WEED DETECTION FOR PRECISION WEED
MANAGEMENT
WEED DETECTION FOR PRECISION WEED
MANAGEMENT
Kefyalew GirmaKefyalew Girma
SOIL/BAE 4213-2002SOIL/BAE 4213-2002
Why ?Why ?
Uneven distribution Density can vary
widely within one field Conventional method
time-consuming and not proven cost-effective
Need for the on-the-go weed detection and treatment
Uneven distribution Density can vary
widely within one field Conventional method
time-consuming and not proven cost-effective
Need for the on-the-go weed detection and treatment
The BOTTOMLINE…….
The BOTTOMLINE…….
Site-specific weed control involves the use of correct treatment for the local weed populations which leads to: reduction in herbicide use on well-
kept fields maximize economic return to the
farmer
Site-specific weed control involves the use of correct treatment for the local weed populations which leads to: reduction in herbicide use on well-
kept fields maximize economic return to the
farmer
Where are the weeds?Where are the weeds?
The weed population must be automatically detected and evaluated across the field
This has led to the research on optical methods for weed detection
The weed population must be automatically detected and evaluated across the field
This has led to the research on optical methods for weed detection
The Principle behind automatic weed
detection
The Principle behind automatic weed
detection
Map/Sensor-based Plant species have a
different reflection in the visible and near-infrared (NIR) range
These differences can be used for automatic classification of crop and weed.
Map/Sensor-based Plant species have a
different reflection in the visible and near-infrared (NIR) range
These differences can be used for automatic classification of crop and weed.
The ChallengeThe Challenge
Refelectance of three plant species
00.20.40.60.8
1
400 450 500 550 600 650 700 750 800 850 900
wavelength (nm)
Ref
lect
ance
Cheat
Rye
Wheat
Refelectance of three plant species
00.20.40.60.8
1
400 450 500 550 600 650 700 750 800 850 900
wavelength (nm)
Ref
lect
ance
Cheat
Rye
Wheat
SunlightSunlight
Chlorophyll bChlorophyll b
-Carotene-Carotene
PhycocyaninPhycocyanin
Chlorophyll aChlorophyll a
300 400 500 600 700 800 300 400 500 600 700 800
Wavelength, nmWavelength, nm
Ab
sorp
tio
nA
bso
rpti
on
Lehninger, Nelson and Cox as presented in SOIL/BAE4213Lehninger, Nelson and Cox as presented in SOIL/BAE4213
Absorption of Visible Light by Photo- pigmentsAbsorption of Visible Light by Photo- pigments
Success StorySuccess Story
Statistical separability of weeds from soybean with Spectral Vision RDACSH3 hyperspectral sensor solid blue regions indicate separability (Sprague & Bunting, 2001).
Statistical separability of weeds from soybean with Spectral Vision RDACSH3 hyperspectral sensor solid blue regions indicate separability (Sprague & Bunting, 2001).
Success Stories…Success Stories…
Spectral data resulted in 91, 63, 63, 100, 46, and 33% correct classification of velvetleaf, redroot pigweed, broadleaf signalgrass, cotton, johnsongrass, and corn, respectively
Based upon airborne images populations generally at or above threshold densities could be correctly classified 2/3 of the time (Reynolds and Shaw, 2000)
Spectral data resulted in 91, 63, 63, 100, 46, and 33% correct classification of velvetleaf, redroot pigweed, broadleaf signalgrass, cotton, johnsongrass, and corn, respectively
Based upon airborne images populations generally at or above threshold densities could be correctly classified 2/3 of the time (Reynolds and Shaw, 2000)
Success Stories…Success Stories…
In field plant parts can be correctly classified as crop or weed in about 90 % of the cases, based on spectral information (Bennett and Pannell, 1998)
On maize, sugarbeet and 11 common weeds, Up to 94% of the reflection spectra of plants were classified correctly as crop or weed (Feyaerts et al. 1999)
In field plant parts can be correctly classified as crop or weed in about 90 % of the cases, based on spectral information (Bennett and Pannell, 1998)
On maize, sugarbeet and 11 common weeds, Up to 94% of the reflection spectra of plants were classified correctly as crop or weed (Feyaerts et al. 1999)
Success Stories…Success Stories…
In wheat and pea , among several species, large patches of wild oat and interrupted windgrass were detected (Lass and Donn Thill, 1998)
In wheat and pea , among several species, large patches of wild oat and interrupted windgrass were detected (Lass and Donn Thill, 1998)
Success StorySuccess Story Pure and Mixed Weed Species Spectral Signatures at 2
meter resolution using AISA (increased no. of bands) Upper Midwest Aerospace Consortium(UMAC)
Pure and Mixed Weed Species Spectral Signatures at 2 meter resolution using AISA (increased no. of bands)
Upper Midwest Aerospace Consortium(UMAC)
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1000
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Canada Thistle
Canda Thistle and Johnson Grass
Johnson Grass
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Canada Thistle
Canda Thistle and Johnson Grass
Johnson Grass
Upper Midwest Aerospace Consortium(UMAC)
Success StoriesSuccess Stories
“I have to admit I wouldn’t have been convinced to start a weed control program without having the images to show me just
how infested those particular pastures are.” Rancher in N. Dakota (UMAC,2002)
“I have to admit I wouldn’t have been convinced to start a weed control program without having the images to show me just
how infested those particular pastures are.” Rancher in N. Dakota (UMAC,2002)
Concerns ...Concerns ...
The limited spectral resolution of multi-spectral sensor is often compounded by their typically poor spatial resolution
The limited spectral resolution of multi-spectral sensor is often compounded by their typically poor spatial resolution
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1000
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5000
Dig
ita
l N
um
be
r
Wavelength (nanometers)
28.5 Meter TM Multispectral
2 Meter AISA Hyperspectral
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2000
3000
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5000
Dig
ita
l N
um
be
r
Wavelength (nanometers)
28.5 Meter TM Multispectral
2 Meter AISA Hyperspectral
Concerns...Concerns...
Canopy Structure similarity Effect of stress Lack of powerful algorithms Investment and benefit
Canopy Structure similarity Effect of stress Lack of powerful algorithms Investment and benefit
The Way ahead ….The Way ahead ….
Sensor
resolution
Algorithms
Unique
plant
features
Thresholds