have strong influences on the SNR. Under398

20

laboratorymeasurementconditions,largercameraangleswerereportedtobebeneficialfor399

powderymildewrecognition in leavesofgrapevinesbyObertietal. (2014).Smallerview400

anglesarediscussedbyPiseketal.(2009)totheoreticallybebetterduetoobservingalarger401

area.Rautiainenetal.(2008)concludedgreaternadiranglesaremoresuitableforviewing402

thetopcanopyandhadaverylimitedviewoftheunderstory.Thisissupportedbythefinding403

that80%ofthecropyieldiscalculatedfromthehealthofthetop3leaves(HGCA,2008).The404

correlationbetweengreenleafretentionandyieldhasbeenobservedinanumberoftrials405

(Reynolds et al., 2009; Ali et al., 2010; Hunt & Poole, 2010) and can be observed with406

referencetotrialworkinbarleyconductedbytheauthorsin2009,whereevery1%reduction407

ingreenleafareaonflag-1atGS80correlatedtoa20kg/halossinyield.Thisistruewhen408

applyingacameraangle.Butwhenassessinglightconditions,anoff-nadiranglecancreate409

more lightingvariability,duetoshadyandsunlitareasof thecrop.VanBeeketal. (2013)410

foundthatforsmalleroff-nadirviewingangles(<20°)ofthesensor,thesunorientationwas411

foundnottobeimportant.This,alongwithissuesofshadowfromtheinfieldmountings,is412

whysmallanglesfortheconfigurationswereselected.413

21

414

Figure5.Principalcomponentanalysissimilaritymapsdevelopedforprincipalcomponents1415and2(a)andforprincipalcomponents1and3(b).Inputvariablesarecameraandlightheight,416lightdistance,cameraangle,integrationtimeandsignal-to-noiseratio(SNR)417

418

22

TomakeadecisiononanoptimalconfigurationthatwouldresultinthehighestSNR,wasnot419

a straightforward process. However, when SNR values are arranged according to the420

integration time (Table 2), a clear trend is observed. For example, SNR increased with421

integrationtime,wherethehighestvaluesofSNRwereobtainedwithanintegrationtimeof422

1000ms, however, theywere onlymarginally higher than the SNR values at 50ms. Since423

several variable combinations resulted in similar SNR values, it is perhaps premature to424

suggesttheoptimalconfigurationasthesinglehighest,soconfigurationswithSNRvariability425

of less than 5% from the highest reading (of each integration time)were considered for426

furtherevaluation.ThefurtheranalysisconfirmsthatthehighestSNRoccurswiththesame427

configurations of 1.2m, 1.2m, and 10° of light height, light distance and camera angle,428

respectively.Asstatedearlier,thecameraheightisnegativelycorrelatedwiththeintegration429

time(Table2).Apossibletheoryforreoccurrenceoftheseconfigurationscouldbethatthey430

allowforthegreatestamountofreflectedlighttobecapturedbythedetector.Amongthe431

integration time steps of 10, 20, 50 and 1000 ms selected, a practical range for on-line432

measurementisbetween10msand50ms,arangewhichissuggestedbythemanufacturers.433

Assumingthatthebest integrationtimeforpracticalon-line(mobile)measurement inthe434

field is 50ms, the optimal configuration parameters of 1.2m, 1.2m, 0.3m and 10° are435

recommendedforlightheight,lightdistance,cameraheight,andcameraangle,respectively.436

TheaverageSNRfor this integrationtime is1.669 (seen in table2),which in referenceto437

Smith (1999)webelieve is a strong signal for a crop canopy.Theseoptimal configuration438

parameterswereadoptedfortheon-linemeasurementofwheatcanopymeasurementinthis439

study.440

441

442

23

Table2.Averageandstandarddeviation(SD)valuesofthehighestsignal-to-noise-ratio443(SNR)obtainedwithdifferentintegrationtime,cameraandlightsourcesettingswhen444scanningawheatcanopy.Theoreticalforwarddistancetravelled(andcapturedtoasingle445dataline)ifappliedonamovingplatformatfieldscale.446

