National Research Institute of Science and Technology for ... · PDF fileNational Research...
Transcript of National Research Institute of Science and Technology for ... · PDF fileNational Research...
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www.irstea.fr
IrsteaNational Research Institute of Science and
Technology for Environment et Agriculture
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www.irstea.fr
▪A public research institute
▪9 centres
▪1,500 employees
Irstea
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www.irstea.fr
Irstea : from Research to Action
Scientific and technical
support of public policy
decision-making and
innovation
A multidisciplinary approach
combining human and social
sciences as well as biophysics
Improving knowlegde in
Water, Land Use and
Environmental
Technologies
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www.irstea.fr
Research organisation in
Irstea : 3 departments
- water
- environmental technologies
- land use
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www.irstea.fr
25 avril 2016
Irstea
Montpellier Centre
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Localization : north of Montpellier
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7Localization : in Agropolis campus
CIRAD
Agropolis
International
IRD
Irstea
AgroParisTech
MTD
IAM
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JRU G-EAU : Water resource management, actors and
uses
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JRU TETIS
Land, environment, remote sensing and spatial information
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JRU ITAP
Information – Technologies – environmental Analysis –
agricultural Processes
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Challenges for image conversionin management data:
the case of UAV
Sylvain Labbé, Gilles RabatelIRSTEA – Montpellier, France
« New technologies for precision agriculture » Agropolo Campinas 24-25 may 2017
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Part 1 UAV image acquisition for agricultural
monitoring: what technologies ?
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Summary
• 1. RGB cameras
• 2. Multispectral cameras
• 3. Hyperspectral cameras
• 4. Thermal cameras
• 5. Radiometric issues
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1. RGB cameras
The simplest, but already a lot of things to do with….
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RGB sensor technology
• The most used: Bayer matrix– Very cheap solution, huge application market
– Actual spatial resolution < number of pixels
• Foveon sensor– better spatial accuracy
– more complex colorimetry
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A necessary step: image mosaicing
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Structure from Motion (SfM)
Direct merging of images impossible (variations of UAV pitch and roll) 3D model mandatory to get orthorectified images
Basic principles
• Image overlapping (> 60%): each point must be seen several times
• Complex optimizing processing to recover camera positions
• Image georeferencing based on UAV embedded GPS (5-10 m accuracy) or ground control points (5-10 cm accuracy)
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SfM in practice• Open source or commercial software suites
(MicMac, VisualSfM, Photoscan, Pix4D…)
• Up to several hours to get an accurate mosaicing
• Digital Surface Model (DSM) as a by-product
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RGB camera for agriculture
• Sometimes used for soil-vegetation discrimination(but not that robust)
• Well-adapted to visual inspection of crop parcels(storm and animal damages, parcel contours and area…)
Excessive green index: Eg = 2G - R - B
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• Low cost, high accuracy 3D modelling
RGB camera for agriculture
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An alternative for 3D modelling: the LIDAR
• Able to detect both ground and vegetation above
• No need for mosaicing process
• Expansive !!! (~80 k€)
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2. Multispectral cameras
Towards more specific spectral information…
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Why get NIR spectral bands ?
• Compared to other natural materials, vegetation has the highest ratio betweenNIR and visible reflectance, especially between NIR and R Useful for robust vegetation discrimination or biomass evaluation
Visible NIR
Reflectance (%)
0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 µm
Water Bare soil
Vegetation
0
10
20
30
40
50
60
70
80
90
100
Ratio vegetation index : RVI = NIR/R [0, ∞ [
Normalized Difference Vegetation Index:NDVI = (NIR-R)/(NIR+R) = (RVI-1)/(RVI +1) [-1, 1]
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How to get NIR spectral bands ?
