use of drones in agriculture – prospects and limitations ... · plantekongres 2014, 14.-15....

1 / 27 Plantekongres 2014, 14.-15. Januar 2014, Herning, Denmark

Use of Drones in Agriculture – Prospects and Limitations

Georg Bareth

Juliane Bendig, Andreas Bolten, Helge Aasen, and many more …

Geography, GIS & RS Group, University of Cologne, Germany

Introduction - UAVs - Sensors - Analysis - Application - Limitations - Conclusions

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Airborne RS: • data acquisition is expensive • multi-temporal image acquisition is expensive • flight / weather restrictions • geo-rectification and -referencing

Optical (Satellite) RS: • optical data is weather dependent • availability of multi-temporal images is poor • repetition frequency: phenology • costs can play a role: priority Microwave (Satellite) RS: • resolution of microwave RS • wavelength (X-, C-, L-Band) • repetition frequency • costs; expertise

Limitations of Remote Sensing (RS)

WV2

DOP and DEM1L

But …

TerraSar-X

Introduction - UAVs - Sensors - Analysis - Applications - Limitations - Conclusions

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Motivation

Objectives - Feasibility of new/existing technologies:

terrestrial laserscanning (TLS) (3D)

hyperspectral libraries (hyperspectral)

unmanned aerial vehicles (UAVs) (3D + hyperspec.)

- New and combined analysis methods: multitemporal crop surface models (CSM)

software development (HyperCorr)

- 3D-Data: plant height, plant growth, emergence, biomass

- Hyperspectral: chlorophyll, nitrogen, stress, biomass

- 3D + Hyperspec.: vitality, abiotic stress, biotic stress, biomass, nitrogen


4 / 27 Introduction - UAVs - Sensors - Analysis - Applications - Limitations - Conclusions

Unmanned Aerial Vehicles (UAVs) … or UAS, Drones … . Google-Search: Drones Google-Search: Micro-UAVs

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UAV-technological Drivers for Aplications? - Science - Military

- Game Industry

- Postal/Parcel Services


Google-Search: Quadrocopter toy

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Example: Low-cost Micro-UAV The Hubsan X4 (H107L):

The Hyundai Fingercam

- Motor (x4): Coreless Motor - Frequency: 2.4GHz - with 4 channels - Flight time: above 9 minutes - Charging time:30 minutes - Flying outdoor - 37 g - approx. € 45,- (- plus 8 g camera)

- 1,3 Megapixel CMOS-Sensor - Video: 720 x 480, 30 fps - 2 h recording time - MicroSD-Karten up to 16 GB - USB 2.0 - 28 g - approx. € 25,-


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UAV Imaging Sensors (< 2 kg)

www.tetracam.com www.rikola.fi

www.imsar.com

www.flir.com

Multispectral RGB Thermal

Microwave LIDAR

Hyperspec


www.canon.com

www.lavionjaune.com

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• Cubert GmbH UHD 185 – Firefly − imaging spectrometer (up to 15 frames/s) − hyperspectral videos − uncooled Si-CCD detector − 450 nm – 950 nm (res.: ca. 8 nm) − sampling interval: 4 nm − 137 bands − resolution: 1 Megapixel − weight: approx. 1 kg (incl. battery, computer) − UAV-optimized

Hyper- and Multi-Spectral Imaging http://www.cubert-gmbh.de

• Panasonic Lumix DMC GX1 – weight: 400 g – resolution: 4592 x 3448 (16 million) pixel – FOV: image size 90 m x 60 m (100 m, 1.8 cm res.) – Lumix G 20mm / F1.7 ASPH Lens – mechanical/electronical trigger – 1920 x 1080 Full-HD http://www.panasonic.de


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Technical Data: − rotors: 4 - 12 − payload: 250 g - 2500 g − weight: 650 g - 1700 g − flying time: 15 - 40 min − distance: sight distance − altitude: 350m − sensors: gyroscope,

acceleration, compass, GPS, barometric altitude

MikroKopter: MK-Oktokopter

• UAV platform (< 5 kg): – stable – lightweight – low-cost – self-manageable / reparable – fast and significant development


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UAV-Campaigns 2013: Central Experiment (Barley)

10 m

RGB – stereo – 100 m (2012)


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TLS- and RTK-Instruments for Groundtruth Multi-temporal approach Devices:

− Riegl LMS Z420i − Nikon D200 − Topcon HiPer Pro − Self-developed reflectors on ranging poles

Riegl LMS Z420i − Range: 2m – 1000m − Accuracy: 1cm − Points per second: 11,000

Topcon HiPer Pro: − DGPS with own base station − Uses carrier phase : ~ 1cm

DGPS Receiver

Digital camera

Laser scanner

Tripod

base station reflector


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ASD Fieldspec-3 • Field spectrometer • 350 nm – 2500 nm (res.: ca. 3/10 nm) • approx. 1300 bands • weight: 5.2 kg (excl. battery, computer)

ASD Fieldspec-3 (for Groundtruth) (Kang et al. 2012)


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Crop Surface Model (CSM)

Plant Growthtotal = t3 – t0 Plant Growthin-season1 = t1 – t0 Plant Growthin-season2 = t2 – t0

Plant Growthin-season3 = t2 – t1 Plant Growthin-season4 = t3 – t2 Plant Growthin-season5 = t3 – t1

BENDIG et al. 2013, HOFFMEISTER et al. 2010 (DOI: 10.1117/12.872315), Tilly et al. (2014, in revision)


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UAV-based DEM (30.04.2013) and CSM (13.06.2013)

