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Weed Detection in Crops Using Computer VisionPresenter: Dr. Yasir Niaz KhanResearchers: Taskeen Ashraf, Danish Gondal, Novaira Noor.http://cs.ucp.edu.pk/index.php/robotics-security/

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UCP Robotics Group

◦Faculty

◦Dr. Yasir Niaz Khan

◦Dr. Syed Atif Mehdi

◦Dr. Musharraf Hanif

◦Dr. Oumeir Naseer

◦Muhammad Awais

◦Researchers

◦Aamir Ishaq

◦Sibtain Abbas

◦Ruhan Asghar

◦Hamad ul Qudous

◦Noman Saleem

◦More than 50

undergrad students

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Road Map

◦ Introduction

◦ Problem Statement

◦ Methodology

◦ Experimentation & Results

◦ Comparison

◦ Conclusion

◦ Future Work

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INTRODUCTION

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Importance of Rice Crop1,2

◦ Feeds over 50% of World’s population

◦ Pakistan 13th world wide in Rice production

◦ Pakistan 4th in Rice Exports

◦ Stands second in terms of staple food in Pakistan

◦ 13% to the total value of Exports

◦ Stands third in terms of cultivation area3

1. Old.parc.gov.pk, "NARC-Rice||Introduction", 2015. [Online]. Available:

http://old.parc.gov.pk/NARC/RiceProg/Pages/intro.html. [Accessed: 20- Dec- 2015].

2. Bayercropscience.com.pk,. 'Bayer Cropscience - Pakistan : Rice'. N.p., 2015. Web. 14 May 2015.

3. Fao.org,. 'Fertilizer Use By Crop In Pakistan'. N.p., 2015. Web. 18 June 2015

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What are Weeds and Weed Control?1

Weeding Approaches

Manual

Hand weeding

Hand hoeing

Partially automated

Herbicides application

Biological means

1. Pakissan.com,. 'Integrated Weed Management In Rice :: Pakistan Agricultural News Chennal-:PAKISSAN.Com:-

'. N.p., 2015. Web. 17 May 2015.

2. Eap.mcgill.ca,. "Biological Control Of Weeds". N.p., 2015. Web. 31 sep. 2015.

Biological Means2

1. biological spray

-spore

suspension of

an endemic

fungus

2. a fish, the

white amur

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Issues with Weed Control Methods

◦Difficult to harvest

◦Disadvantage of uniform spraying▫Uneconomical

▫Affects crop health

▫Environmental Pollution

▫Resistance to sprays

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Resistance to sprays

Survey website at http://www.weedscience.org on

September 13th, 2015.

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Weeds are a

Problemo Weed destroys 15-20% or in some

cases up to 50% of the crop1

o Uniform spraying is uneconomical

o Control Period of weeds is first 40-50

days

1. Pakissan.com,. 'Integrated Weed Management In Rice :: Pakistan Agricultural News

Chennal-:PAKISSAN.Com:-'. N.p., 2015. Web. 17 May 2015.

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Problem Statement

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Problem Statement

“Automated localized weed detection in

rice fields to avoid excessive uniform

spraying; that will result in high, good

quality yield with low production cost.”

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Problem Statement

“Automated localized weed detection in

rice fields to

that will result in

high, good quality yield with low

production cost.”

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Problem Statement

“

in rice fields to

that will

result in high, good quality yield with low

production cost.”

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Initial Experimentation

◦Testing conducted with few images

(DS-1)(1,2)

◦Broadleaf and sedges

1. Jircas.affrc.go.jp,. 'JIRCAS cyperus Difformis plants In Lowland Savanna Of West Africa'. N.p., 2015.

Web. 10 May 2015.

2. Mikobi.deviantart.com,. 'Water Lily In The Rice Paddies Around Angkor Wat'. N.p., 2015. Web. 10 May

2015.

