Evaluating UAV-Based Remote Sensing for Hay Yield Estimation.

Kyuho Lee, Kenneth A Sudduth, Jianfeng Zhou
Author Information
  1. Kyuho Lee: Department of Chemical and Biomedical Engineering, University of Missouri, Columbia, MO 65211, USA. ORCID
  2. Kenneth A Sudduth: USDA-ARS Cropping Systems and Water Quality Research Unit, Columbia, MO 65211, USA. ORCID
  3. Jianfeng Zhou: Division of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA. ORCID

Abstract

(1) Background: Yield-monitoring systems are widely used in grain crops but are less advanced for hay and forage. Current commercial systems are generally limited to weighing individual bales, limiting the spatial resolution of maps of hay yield. This study evaluated an Uncrewed Aerial Vehicle (UAV)-based imaging system to estimate hay yield. (2) Methods: Data were collected from three 0.4 ha plots and a 35 ha hay field of red clover and timothy grass in September 2020. A multispectral camera on the UAV captured images at 30 m (20 mm pixel) and 50 m (35 mm pixel) heights. Eleven Vegetation Indices (VIs) and five texture features were calculated from the images to estimate biomass yield. Multivariate regression models (VIs and texture features vs. biomass) were evaluated. (3) Results: Model R values ranged from 0.31 to 0.68. (4) Conclusions: Despite strong correlations between standard VIs and biomass, challenges such as variable image resolution and clarity affected accuracy. Further research is needed before UAV-based yield estimation can provide accurate, high-resolution hay yield maps.

Keywords

References

  1. Sensors (Basel). 2023 Jan 31;23(3): [PMID: 36772581]

Grants

  1. 5070-12000-001-004S/Agricultural Research Service

MeSH Term

Remote Sensing Technology
Biomass
Unmanned Aerial Devices
Crops, Agricultural

Word Cloud

Created with Highcharts 10.0.0hayyieldUAV0VIsbiomasssystemsresolutionmapsevaluatedsystemestimate4ha35multispectralimagesmmmpixeltexturefeaturesimage1Background:Yield-monitoringwidelyusedgraincropslessadvancedforageCurrentcommercialgenerallylimitedweighingindividualbaleslimitingspatialstudyUncrewedAerialVehicle-basedimaging2Methods:DatacollectedthreeplotsfieldredclovertimothygrassSeptember2020cameracaptured302050heightsElevenVegetationIndicesfivecalculatedMultivariateregressionmodelsvs3Results:ModelRvaluesranged3168Conclusions:DespitestrongcorrelationsstandardchallengesvariableclarityaffectedaccuracyresearchneededUAV-basedestimationcanprovideaccuratehigh-resolutionEvaluatingUAV-BasedRemoteSensingHayYieldEstimationyield-monitoringprecisionagricultureremote-sensingtechnology

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