447

3.3Hyperspectralimager;on-linemeasurement448

Duringtheon-linemeasurement,itwasnoticedthattherewasanunavoidablebounceinthe449

boomobservedtobeat±0.2moftheoriginalheightofthemounting(setat0.3mabovethe450

crop canopy). On inspection of the data the camera height from the object affected the451

uniformityoflightintensitymeasuredacrossthepixels,(particularlyatthebeginningandend452

of the captured line). Therefore, for calibrating the on-line hyperspectral scans, it is453

recommendedtoovercomethisfluctuationbyremovingthefirstandlast320pixelsfromthe454

spectral data. This is specific to this hyperspectral imager but is an interesting factor for455

consideration.456

Comparingthelaboratorywithon-linefieldmeasuredcanopyspectra,onecanobservethat457

thelaboratoryreflectancescanstobemarginallyhigherandmoreconsistentthantheon-line458

scans(Fig.6).Initially,theon-linespectrashowedvariationincanopyspectrathroughoutthe459

field,whichappearedtobeinresponsetodifferentcropspatialconditions.Forexample,the460

fieldscan1referstocanopyofwaterstressedwheatplants,whereasfieldscan2refersto461

Average,

SNR SD

Integration

time,

ms

Light

height,

m

Light

distance,

m

Camera

height,

m

Camera

angle,

deg

Distance

travelled,

m

1.688 0.102 1000 1.2 1.2 0.15 10 4

1.669 0.160 50 1.2 1.2 0.30 10 0.2

1.471 0.103 20 1.2 1.2 0.45 10 0.08

1.386 0.078 10 1.2 1.2 0.45 10 0.04

24

healthierwheatplants.Cropstartstoshowyellowcolourasasymptomofwaterstress,which462

leadstoreducelightabsorptionandincreasedreflectanceasshowninfieldscan1(Fig.6).463

More detailed analysis of crop and soil properties needs to be assessed to understand464

differencesinqualityofcanopyspectracollectedinthefield.465

466

Figure6.Comparisonofcanopyspectraofwheatcropobtainedinthelaboratory(thedashed467line)andon-lineinthefield(thedottedandgreylines).Cropwasofthesamecultureandat468comparable growth stages. Laboratory scanswere collected under the suggested optimal469configurations.Theon-linefieldscansare:1)fieldscan1(thegreyline)isofamorewater470stressedplantand2)fieldscan2(thedottedline)isofalesswaterstressedplant.471

472

25

473

Figure7.Mapsofon-linemeasuredsoilmoisturecontent(MC)(a)totalnitrogen(TN)(b),and474theaveragesignal-to-noiseratio(SNR)perscan(c).475

476

3.4 Influence of soil properties on signal-to-noise ratio during the on-line477

measurement478

Theon-linemeasuredsoilMCandTNmapsofthefieldprovidedavisualexplanation(Fig.7)479

foradropinabsorbancewithincertainareasofthefield.ComparingtheMCmapwiththe480

SNRmap,onecandrawageneralconclusionthatareasoflowMCcorrespondwithareasof481

26

highSNR,andviceversa(areasofhighMCbeingoflowSNR).Thiscanbeexplainedbythe482

factthatsoildeformationunderthetractortyreinwetsoilsislargerthanthatindriersoil483

conditions.Alargersoildeformationshouldresultinalargerfluctuationincameraheightand484

angle,comparedtothebaselinesetup,whichmayleadtoreducingtheSNR.Workcarriedout485

bySöhne(1958)showedthatanincreaseintheMCofasoilandtheincreaseinpayloadona486

tyrebothincreasethedepthofsoildeformation.487

Bothon-linemeasuredMCandTNwerefoundtohavesignificanteffectsontheSNRofthe488

wheatcanopyspectraat95%confidence.ThekappavaluesbetweentheSNRmaponone489

handandTNandMCmapsontheotherhandconfirmspatialsimilarityordifferencethefield.490

Landis& Koch (1977) classified Kappa values into different categories: 0–0.20, 0.21–0.40,491

0.41–0.60, 0.61–0.80, and 0.81–1, which indicate slight, fair, moderate, substantial, and492

almostperfectagreement,respectively.ResultsshowthattheKappavaluebetweenTNand493

SNRisrathersmall(kappa=0.56),indicatingmoderatesimilaritybetweenthesetwomaps.494

SinceTNisthemainsourceofmineralnitrogenessentialforcropgrowthanddevelopment,495

itcanbeinfluentialonthequalityofcropcanopyandSNR.ThekappavaluebetweenMCand496

SNR was much higher (kappa = 0.75) than that between TN and SNR map, confirming497

substantialsimilaritybetweenthetwomaps.Thissupportstheearliersuggestionaboutthe498

influence of MC on soil deformation that changes the initial (optimal) hyperspectral499

measurement configuration obtained in the laboratory and implemented for on-line500

measurementinthefield.Inordertoreducethenegativeinfluenceofsoildeformationon501

SNR due to high soil MC during on-line scanning, proper spectra pre-processing is502

recommendede.g.,normalisation,derivation,andmultiplicativescattercorrections.Further503

researchisrequiredtoconfirmthisassumption,aswhenmeasuringon-line,deviationsfrom504

