weed detection in rice crop using computer...

Centre of Robotics

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/

Centre of Robotics

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

Centre of Robotics

Road Map

◦ Introduction

◦ Problem Statement

◦ Methodology

◦ Experimentation & Results

◦ Comparison

◦ Conclusion

◦ Future Work

Centre of Robotics

INTRODUCTION

Centre of Robotics

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

Centre of Robotics

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

Centre of Robotics

Issues with Weed Control Methods

◦Difficult to harvest

◦Disadvantage of uniform spraying▫Uneconomical

▫Affects crop health

▫Environmental Pollution

▫Resistance to sprays

Centre of Robotics

Resistance to sprays

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

September 13th, 2015.

Centre of Robotics

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.

Centre of Robotics

Problem Statement

Centre of Robotics

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.”

Centre of Robotics

Problem Statement

“Automated localized weed detection in

rice fields to

that will result in

high, good quality yield with low

production cost.”

Centre of Robotics

Problem Statement

“

in rice fields to

that will

result in high, good quality yield with low

production cost.”

Centre of Robotics

Centre of Robotics

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.

Centre of Robotics

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.

Centre of Robotics

Experimental setup & Dataset

Centre of Robotics

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

Centre of Robotics

Technique 1

Using Wavelet Transform involving Blur Detection

Centre of Robotics

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

Centre of Robotics

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

Centre of Robotics

Weed Detection

Input image

Excessive green image

Wavelet Transform

Thresholding on Diagonal Coefficients

Inverse Wavelet Transform

Dilation

Remove small regions

Output image

Centre of Robotics

Steps 1-3

Original Image Excessive Green Image

Diagonal Coefficient Diagonal Coefficient(Filtered)

Centre of Robotics

Steps 4-5

Dilation

Centre of Robotics

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%

Centre of Robotics

Weakness

Accuracy drops drastically when texture

difference decreases with the growth of

grass

Centre of Robotics

Technique 2

Using SVM and Random forest with Moments

Centre of Robotics

Dataset-2 Density Based

Centre of Robotics

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

Centre of Robotics

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

Centre of Robotics

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

Centre of Robotics

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

Centre of Robotics

Comparisons

Accuracy and Execution Time

Centre of Robotics

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

Centre of Robotics

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

Centre of Robotics

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

Centre of Robotics

Topic

Plants Classification using Hough Line

Transform & Support Vector Machine(SVM)

Researcher: Umar Muzaffar52

Centre of Robotics

Tools & Technology

◦Visual Studio

◦Image Processing( Opencv, C++)

53

Centre of Robotics

Data set Collection

◦All dataset collected from:

University of Central Punjab

Fields

Nurseries

54

Centre of Robotics

Sample Images

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

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

55

Centre of Robotics

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

56

Centre of Robotics

Techniques Used

◦Hough Line Transform (To extract

different shapes)

◦SVM (Support Vector Machine)

57

Centre of Robotics

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

Centre of Robotics

ResultsCherry’s Plant

59

Centre of Robotics

Continue.

Cauliflower’s Plant

60

Centre of Robotics

Continue.

RedChilli’s Plant

61

Centre of Robotics

Continue.

Potato’s Plant

62

Centre of Robotics

Continue.

It’s Wall Palm Tree

63

Centre of Robotics

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.

64

Centre of Robotics

Disease Identification in Crops

Researcher: Sibtain Abbas

Centre of Robotics

Goals

◦Increase in production.

◦Quality crops.

◦Reduce economic damage.

Centre of Robotics

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

Centre of Robotics

Common Diseases

◦Fusarium

◦Leaf Rust

◦Leaf Blotch

◦Wilt

◦Chlorosis

◦Scorch

Centre of Robotics

Fusarium Blight

◦Fungal Disease

◦Causes

◦Effect on US Economy

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

Centre of Robotics

Leaf Scorch

◦Browning of Leaf Tissues, Veins and Tips.

◦Causes

◦Effect

Centre of Robotics

Basic Steps

Centre of Robotics

Flow Chart

Centre of Robotics

Histogram- Methodology

Blurring the image.

Blurred Image

Centre of Robotics

Histogram

◦HSV is used to improve color space

accuracy.

Centre of Robotics

Histogram

◦Canny Edge Detection is used to further

enhance the details.

Centre of Robotics

Histogram

◦Healthy and Diseased Histograms.

Centre of Robotics

Histogram- Results

Centre of RoboticsMulti Class SVM

◦Converting RGB to Gray Scale

◦Image Pre Processing

◦Image Segmentation

◦Feature Extraction

◦Classification

◦Testing

Centre of Robotics

Multi Class SVM- Results

Centre of Robotics

Multi Class SVM- Results

Centre of Robotics

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

Centre of Robotics

Accuracy

◦Maximum accuracy achieved after 500

iterations.

Centre of Robotics

Future Work

◦Improve the Accuracy.

◦Parallel detection of Weeds and Diseases.

Centre of Robotics

Thank You!