Basic issue:
A CDD sensor with a Bayer matrixincluding a NIR filter is technically feasible, but the
agricultural market is not large enough(should be x1000)
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Mono-lens solutions
• Industrial multi-CCD camera:
– requires embedded electronics for camera control and image storage
Removing the
NIR blocking filter
Setting a blue or red
band-pass filter
• Modified camera + linear combinationof channels (e.g. MAPIR cameras)
– Lose color information
– NDVI is not that accurate
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Multi-lens solutions
Removing the
NIR blocking filter
Setting a NIR
band-pass filter
R+G+B NIRR+G+B NIR
• Camera coupling:
• Commercial multi-lens devices– Supplementary bands for better crop
assessment and phenotyping
– Integrated GPS receiver
– SD card storage
Sequoia(www.parrot.com)
Airphen(www.hiphen-plant.com)
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Band registration issue
Due to inter-lens distance,band registration accuracy limited
to a few centimeters
Not adapted to high resolution imagery
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Band registration by Fourier-Mellin transform(Rabatel & Labbé, 2016)
• Developed for centimetric imagerywith coupled still cameras
• Registration based on local Fourier spectra analyses
Successfully applied alsoon Airphen multi-lens camera
~0.3 pixel accuracy
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3. Hyperspectral cameras
Still more spectral information…
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Hyperspectral imagery
• Each pixel contains hundredsof bands (instead of a few ones)
• Allows very accurate discriminations and assessments:
- Variety discrimination
- Chemical content assessment
- Disease detection
…
• Can be used to compute specific vegetationindexes (MCARI, SAVI, etc…) or to deploymultivariate analysis tools (PLS-R, SVM, etc.)
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Acquisition techniques
• Push-broom acquisition• Image is acquired line after line
• Requires a scanning motion and spatial reconstruction
• Sequential acquisition• Images in various bands are acquired successively,
using a tunable bandpass filter (LCTF or interferometer)
• Requires a perfect immobility
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Some in-field examples
Weed/crop discrimination(Irstea, 2012)
Leaf nitrogen content mapping(Irstea, 2014)
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UAV embedding
• Push-broom• Requires a high accuracy IMU for line acquisition
control and image reconstruction high quality but expansive solution (>100 keuros)
• Sequential acquisition• Requires band registration to compensate UAV instability Fourier-Mellin solution has been tested successfullymedium cost solution (~ 40 k€)
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5. Thermal cameras
Water stress assessment…
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Main characteristics
• Provides images in 8-12 µm spectral range, where material are light emitters according to their temperature.
• Low spatial resolution (usually 320x256 or 640x512)
• Temperature drift effects (especially with UAV uncooled cameras)
• ~ 3000-5000 Euros
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Plant water stress assessment
• Leaf temperature versus ambiant temperature gives an indication on water stress (stressed plants reduce their evapo-transpiration to savewater)
• Relationship between signal and temperature depends on camera calibration and drift requires temperature references in the field
• Usually coupled with NDVI measurements(to compensate for pixel mixing)
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5. Radiometric issues
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From luminance to reflectance
• Only the reflectance R() is valuable for crop assessment,but the camera provides L()
E() L() = E().R()
R()
L()R()
• A radiometric correction must be applied
• The more spectral information is expected (e.g. hyperspectral imagery),the more accurate the correction must be
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Radiometric correction techniques
• Solution 1: to measure incident light E() some cameras include an irradiance sensor(Sequoia multispectral camera, Rikola hyperspectral camera)
E() L() = E().R()
R()
L()R()
• Solution 2: to measure luminance Lref() = E() . Rref() on a reference groundtarget with known reflectance Rref()(and recover it in the final image)
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Canopy effects
Due to 3D canopy structure, leavesreceive a part of light from
surrounding leaves, modifying the apparent reflectance
• Some hyperspectral indices have been designed to take these effects into accountat low spatial resolution (e.g. Wu et al, 2008 for chlorophyll content estimation)
• No formal solution right now, but we do work hard on it !(PhD thesis Irstea, 2015-2017)
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To summarizeSensor type Usage Operation
difficultyPrice
Color (RGB) Visual observation3D modelling
+ +
Multi-spectral Biomass estimation (NDVI)
Robust vegetation/soildiscrimination
++ ++
Hyperspectral Variety discriminationLeaf chemical components
++++ ++++
Thermal Temperature measurement ++++ ++
Lidar 3D modelling ++ ++++
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Part 2UAV Applications to precision agriculture
• Various kinds of UAV
• Regulatory and economical issues
• Applications in precision agriculture
• Few applications in environmental field
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Drone or UAV (unmanned aerial vehicle) or RPAS (remotely piloted aircraft system)
Journée Optitec 2014
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The range of different drones according to US classifications
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Micro UAV Evolution
Choice of architecture(speed, yield, robustness).