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Initial Experimentation

◦Three techniques▫Based on localized FFT and Edge Detection1

▫Based on localized Entropy

▫Based on Wavelet Transform2

0

10

20

30

40

50

60

70

80

90

100

Localized FFT LocalizedEntropy

Discrete WaveletTransform

Comparison Using Accuracy and FPR

Accuracy

FPR

Accuracy:

89.60 %

Techniques Accuracy FPR

FFT 74.85% 27.83%

Entropy 76.66% 24.09%

Wavelet 89.60% 17.50%

1. Nejati, Hossein, Zohreh Azimifar, and Mohsen Zamani. "Using fast fourier transform for weed detection in corn fields."

Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on. IEEE, 2008

2. Noor, Novaira, and Yasir Niaz Khan. 'Weed Detection In Wheat Fields Using Computer Vision'. Graduate. FAST-NU

Lahore, 2014. Print.

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Experimental setup & Dataset

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Experimental Setup

◦Setup▫MATLAB 2014 64 bit

▫Windows 8 64 bit

▫4 GB RAM

▫Core i5 1.70GHz Processor

▫LibSVM and RF

◦Dataset

▫Images taken height of 2-4 ft.

▫Angle of capture is 90 degrees

▫Image resolution is 1920x1080

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Technique 1

Using Wavelet Transform involving Blur Detection

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Overall Technique

Video

Extract Every Nth Frame(Image)

Blur Detection Module

Weed Detection Module

Output image

Calculate Weed Coverage

Trained

SVM

Model

Blur

Non-Blur

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Blur Detection

Dataset blur/Non-Blur labelled images

Get image one by one

Convert RGB to Gray

Calculate Discrete Laplacian

Extract Features

Train SVM (Batch Training)

Linear SVM Model

Min, max, std

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Weed Detection

Input image

Excessive green image

Wavelet Transform

Thresholding on Diagonal Coefficients

Inverse Wavelet Transform

Dilation

Remove small regions

Output image

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Steps 1-3

Original Image Excessive Green Image

Diagonal Coefficient Diagonal Coefficient(Filtered)

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Steps 4-5

Dilation

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Accuracy & FPR

◦ Total Frames = 1717

◦ Total Frames processed = 172

◦ Non-blur frames detected = 67

◦ Accuracy of blur detection = 84.88%

◦ FPR of blur detection = 18.46%

◦ Weed Detection Accuracy = 68.95%

◦ FPR = 12.69%

◦ Weed Detection Accuracy after blur removal =

76.16% (8% increase)

◦ FPR = 13.38%

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Weakness

Accuracy drops drastically when texture

difference decreases with the growth of

grass

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Technique 2

Using SVM and Random forest with Moments

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Dataset-2 Density Based

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Weed Detection

Density Based Dataset

Extract Green channel from RGB

Calculate Mean,variance,kurtosis,skew

Train Classifier (Batch Training)

Calculate n-fold cross validation

Calculate complex

moments

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Accuracy Using First Four Moments

68.00

70.00

72.00

74.00

76.00

78.00

80.00

82.00

84.00

86.00

88.00

1 2 3 4 5

Accu

rac

y

No. of Iterations

Linear kernel

RBF kernel

Random Forest

Accuracy: 82.22% RBF

Kernel SVM C=8, g=0.25

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Accuracy Using Complex Moments

Accuracy: 81.42% random

forests with 300 trees

66.00

68.00

70.00

72.00

74.00

76.00

78.00

80.00

82.00

84.00

86.00

1 2 3 4 5

Accu

rac

y

No. of Iterations

Linear kernel

RBF kernel

Random Forest

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Accuracy Using Combined Moments

Accuracy: 86.06% RF

With 300 trees

70

72

74

76

78

80

82

84

86

88

90

1 2 3 4 5

Acc

ura

cy

No. of Iterations

Linear kernel

RBF kernel

Random Forest

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Comparisons

Accuracy and Execution Time

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Accuracy

0

10

20

30

40

50

60

70

80

90

100

Linear kernel RBF kernel Random Forest

Accu

rac

y

Type of classifiers

Moments Feature set

GLCM feature set

Accuracy: 86.06% RF

With 300 trees

Moments Feature Set

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Execution Time

0

5

10

15

20

25

30

35

Wavelet Transform withblur detection

Moments GLCM features

Execu

tio

n t

ime i

n s

eco

nd

s

Linear SVM kernel

RBF SVM kernel

Random forests

Wavelet Transform

Less feature extraction Time

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Conclusion

◦ Strengths

• Different densities of grasses

• Multiple backgrounds (dry soil, muddy

soil, straw/stalk)

• Grasses are a common weed in other

crops such as cotton.