27

the optimum configuration (due to tractor vibration, bounce in the boom and soil505

deformation)wereunavoidable.506

507

4 Conclusions508

This study was undertaken to determine an optimal measurement configuration of a509

hyperspectrallineimager(400-750nm)byevaluatingtheindividualandinteractioneffectsof510

thesystemsconfigurations,onthehyperspectralcalculatedSNRformeasurementofawheat511

canopy under laboratory scanning conditions. Optimal configurationwas determined and512

implementedforon-linefieldmeasurement.Theinfluenceofon-linemeasuredsoilmoisture513

content(MC)andtotalnitrogen(TN)onSNRwasevaluated.Resultsallowedthefollowing514

conclusionstobedrawn:515

1-Theintegrationtimefollowedbythecameraheightandcameraangleappearedtohave516

thelargestinfluenceoftheSNR.Alongintegrationtime(>50ms)wasofanegligibleinfluence517

andonlyslightlyincreasedtheSNR,butresultinspectralsaturation,henceshouldbeavoided.518

3-ThePCAsimilaritymapshowedthatthelightheightanddistancehaveastrongcorrelation519

witheachotherbutaminimalinfluenceonSNR.520

4-Bothon-linemeasuredMCandTNwerefoundtohavesignificanteffectsontheSNRofthe521

wheatcanopyspectraat95%confidence.Theon-linesoilmeasurementsrevealedstronger522

spatialsimilaritybetweenthehyperspectralSNRandMCmaps(kappavalue=0.75),which523

wasattributedtosoildeformationbelowthetractortyre.524

5-Thevariablereflectedlightintensitycapturedbythedifferentpixelsacrossthelineimagery525

isaninterestingfactortotakeintoaccount,duetotheimpactofvaryingcameraheightduring526

28

the on-linemeasurement.Whilst the solution suggested here is appropriate, it is camera527

specific.528

FurtherworkisplannedtoovercomevariationinSNRassociatedwithcameraheightchanges529

(vibration,bounceintheboom,andsoildeformationduringtheon-linemeasurement)bythe530

implementationofaproper spectrapre-processing. It isalsoplanned to implement these531

hyperspectral measurement configurations for on-line measurements of crop canopy for532

detectionofcrophealthanddiseasepresence.533

534

Acknowledgement535

Weacknowledge the funding received forFarmFUSEproject fromthe ICT-AGRIunder the536

EuropeanCommission’sERA-NETschemeunderthe7thFrameworkProgramme,andtheUK537

DepartmentofEnvironment,FoodandRuralAffairs(contractno:IF0208).538

539

References540

AliM.A.,HussainM.,KhanM.I.,AliZ.,ZulkiffalM.,AnwarJ.,SabirW.,ZeeshanM.(2010).541

Source-sink relationship between photosynthetic organs and grain yield attributes during542

grainfillingstageinspringwheat(Triticumaestivum).InternationalJournalofAgricultureand543

Biology,12:pp.509–515.544

Asner,G.P.(1998).Biophysicalandbiochemicalsourcesofvariabilityincanopyreflectance.545

RemotesensingofEnvironment,64(3),234-253.546

29

Barbedo, J.G., Tibola,C. S.,& Fernandes, J.M. (2015).Detecting Fusariumheadblight in547

wheatkernelsusinghyperspectralimaging.BiosystemsEngineering,131,65-76.548

Bock,C.H.,Graham,J.H.,Gottwald,T.R.,Cook,A.Z.,&Parker,P.E. (2010).Windspeed549

effectsonthequantityofXanthomonascitrisubsp.citridisperseddownwindfromcanopies550

ofgrapefruittreesinfectedwithcitruscanker.PlantDisease,94(6),725-736.551

Bravo,C.,Moshou,D.,Orberti,R.,West,J.,McCartney,A.,Bodria,L.andRamon,H.(2004).552