- Propulsion (electric, thermal).
- Energy source- Battery- Hydrogen cell- Laser- Hybrid- Solar
Fixed wings Multirotor
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UAV Upcoming evolution
-New design-Convertible plane/helicopter-Aerial and submarine-Flapping wings
-
ONERA
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Regulatory and economical issues
• Coexistence with aerial traffic– In France over unpopulated areas:
• 150m (500ft) high and 200m horizontally from pilot
• 50m high and 1000m from pilot (with FPV)
• 25 kg max
• Pilot theorical and practical licenses
– Business solutions• service provider or owner of UAV and/or sensors
and/or data processing chain
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Remote sensing typical use of UAV
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How UAV can help with precision agriculture
Remote sensing
• Information on soil and field (heterogeneity)
• Crop monitoring: yields, damage assessment, weeds
• Optimized application (yields): fertilizer, pesticides
• Optimized application (balanced management of resources) : water, variety phenotyping
• Health assessment: early warning, targeted treatment
Other UAV payloads
• Crop spraying, crop planting, etc.
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Information on soil and field
• Palm tree plantation (Indonesia)
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Missing trees and heterogeneityInterpolation
NDVINDVI(Visible) Visible + parcelle
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Fertilization
Parrot drone and sensors – output : fertilization recommendation map
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Weed detection (RHEA project)
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Weed detection
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Weed detection (RHEA project)
Source: Institute for Sustainable Agriculture
(CSIC – Espagne)
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Hydric stress
• Without stressCrops transpire and temperature of leaves
is lower than air temperature
• With stressCrops temperature is higher than
Ambiant air temaperature
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Exemple of hydric stress on thermal image
(Irstea Montpellier)
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Thermal camera
UAV with visible, NIR and TIR
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UAV with visible, NIR and TIR
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Wheatplot
UAV and precision irrigation
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April
WDI
1
0
UAV and precision irrigation
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Irrigation
En cours
WDI
1
0
May
UAV and precision irrigation
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Visible image
Water stress study
High throughput Phenotyping
Exemple of a orchard with 120 apple varieties
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High throughput Phenotyping
Water stress indexes
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55 / nb total
VISIBLE PROCHE INRAROUGE
NDVI
High throughput PhenotypingSugarcane (Reunion island)
26°C
39°C
28°C
27°C
35°C
Temperature
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Few other examples with UAV
• Spraying
• Sample collection in water bodies
• Environmental impact assesment
• Lidar and forestry
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57
SPRAYING UAV
Yamaha Rmax
Aerial spraying of pesticides is currently forbidden in Europe
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NIR false color image – Guyane
500 m
Wetland monitoring(French Guyana)
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Classification result
Cartographie des zones humides en 2010
Wetland monitoring(French Guyana)
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ForestryLidar examples
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Forestry : wood volume estimation
[Bouvier, 2015]
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To summarizeApplication Usage Development
stageIssues
Crop monitoring Visual observationYield forecastDamage assesment
Operational
Precisionagriculture
Fertilization
Weed detection
Precision irrigation
Operational
Prototype stage
Feasability stage
Adapted groundmaterials
Phenotyping Variety yield
Variety resistance to stress
Operational
Research stage Sensors sensitivity
Health assesment Early alert and treatment Research stage Detection and riskmodelling
Other payloads Spraying Operational Regulation
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Thank you