◦ Limitations

• First technique dependents on growth

stage

• Threshold of dilation, area removal

needs to determined.

• Limited to a single type of weed

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Topic

Plants Classification using Hough Line

Transform & Support Vector Machine(SVM)

Researcher: Umar Muzaffar52

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Tools & Technology

◦Visual Studio

◦Image Processing( Opencv, C++)

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Data set Collection

◦All dataset collected from:

University of Central Punjab

Fields

Nurseries

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Sample Images

Kangi Palm’s Plant Potato’s Plant Pea’s Plant

(Captured from UCP) (Captured from Fields) (Captured from Nursery)

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Training of data

◦There were total of 9 species which I

classified successfully

◦There were total of 300 images collected

◦Each specie consist of 33 images.

◦31 images were used for testing purpose

◦2 images were used for validation purpose

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Techniques Used

◦Hough Line Transform (To extract

different shapes)

◦SVM (Support Vector Machine)

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Flow Chart

58

Input Image

Apply Hough Line

Transform.

Apply Canny Edge

Detector

Apply Bilateral Filter

to reduce noise

Find different shapes

of leaves

Apply SVM for

classification

If image’s data

matches

Output plant’s name

Save Features

in file

If image’s data

doesn’t match

Output “It’s not match

to existing data”

Extract length & width

of leaves

Start

End

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ResultsCherry’s Plant

59

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Continue.

Cauliflower’s Plant

60

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Continue.

RedChilli’s Plant

61

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Continue.

Potato’s Plant

62

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Continue.

It’s Wall Palm Tree

63

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Future Work

◦To improve my system, I will use different

techniques Like Odd Gabor Filters and

morphological operations

◦It will help me to detect even veins of the

leaves

◦It will give much accurate results than, by

detecting the shapes of the leaves.

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Disease Identification in Crops

Researcher: Sibtain Abbas

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Goals

◦Increase in production.

◦Quality crops.

◦Reduce economic damage.

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Losses in Punjab

Crop Value of Damage

($ millions)

Cost of Control

($ millions)

Rice 1.77 0.61

Wheat 1.83 0.40

Cotton 2.23 1.7

Totals 5.83 2.71

http://www.fin

ance.gov.pk/survey/chapters_15/Annex_III_disease_damage.pdf

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Common Diseases

◦Fusarium

◦Leaf Rust

◦Leaf Blotch

◦Wilt

◦Chlorosis

◦Scorch

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Fusarium Blight

◦Fungal Disease

◦Causes

◦Effect on US Economy

http://www.ars.usda.gov/is/pr/2010/100401.htm

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Leaf Scorch

◦Browning of Leaf Tissues, Veins and Tips.

◦Causes

◦Effect

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Basic Steps

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Flow Chart

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Histogram- Methodology

Blurring the image.

Blurred Image

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Histogram

◦HSV is used to improve color space

accuracy.

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Histogram

◦Canny Edge Detection is used to further

enhance the details.

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Histogram

◦Healthy and Diseased Histograms.

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Histogram- Results

Centre of RoboticsMulti Class SVM

◦Converting RGB to Gray Scale

◦Image Pre Processing

◦Image Segmentation

◦Feature Extraction

◦Classification

◦Testing

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Multi Class SVM- Results

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Multi Class SVM- Results

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Multi Class SVM- Results

Stage No of Images Execution Time (sec)

Feature Extraction 100 90

Training 25/ per class 3.3

Testing 20/ per class 0.7

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Accuracy

◦Maximum accuracy achieved after 500

iterations.

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Future Work

◦Improve the Accuracy.

◦Parallel detection of Weeds and Diseases.

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Thank You!