remote sensing applications for phenotyping

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Remote Sensing applications for phenotyping.

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“Pheno …” - what?

• A phenotype is any observable characteristic or trait of an organism, such as its morphology, development, biochemical or physiological properties.

• Phenomics is the field of research studying the results from the expression of plant genes as well as the influence ofenvironmental incl. managed factors and the interactions between the genes and these factors.

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Mapping field gradients

Environmental data collection

Phenotyping

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Phenomics: Genotype x Environment x Management

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Phenotypic properties• Size, growth• Shape, architecture• Photosynthesis• Gas exchange• Color, pigments• Metabolite content• Yield

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Categories of trait evaluation

1. Proximal/remote sensing and imaging

2. Sample collection and their analysis in laboratory

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Proximal/remote sensing for phenotyping…

… is non-destructive and non-invasive

… is mostly based on the reflectance or transmission of light through the plant parts ( information on “colour”)

… can involve active components (like a light emitting source or a magnetic coil): RADAR, LIDAR/LASER, MRI, PET ( information on “travel time”)

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Full featured Phenotyping platform• Multiple sensors

???

Tools for proximal sensing (information collection near the crop)

EM38: Electromagnetic soil mapping(Measures conductivity to electrical current) • soil moisture content• salt levels• soil texture

~ 17,000 USD

Field Spectrometer• Vegetation/soil Reflectance• NDVI

15,000 – 50,000 USD

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Non-destructive plant/crop measurementPlant/Conopy Root Seed

Measurement• Multi/Hyperspectral

imaging• Chlorophyll fluorescence• Thermal imaging• Leaf area• Leaf angle• Stem/internode length• Plant/Canopy height• 3-D characteristics

Calculated variables• Light interception• Growth rate of leaves,

stems, plant or plants• Dry weight of leaves, stems,

flowers or plant• Shoot biomass

Estimated traits• Yield

Measurements• Root area/volume• Root length/depth• Thickness• Branching

Calculated variables• Growth rate• Root biomass

Estimated traits• Shoot/root• Compactness

Measurements• Number of panicles

Total number of seeds per plant

• Number of fertile seeds• Seed weight• Seed yield• Seed width, length and area

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Challenges in phenotype recognition

Traits develop and change…… over time (throughout the crop cycle)... in space (at the canopy level)

crops don’t capture resources (radiation, water and nutrients) uniformly throughout the season

Multiple measurements per season are highly recommended (Time series creation)

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Image based sensors (cameras)Can be categorized into three different classes:

1. Panchromatic/ single banded cameras2. RGB/CIR cameras3. Multi-Spectral cameras4. Hyperspectral cameras5. Narrow banded Cameras (like Chlorophyll

Fluorescence Sensors/MIPS)

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Camera sensors (1/3):RGB/CIR Cameras

Conventional digital cameras usually capture light from three different wavelengths: red, green and blue. For CIR cameras the blue filter is replaced with a NIR filter to enable the capture of wavelengths in the Near Infrared spectrum. Most RGB/CIR cameras use a bayer filter so the colour information for every 50% has to be interpolated.

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Camera sensors (2/3):Multispectral cameras

Multi-Spectral sensors capture up to nine different bands from the electro-magnetic spectrum (Typically 4-5). Some wavelengths are not captured. Each band covers approximately 50-400 nm of the EMS. They either use one camera per channel or incorporate a mechanism to redirect the light to one particular CCD array per spectral band (For example moving mirrors/lenses).

Top: Landsat 8 (OLI) Bottom: Landsat 7 (ETM+)

Color information for 8 pixels out of 64 = 87.5% of all pixels have to be interpolated!

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Camera sensors (3/3):Hyperspectral cameras

Hyperspectral cameras capture the reflected light in hundreds of different bands. Each band only covers a very small range of the EMS (approximately 3-5 nm). HS cameras provide collect images from nearly 200 different bands. The data is usually organized in “Hyperspectral cubes”.

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Phenotyping related limitations of spectroradiometric sensors

• Different plant parts have their particular absorption spectrum which vary as the plant develops.

• Leaf properties like roughness, trichomes, epicuticular waxes all have influence on the reflectance.

Measurements need to be standardized to ensure comparability between similar development stages. Some issues that should be taken into consideration: – soil covering (due to variations in canopy density) – Plant architecture (plant height, leaf size and angle, emerged spikes)

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Sensors and measurable properties (1/2)

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Sensors and measurable properties (2/2)Sensor Parameters +/-RGB color, shape, root architecture,

morphology, surface features, leaf disease assessments, …

+ Rapid measurements+ affordable solutions- Limited physiological information

Fluorescense Photosystem II, disease severity - only for rosettes- pre acclimation conditions‐

Thermal (IR) Surface temperature ( leaf and canopy transpiration)

- Rapid data collection- Sound bio physical interpretation required‐

Multi-/Hyperspectral

Water content, seed composition, Biomass, leaf and canopy water status, disease severity, pigment composition

+ Large amount of information- Costs

Stereo Camera Systems

Shoot structure, leaf angle distribution, canopy structure

+ High 3D accuracy- Complex data reconstructions

LIDAR Shoot biomass, leaf angle distribution, canopy structure

+ excellent 3D accuracy- Some instruments require specific illumination

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Comparison of different Phenotyping platforms

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Remote sensing applicationsA comparison

Satellite image acquisition UAV mapping

Repeatability Limited: (revisiting time of satellites: 1,2 days off-NADIR and ~16 days at NADIR)

Unlimited (But depends on weather conditions, number of batteries/chargers available)

Resolution not smaller than 30 cm/pixel ( “mixed Pixels”)

Depends on altitude of UAV. Typically 4-15 cm at 20-100 m altitude (depends on Camera)

Image size Unknown (accuracy/resolution significantly decreases to the edges). Estimated 8,000 x 8,000 pixels

Typically 1,280 x 960 pixels

Price ~ 30-60 USD per km2 for nine spectral bands at ~ 30 cm resolution (WorldView 3)

Labour costs only

Ready to use images

YES (But image enhancements may be performed as well)

NO (image alignment necessary, can be automated if image quality is sufficient).

Clouds YES (Especially during raining season) NO

• Rainy season = clouds• coarse resolution

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UAV platforms

• Balloons (“Blimps”)– Excellent payload (several kilograms)– Controlled through ropes that are pulled by people ( Limited manoeuvrability)

• Fixed wing UAV (planes)– Fast– Need runways/flat surfaces to land– Limited payload

• Paragliders– Slow (good for long exposures)– Limited manoeuvrability

• Helicopter– Can start/land vertically (no runway needed) and maneuvered into a stationary position– High payload– Vibration issues

• Multi-Rotor/Multicopter– Can start/land vertically (no runway needed) and maneuvered into a stationary position– Excellent payload (Up to 3 kg, depending on Multicopter size)

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UAV example (1/2)List of components:

• Frame• Motors• LiPo Battery• Power module• Electronic speed controllers• Flight computer (PixHawk)• Buzzer• Safety button• Telemetry• Compass, Magnetometer• Receiver

• Transmitter• Software• (FPV = First person view)

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UAV example (2/2)

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Putting it all together

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Thank you!