Foliar disease detection in the field using optical sensor fusion.Agricultural Engineering553

International:CIGRJournalofScientificResearchandDevelopment.ManuscriptFP04008.Vol.6,554

14p.555

Buckee, G. K. (1994). Determinationoftotal nitrogeninbarley,maltandbeerbyKjeldahl556

proceduresandtheDumascombustionmethod—Collaborativetrial.JournaloftheInstitute557

ofBrewing1994,100(2)pp.57–64.558

Cameron,R.J.(1970).LightintensityandthegrowthofEucalyptusseedlings:II.Theeffectof559

cuticular waxes on light absorption in leaves of Eucalyptus species. Australian Journal of560

Botany,18:pp.275–284.561

Cohen, J., 1960. Kappa: Coefficient of concordance.Educational Psychologist,562

Measurement,20,p.37563

Coops,N.C.,Smith,M.L.,Martin,M.E.,&Ollinger,S.V.(2003).Predictionofeucalyptfoliage564

nitrogencontentfromsatellite-derivedhyperspectraldata.IEEETransactionsonGeoscience565

andRemoteSensing,41(6),1338-1346.566

30

Curran,P.J.andDungan,J.L.,1989.Estimationofsignal-to-noise:anewprocedureappliedto567

AVIRISdata.IEEETransactionsonGeoscienceandRemoteSensing,27(5):pp.620-628.568

Dasu, T.,& Johnson, T. (2003).Exploratorydatamininganddata cleaning(Vol. 479). John569

Wiley&Sons.570

Daumard,F.,Champagne,S.,Fournier,A.,Goulas,Y.,Ounis,A.,Hanocq, J.F.andMoya, I.,571

2010. A field platform for continuous measurement of canopy fluorescence.IEEE572

TransactionsonGeoscienceandRemoteSensing,48(9),pp.3358-3368.573

Dell’Endice, F. (2008). Improving the Performance of Hyperspectral Pushbroom Imaging574

Spectrometers for Specific Science Applications. InISPRS 2008: Proceedings of the XXI575

Congress: Silk Road for Information from Imagery: The International Society for576

PhotogrammetryandRemoteSensing(pp.215-220).577

Demetriades-Shah,T.H.,Steven,M.D.andClark,J.A.,1990.Highresolutionderivativespectra578

inremotesensing.RemoteSensingofEnvironment,33(1):pp.55-64.579

Dytham,C.(2011).Choosingandusingstatistics:abiologist'sguide.JohnWiley&Sons.580

Gates, D.M., Keegan, H.J., Schleter, J.C. andWeidner, V.R. (1965). Spectral properties of581

plants.Appliedoptics,4(1),pp.11-20.582

Gnyp,M.L.,Miao,Y., Yuan,F.,Ustin, S.L., Yu,K., Yao,Y.,Huang,S.andBareth,G. (2014).583

Hyperspectralcanopysensingofpaddyriceabovegroundbiomassatdifferentgrowthstages.584

FieldCropsResearch,155:pp.42-55.585

Gómez-Casero,M.T.,Castillejo-González, I.,García-Ferrer,A.,Peña-Barragán, J.M., Jurado-586

Expósito,M.,García-Torres,L.andLópez-Granados,F.(2010).Spectraldiscriminationofwild587

31

oatandcanarygrassinwheatfieldsforlessherbicideapplication.AgronomyforSustainable588

Development,30(3),pp.689-699.589

Hahn,F.,(2009).Actualpathogendetection:Sensorsandalgorithms—areview.Algorithms2,590

301–338.591

Herrmann, I., Shapira, U., Kinast, S., Karnieli, A. and Bonfil, D.J., 2013. Ground-level592

hyperspectral imageryfordetectingweeds inwheatfields.Precisionagriculture,14(6):pp.593

637-659.594

HomeGrownCerealAuthority(HGCA)(2008).Thespringwheatdiseasemanagementguide.595

Warwickshire:HomeGrownCerealsAuthority(HGCAPublication).596

Huadong,G. (2001).ApplicationsofRadarRemoteSensing inChina.NewYork:Taylorand597

Francisinc.56.598

Huete,A.R.(1988).Asoilvegetationadjustedindex(SAVI).RemoteSensingofEnvironment,599

