An Aerial Remote Sensing Platform for High Throughput Phenotyping of Genetic Resources

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An Aerial Remote Sensing Platform for High Throughput Phenotyping of Genetic Resources Observing with “artificial eyes” Maize Physiology, (Zimbabwe) Wheat Physiology, (Mexico) CIMMYT

description

Remote sensing –Beyond images Mexico 14-15 December 2013 The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

Transcript of An Aerial Remote Sensing Platform for High Throughput Phenotyping of Genetic Resources

Page 1: An Aerial Remote Sensing Platform for High Throughput Phenotyping of Genetic Resources

An Aerial Remote Sensing Platform for High Throughput Phenotyping of Genetic Resources

Observing with “artificial eyes”

Maize Physiology, (Zimbabwe)Wheat Physiology, (Mexico)CIMMYT

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Remote sensing for field phenotyping at CIMMYT Why do we need remote sensing?• Non-intrusive measurements• Coverage of large areas• Accessibly• Reduce error variance Sensors• Ground-based: e.g. Thermal, Hyperspectral, Ground Penetrating Radar• Airborne: Thermal, multispectral• Space-borne: Multispectral Remote Sensing for field phenotyping CIMMYT1. Phenotyping: Drought, irrigation, low nitrogen2. Field variability:3. Stress Evaluation

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Ground-based Remote Sensing for Field Phenotyping

Spectroradiometer IR Thermometer GreenseekerCossani et al, 2013

Weber et al. 2012; Zia et al. 2012; Cairns et al. 2012

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Problems with hand-held measurements

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Weber et al. 2012; Cairns et al. 2013; Prasanna et al. 2013;; Araus and Cairns, 2014

Reducing field variability

Increasing the “signal to noise” ratio should increase breeding efficiency

Current methods to characterise field variability are too slow to be incorporated into the breeding pipeline

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Airborne Remote Sensing Platform for High Throughput Phenotyping

• AB1100 helium filled blimp.• Tethered.• L8.0m, W3.1m.• Payload approx 6 kg• Max flight height 300 m.• Max wind speed 13 m/s.

• Astec Falcon 8, 8-rotar UAV.• Remote controlled.• 650 g payload.• Max flight height approx 130 m.• Max wind speed 10 m/s.

• Airelectronics Skywalker• Autonomous flying

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Platoform Instrument SpecificationsHelium Blimp Tetracam mini MCA 12

Channel Imaging Spectrometer

• Resolution: 1280 x 1024 (1.3 megepixels)

• Spectral Range: 12 channels between 445-980 nm

ASTEC Falcon/Skywalker Tetracam ACD Light Multispectral Camera

• Resolution: 2048 x 1536 (3.1 megapixels)

• Spectral Range: 3 bands in Green, Red and NIR

ASTEC Falcon FLIR Tau 640 LWIR Uncooled Thermal Imaging Camera

• Resolution: 640 x 512 (0.3 megapixels)

• Spectral Range: 7.5-13 μm

ASTEC Falcon SONY NEX-5N Digital Camera

• Resolution: 4588 x 3056 (14 megapixels)

Skywalker Miricle 307 KS Thermal Camera

• 640x480 detector resolution: (0.3 megapixels) and 25μm pitch KS

Airborne Remote Sensing Platform

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Physiological parameter

Indices (from skywalker)

Plant water use Canopy temperature

Canopy conductance Canopy temperature

Plant growth (biomass)

NDVI

Nutrient deficiency Senescence/color

Airborne Remote Sensing Platform

Collaboration with Crop Breeding Institute (Zimbabwe), University of Barcelona and CSIC

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Field variability assessment:• Priority setting of trials• Incorporating variation into field design

Airborne Remote Sensing Platform

Crop status:• NDVI → green biomass → crop senescence• Reflectance → water content → chlorophyll content

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Airborne VS Ground

0.50 0.60 0.70 0.800.50

0.60

0.70

0.80

f(x) = 0.771202441869451 x + 0.131097474414504R² = 0.712717074376984

MSAVI BLIMP VS NDVI Ground Drought_1

MSAVI BLIMP

ND

VI G

RO

UN

D

0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.450.50

0.60

0.70

0.80

f(x) = − 0.74183172942488 x + 0.863742285002525R² = 0.736201871997282

NCPI BLIMP VS NDVI GroundDrought_1

NCPI BLIMP

ND

VI G

RO

UN

D

0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34 0.360.60

0.65

0.70

0.75

f(x) = 0.811406521069647 x + 0.448749431559209R² = 0.835179966486121

NDVI BLIMP VS NDVI GroundIrrigation_1

NDVI BLIMP

ND

VI G

RO

UN

D

0.56 0.58 0.60 0.62 0.64 0.66 0.680.60

0.65

0.70

0.75

f(x) = 1.19573788140151 x − 0.0684435444157311R² = 0.828075934075804

PSND BLIMP VS NDVI GroundIrrigation_1

PSND BLIMP

ND

VI G

RO

UN

D

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Airborne VS Yield, BiomassTrial NDVI Ground Yield (g/m2) Biomass (g/m2)CIMCOG_H_1 NDVI UAV 0.85 0.77 0.79

NDVI GROUND 0.63 0.58CIMCOG_H_2 NDVI UAV 0.89 0.79 0.72

NDVI GROUND 0.74 0.64SEED_SEL NDVI UAV 0.82 0.67 -

NDVI GROUND 0.43 -DIVERSITY SET NDVI UAV 0.71 0.64 0.76

NDVI GROUND 0.63 0.66FIGS NDVI UAV 0.90 0.60 0.69

NDVI GROUND 0.58 0.66

Trial CT Ground (oC) Yield (g/m2) Biomass (g/m2)Diversity_Set Thermal Index (UAV) 0.59 -0.57 -0.64

CT Ground (oC) -0.56 -0.60CIMCOG_H_1 Thermal Index (UAV)

0.76 -0.73 -0.78CT Ground (oC) -0.55 -0.61

CIMCOG_H_2 Thermal Index (UAV)0.73 -0.74 -0.78

CT Ground (oC) -0.62 -0.67

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8 band multispectral imagery + panchromatic image.770 km altitude.46 cm panchromatic (single band) spatial resolution + 1.85 cm multispectral spatial resolution

Pan-sharpened multispectral WV-2 satellite image.

Multispectral UAV image

8.5 x 2.4 m plots

Worldview-2 Satellite Imagery

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Worldview-2 Satellite Imagery

Although at high spatial resolution, still too coarse to be applied to small plots (approx 2m x 0.8 m)

Pansharpened multispectral image (46 cm spatial resolution) applied to large-plot trial (8.5 x 2.4 m) to derive NDVI.

UAV Multispectral Camera Satellite Imagery

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Airborne remote sensing can be used for non-destructive screening of plant physiological properties over large areas.

Enough resolution to obtain information at plot level while being able to measure several hundred plots with one take.

Potential to be used as selection tool for breeders (e.g. CT, spectral indices); avoiding time and costs involved with harvesting to increasing breeding efficiency.

Conclusion

Yield distribution of 3 years mean drought trials (Cd Obregon, Mexico)

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% of check

%

Conventionalcrosses

Physiologicaltrait crosses

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THANKS