AgriculturalApplicationsofHyperspectralImagingfromaUAS

J.Kerekes,C.Salvaggio,J.vanAardt,T.Bauch,N.Raqueño, E. Myers, E. Hughes DigitalImagingandRemoteSensingLaboratoryChesterF.CarlsonCenterforImagingScience

RochesterInstituteofTechnologyRochester,NewYork,USA

Abstract

Hyperspectralimagingfromsmallunmannedaerialsystems(sUAS)hasgrowninpopularitywith the advent of small, relatively low-cost sensor systems togetherwith availability ofstablemulti-rotorplatforms.Agriculturalapplicationsofremotesensing,suchascrophealthmonitoringordiseasedetectionpreviouslydonewithsatellitesoraircraftonlyafewtimesduringagrowingseason,cannowbeperformedseveraltimesduringtheseason.Inadditiontothesemorefrequentcollectionopportunities,therelativelylowflyingheightofsUASleadstoveryhighspatialresolutionaffordingadetailedviewofthecropatthedifferentgrowthstages. During the summer of 2018RIT researchersperformed repeat-visit collectionsofseveralagriculturalfieldswiththeirMX1multi-modalsensorpayloadpackage.Thispackageincluded several sensors on a single Matrice 600 Pro UAS: 1) a Headwall Nano VNIRhyperspectral imager;2)aVelodyneVLP-16lidarsystem;3)aTamarisk thermal infraredimager;and4)visiblecolorcamera.AlsoonthepackageisahighprecisionGPS/IMUsystemforprecisegeolocation.

Figure1.Left:MX1sensorpackageonMatrice600;Right:PhotofromUSDABeltsvillesite.

Agricultural research applications studied with these data include sensing forproactivemanagementofwhitemoldinsnapbeansviafloweringdetection(>90%accuracy)[1],vineyardmoisturestressassessment(Si-rangevs.shortwave-infraredspectralregions),and corn phenological monitoring (multi-temporal sUAS and daily Planet Labs imagery).Preliminaryevaluationsof thehyperspectraldatahave indicatedtheability tosense leaf-levelbiochemicalcharacteristicsofthecrops,inadditiontocanopycharacteristicssuchasleaf area index. Best practices for sensor calibration have also been studied, specificallycomparingtheempiricallinemethod(ELM)toat-altituderadianceratios,towardpractical,real-time spectral calibration [2].As the research continues, thedata from the additionalsensorsontheplatformwillbeintegratedintotheanalysistodemonstratethemeasurementofplantgeometriesfromthelidardataaswellassupportingevapotranspirationstudieswiththethermaldata.

Someexamplesofthehyperspectraldataareprovidedbelow.Figure2showsageorectifiedRGBcompositefromtheHeadwallNanoHSIcameraoveranareaofatestcornfieldattheUSDABeltsville research site. Figure3 showsan example fieldmean spectrum from thatcollectaswellasspectralstatisticsforfloweringandnon-floweringsnapbeanplants.

Figure2.RGBimagefromUSDABeltsvillesite.

Figure3.SampleNanospectra(272bands).Left:FieldaveragecornspectrumfromUSDABeltsvillesite;Right:Spectralstatisticsfromsnapbeantestsite.References[1] E.Hughes, S. Pethybridge, J.Kikkert, C. Salvaggio, J. vanAardt, "Snapbean flowering

detection from UAS imaging spectroscopy," Proc. 14th International Conference onPrecisionAgriculture(24-27June2018).

[2] B.Mamaghani,G.Sasaki,R.Connal,K.Kha,J.Knappen,R.Hartzell,E.Marcellus,T.Bauch,N.Raqueño,C. Salvaggio, "An initial explorationof vicariousand in-scene calibrationtechniques forsmallunmannedaircraftsystems,"Proc.SPIE10664,AutonomousAirand Ground Sensing Systems for Agricultural Optimization and Phenotyping III,1066406(21May2018).