weed detection for precision weed management

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WEED DETECTION FOR PRECISION WEED MANAGEMENT Kefyalew Girma SOIL/BAE 4213-2002 SOIL/BAE 4213-2002

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WEED DETECTION FOR PRECISION WEED MANAGEMENT. Kefyalew Girma. SOIL/BAE 4213-2002. 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. The BOTTOMLINE……. - PowerPoint PPT Presentation

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Page 1: WEED DETECTION FOR PRECISION  WEED MANAGEMENT

WEED DETECTION FOR PRECISION WEED

MANAGEMENT

WEED DETECTION FOR PRECISION WEED

MANAGEMENT

Kefyalew GirmaKefyalew Girma

SOIL/BAE 4213-2002SOIL/BAE 4213-2002

Page 2: WEED DETECTION FOR PRECISION  WEED MANAGEMENT

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

Page 3: WEED DETECTION FOR PRECISION  WEED MANAGEMENT

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

Page 4: WEED DETECTION FOR PRECISION  WEED MANAGEMENT

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

Page 5: WEED DETECTION FOR PRECISION  WEED MANAGEMENT

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.

Page 6: WEED DETECTION FOR PRECISION  WEED MANAGEMENT

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

Page 7: WEED DETECTION FOR PRECISION  WEED MANAGEMENT

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

Page 8: WEED DETECTION FOR PRECISION  WEED MANAGEMENT

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).

Page 9: WEED DETECTION FOR PRECISION  WEED MANAGEMENT

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)

Page 10: WEED DETECTION FOR PRECISION  WEED MANAGEMENT

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)

Page 11: WEED DETECTION FOR PRECISION  WEED MANAGEMENT

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)

Page 12: WEED DETECTION FOR PRECISION  WEED MANAGEMENT

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)

0

1000

2000

3000

4000

5000

Canada Thistle

Canda Thistle and Johnson Grass

Johnson Grass

0

1000

2000

3000

4000

5000

Canada Thistle

Canda Thistle and Johnson Grass

Johnson Grass

Upper Midwest Aerospace Consortium(UMAC)

Page 13: WEED DETECTION FOR PRECISION  WEED MANAGEMENT

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)

Page 14: WEED DETECTION FOR PRECISION  WEED MANAGEMENT

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

0

1000

2000

3000

4000

5000

Dig

ita

l N

um

be

r

Wavelength (nanometers)

28.5 Meter TM Multispectral

2 Meter AISA Hyperspectral

0

1000

2000

3000

4000

5000

Dig

ita

l N

um

be

r

Wavelength (nanometers)

28.5 Meter TM Multispectral

2 Meter AISA Hyperspectral

Page 15: WEED DETECTION FOR PRECISION  WEED MANAGEMENT

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

Page 16: WEED DETECTION FOR PRECISION  WEED MANAGEMENT

The Way ahead ….The Way ahead ….

Sensor

resolution

Algorithms

Unique

plant

features

Thresholds