25:pp.295–309.600

HuntJ.,&PooleN.(2010).Simulatingleafareadurationtopredictyieldresponsetofoliar601

fungicide in wheat and barley. In: Dove H, Culvenor RA, editors. Food security from.602

sustainableagriculture.Proceedingsof15thAgronomyConference;2010Nov;Lincoln,New603

Zealand.604

Kuang,B.,Mahmood,H.S.,Quraishi,M.Z.,Hoogmoed,W.B.,Mouazen,A.M.&vanHenten,605

E.J. (2012). 4 Sensing Soil Properties in the Laboratory, In Situ, and On-Line: A606

Review.AdvancesinAgronomy,114(1),pp.155-223.607

32

Kuang,B.&Mouazen,A.M.,(2013).Non-biasedpredictionofsoilorganiccarbonandtotal608

nitrogen with vis–NIR spectroscopy, as affected by soil moisture content and609

texture.Biosystemsengineering,114(3),pp.249-258.610

Landis,J.R.,&Koch,G.G.(1977).Themeasurementofobserveragreementforcategorical611

data.Biometrics,33,159-174.612

Lee,J.B.,Woodhyatt,S.,andBerman,M.(1990).Enhancementofhighspectralresolution613

remote-sensingdatabyanoise-adjustedprincipalcomponentstransform.IEEETransactions614

onGeoscienceandRemoteSensing,28:pp.295–304615

Lenk,S.,Chaerle,L.,Pfündel,E.E.,Langsdorf,G.,Hagenbeek,D.,Lichtenthaler,H.K.,VanDer616

Straeten,D.,&Buschmann,C.,(2007).Multispectralfluorescenceandreflectanceimagingat617

theleaflevelanditspossibleapplications.JournalofExperimentalBotany58(4),807–814.618

López,M.M.,Bertolini,E.,Olmos,A.,Caruso,P.,Gorris,M.T.,Llop,P.,Penyalver,R.,&Cambra,619

M., (2003). Innovative tools for detection of plant pathogenic viruses and bacteria.620

InternationalMicrobiology6,233–243.621

Moshou,D.,Bravo,C.,Oberti,R.,West, J.,Bodria,L.,McCartney,A.,&Ramon,H., (2005).622

Plant disease detection based on data fusion of hyper-spectral and multi-spectral623

fluorescenceimagingusingKohonenmaps.Real-TimeImaging11(2),75–83.624

Mouazen, A.M. (2006). Soil SurveyDevice. International publication published under the625

patent cooperation treaty (PCT). World Intellectual Property Organization, International626

Bureau. International Publication Number: WO2006/015463; PCT/BE2005/000129; IPC:627

G01N21/00;G01N21/00.628

33

Mouazen,A.M.;Anthonis, J.;&Ramon,H., (2005).Anautomaticdepthcontrolsystemfor629

online measurement of spatial variation in soil compaction, Part 4: improvement of630

compactionmapsbyusingaproportionalintegrativederivativedepthcontroller.Biosystems631

Engineering,90(4),409-418.632

Oberti,R.,Marchi,M.,Tirelli,P.,Calcante,A.,Iriti,M.,&Borghese,A.N.(2014).Automatic633

detection of powderymildew on grapevine leaves by image analysis: Optimal view-angle634

rangetoincreasethesensitivity.ComputersandElectronicsinAgriculture,104,1-8.635

Okamoto, H. & Lee, W.S. (2009). Green citrus detection using hyperspectral636

imaging.ComputersandElectronicsinAgriculture,66(2):pp.201-208.637

Pinter, JR, P.J. and Jackson, R.D. (1985). Sun-angle and canopy-architecture effects on the638

spectralreflectanceofsixwheatcultivars.INT.J.REMOTESENSING,6(12):pp.1813-1825.639

Pisek, J., Chen, J.M.,Miller, J. R., Freemantle, J. R., Peltoniemi, J. I., & Simic, A., (2009).640

Mapping forest background reflectance in a boreal region using multi-angle Compact641

AirborneSpectrographic Imager (CASI)data. IEEETransactionsonGeoscienceandRemote642

Sensing,47.doi:10.1109/TGRS.2009.2024756.643

Ramamurthy,R.,Canning,C. F., Scheetz, J.P.,&Farman,A.G. (2004). Impactofambient644

lightingintensityanddurationonthesignal-to-noiseratioof imagesfromphotostimulable645

phosphor plates processed usingDenOptix® and ScanX® systems. Dentomaxillofac Radiol,646