Introduction - Methods - First Results - Conclusions & Outlook

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Hyperspectral CSM (13.06.2013) R = 802 nm; G = 550; B = 462 nm UAV: 30 m; resolution: 1,3 cm


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FieldSpec-3 Zonal Statistics

Cubert Firefly vs. ASD FieldSpec-3: Comparison


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Cubert Firefly: Zonal Statisctics

variability enlarges

λ in nm

refle

ctan

ce


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Cubert Firefly vs. ASD FieldSpec-3

λ in nm

refle

ctan

ce


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Firefly VIs vs. FieldSpec-3 VIs

Firefly Fieldspec-3 Plot 41 Plot 42 Plot 43 Plot 41 Plot 42 Plot 43

Plant height

NDVI(800; 670) 0,92 0,93 0,92 0,94 0,95 0,94

GI(554; 678) 2,74 2,23 2,37 3,87 4,01 3,94

RVI(800; 670) 23,2 28,0 24,0 29,9 42,9 33,2

OSAVI(800; 670) 0,87 0,86 0,85 0,88 0,87 0,88

TVI(750; 670; 550) 35,2 31,5 32,7 36,9 30,2 34,90

RRE(670; 782) 0,35 0,31 0,32 0,35 0,29 0,33

REP(742; 702) 722 721 721 723 725 723

NDVI(800; 670) GI(554; 678)

GI OSAVI NDVI



Quantalab (Source: Pablo Zarco-Tejada (IAS-CSIC), http://quantalab.ias.csic.es)

http://www.nottingham.ac.uk/eotechcluster/documents/events/uav-sig/zarcorspsocannuallecture.pdf

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Quantalab (Source: Pablo Zarco-Tejada (IAS-CSIC), http://quantalab.ias.csic.es)

http://www.nottingham.ac.uk/eotechcluster/documents/events/uav-sig/zarcorspsocannuallecture.pdf


www.gizmag.com/uav-crop-dusting

www.swiss-drones.com

Feimut Stephan

Applications: Status Quo • UAV-based imaging: N, biomass, stress … • Crop dusting • Up to 40 % of paddy rice in Japan is

co-managed with RC-Yamaha-Heli • UAVs in vineyards • No PreAg with UAVs • UAV-based herd management

http://youtu.be/kmymGlp1nmY


Current Limitations of UAV-application in Agriculture • weather dependent (wind, rain etc.) • time lack from data acquisition to map product • 300 – 1000 ha per campaign difficult but possible (Quantalab) • GPS accuracy for PreAg (but 1 cm prototypes are flying) • regulations by law:

- Germany: * < 5 kg easy to organize (< 300 m altitude) * < 25 kg possible but more paper work * > 25 kg I would say impossible

- USA: Impossible due to the U.S. Department of Transportation’s Federal Aviation Administration (FAA) regulations. Commercial flying ban announced in 2007 by FAA. This might change in 2015. - China: * < 25 kg seems to be no problem * > 25 kg possible * informal information: commercial flying possible

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Conclusions Pros • similar sensors which are in ground-based application (N-sensor …) • UAV techniques work well • highly flexible in space and time - multi-temporal image acquisition • all kind of crop/plant sensing is possible • plant height differences can be detected (< 3 cm) • hyperspectral CSM / 3D-point clouds possible • single plants monitoring is possible (orchards, sugar beet …) • Phenotyping

Cons • law restrictions • reliability • cost/benefit


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The AT Black Knight Transformer www.advancedtacticsinc.com The Volocopter

Source: http://www.e-volo.com

Gyrocopter for crop spraying www.autogyro-professional.com

Outlook next 3-5 years • Sensors, UAV-platforms, and analysis are available and in applications (niches) • Development of automated analysis:

easy to use systems for real-time decision support / management

• UAV-applications in agriculture in an experimental stage (2015?)

• More „real“ applications with „Manned Aerial Vehicles“


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Outlook next 5-10 years • Fully-(/Semi-)automated UAV-platforms in practise for sensing and management • Technological boost from robotics (also field robots) and car industry • UAV-swarms for multiple tasks • UAVs are additional/supplementary „robots“

to ground-based robots and traditional PreAg / RS

http://www.bostondynamics.com

www.VerticalFarm.com


27 / 27 Plantekongres 2014, 14.-15. Januar 2014, Herning, Denmark

Use of Drones in Agriculture – Prospects and Limitations

Georg Bareth

Juliane Bendig, Andreas Bolten, Helge Aasen, and many more …

Geography, GIS & RS Group, University of Cologne, Germany

Introduction - UAVs - Sensors – Analysis - Application - Limitations - Conclusions

28 / 27

“… optimal nitrogen fertilizer application regimes in crop production

have two requirements: (1) knowledge of the adequate N content for a

given amount of biomass and (2) the development of fast, accurate

methods to determine the actual N content and biomass (or N

demand) of the crop plant ... .” Bodo Mistele and Urs Schmidhalter (2008): Estimating the nitrogen nutrition index using spectral canopy reflectance measurements. Europ. J. Agronomy 29/4: 184-190. DOI: 10.1016/j.eja.2008.05.007 NNI = Nact / Nc

Nact = actual measured N content as a percent of the dry matter of the canopy biomass

Nc = the critical N content for the crops of each plot given their amount

of dry weight

Nitrogen Nutrition Index - NNI

Discussion

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www.icasd.org www.cropsense.de

Related Project Activities

www.tr32.de

Discussion

use of drones in agriculture – prospects and limitations ... · plantekongres 2014, 14.-15....

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