Agricultural Applications of Hyperspectral Imaging from a UAS J. Kerekes, C. Salvaggio, J. van Aardt, T. Bauch, N. Raqueño, E. Myers, E. Hughes Digital Imaging and Remote Sensing Laboratory Chester F. Carlson Center for Imaging Science Rochester Institute of Technology Rochester, New York, USA Abstract Hyperspectral imaging from small unmanned aerial systems (sUAS) has grown in popularity with the advent of small, relatively low-cost sensor systems together with availability of stable multi-rotor platforms. Agricultural applications of remote sensing, such as crop health monitoring or disease detection previously done with satellites or aircraft only a few times during a growing season, can now be performed several times during the season. In addition to these more frequent collection opportunities, the relatively low flying height of sUAS leads to very high spatial resolution affording a detailed view of the crop at the different growth stages. During the summer of 2018 RIT researchers performed repeat-visit collections of several agricultural fields with their MX1 multi-modal sensor payload package. This package included several sensors on a single Matrice 600 Pro UAS: 1) a Headwall Nano VNIR hyperspectral imager; 2) a Velodyne VLP-16 lidar system; 3) a Tamarisk thermal infrared imager; and 4) visible color camera. Also on the package is a high precision GPS/IMU system for precise geolocation. Figure 1. Left: MX1 sensor package on Matrice 600; Right: Photo from USDA Beltsville site. Agricultural research applications studied with these data include sensing for proactive management of white mold in snap beans via flowering detection (>90% accuracy) [1], vineyard moisture stress assessment (Si-range vs. shortwave-infrared spectral regions), and corn phenological monitoring (multi-temporal sUAS and daily Planet Labs imagery). Preliminary evaluations of the hyperspectral data have indicated the ability to sense leaf- level biochemical characteristics of the crops, in addition to canopy characteristics such as leaf area index. Best practices for sensor calibration have also been studied, specifically comparing the empirical line method (ELM) to at-altitude radiance ratios, toward practical, real-time spectral calibration [2]. As the research continues, the data from the additional sensors on the platform will be integrated into the analysis to demonstrate the measurement of plant geometries from the lidar data as well as supporting evapotranspiration studies with the thermal data.

J. Kerekes, C. Raqueño, Salvaggio, J. van Aardt, T. Bauch, N. E. …€¦ · Agricultural Applications of Hyperspectral Imaging from a UAS J. Kerekes, C. Raqueño, Salvaggio, J

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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.

Page 2: J. Kerekes, C. Raqueño, Salvaggio, J. van Aardt, T. Bauch, N. E. …€¦ · Agricultural Applications of Hyperspectral Imaging from a UAS J. Kerekes, C. Raqueño, Salvaggio, J

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).