33(5),307-11647

Rautiainen,M.,Lang,M.,Mõttus,M.,Kuusk,A.,Nilson,T.,Kuusk,J.&Lükk,T.,2008.Multi-648

angular reflectance properties of a hemiboreal forest: An analysis using CHRIS PROBA649

data.RemoteSensingofEnvironment,112(5),pp.2627-2642650

34

Reynolds,M.,Foules,M.J.,Slafer,G.A.,Berry,P.,Parry,M.A.,Snape,J.W.,Angus,W.J.(2009).651

Raisingwheatpotential.JournalofExperimentalBotany.60:pp.1899–1918.652

Roggo,Y.,Duponchel,L.,Huvenne,J.P.(2003).Comparisonofsupervisedpatternrecognition653

methodswithMcNemar’sstatisticaltest:applicationtoqualitativeanalysisofsugarbeetby654

near-infraredspectroscopy.AnalyticaChimicaActa,477(2):pp.187–200.655

Rouse, J.W. Jr., R.H. Haas, D.W. Deering, J.A. Schell and J.C. Harlan. 1974.Monitoring the Vernal656

Advancement andRetrogradation (GreenWave Effect) ofNatural Vegetation.NASA/GSFC Type III657

FinalReport,Greenbelt,MD.,371.658

Sankaran,S.,Mishra,A.,Ehsani,R.,&Davis,C.(2010).Areviewofadvancedtechniquesfor659

detectingplantdiseases.ComputersandElectronicsinAgriculture,72(1),1-13.660

Schmale,D.G.,III,&Bergstrom,G.C.(2003).Fusariumheadblightinwheat.ThePlantHealth661

Instructor.http://dx.doi.org/10.1094/PHI-I-2003-0612-01.662

Smith,S.W.(1999).TheScientistandEngineer'sGuidetoDigitalSignalProcessing.2nded.663

SanDiego:CaliforniaTechnicalPublishing,(Chapter2pp13-16).664

Sohne,W.(1958).Fundamentalsofpressuredistributionandsoilcompactionundertractor665

tires.AgriculturalEngineering,39,290666

Tekin,Y.,Kuang,B.,&Mouazen,A.M.(2013).Potentialofon-linevisibleandnearinfrared667

spectroscopy for measurement of pH for deriving variable rate lime668

recommendations.Sensors,13(8),10177-10190.).669

VanBeek,J.,Tits,L.,Somers,B.,&Coppin,P.(2013).Stemwaterpotentialmonitoringinpear670

orchardsthroughWorldView-2multispectralimagery.RemoteSensing,5(12),6647-6666.671

35

Van derMeer, F. and De Jong, S.M., 2000. Improving the results of spectral unmixing of672

Landsat Thematic Mapper imagery by enhancing the orthogonality of end-673

members.InternationalJournalofRemoteSensing,21(15):pp.2781-2797.674

Webster, R. (2007). Analysis of variance, inference, multiple comparisons and sampling675

effectsinsoilresearch.EuropeanJournalofSoilScience,58(1),pp.74-82.676

West, J.S., Bravo, C., Oberti, R., Lemaire, D.,Moshou, D. andMcCartney, H.A., 2003. The677

potentialofopticalcanopymeasurementfortargetedcontroloffieldcropdiseases.Annual678

reviewofPhytopathology,41(1),pp.593-614.679

Wu,D.,Feng,L.,Zhang,C.,&He,Y.,(2008).EarlydetectionofBotrytiscinereaoneggplant680

leavesbasedonvisibleandnear-infraredspectroscopy.TransactionsoftheASABE51(3),pp.681

1133–1139.682

Yuan,L.,Pu,R.,Zhang,J.,Wang,J.andYang,H.(2016).Usinghighspatialresolutionsatellite683

imagery formappingpowderymildewat a regional scale.PrecisionAgriculture,17(3): pp.684

332-348.685

Zadoks,J.C.;T.T.Chang,C.F.Konzak(1974)."Adecimalcodeforthegrowthstagesofcereals.".686

WeedResearch14(6):415–421.doi:10.1111/j.1365-3180.1974.tb01084.x.687

Zhong,C.,Ding,Y.,&Fu,J.(2011).ImageForwardMotionCompensationMethodforSome688

Aerial Reconnaissance Camera Based on Neural Network Predictive Control.Advanced689

ResearchonComputerScienceandInformationEngineering.152(1),pp.237-244.690