Weed Detection in Crops Using Computer Vision

Transcription

1 Weed Detection in Crops Using Computer Vision Presenter: Dr. Yasir Niaz Khan Researchers: Taskeen Ashraf, Danish Gondal, Novaira Noor.

2 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

3 Road Map Introduction Problem Statement Methodology Experimentation & Results Comparison Conclusion Future Work

4 INTRODUCTION

5 Importance of Rice Crop 1,2 Feeds over 50% of World s population Pakistan 13 th world wide in Rice production Pakistan 4 th 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 area 3 1. Old.parc.gov.pk, "NARC-Rice Introduction", [Online]. Available: [Accessed: 20- Dec- 2015]. 2. Bayercropscience.com.pk,. 'Bayer Cropscience - Pakistan : Rice'. N.p., Web. 14 May Fao.org,. 'Fertilizer Use By Crop In Pakistan'. N.p., Web. 18 June 2015

6 What are Weeds and Weed Control? 1 Manual Weeding Approaches Partially automated Biological Means 2 1. biological spray -spore suspension of an endemic fungus 2. a fish, the white amur Hand weeding Hand hoeing Herbicides application Biological means 1. Pakissan.com,. 'Integrated Weed Management In Rice :: Pakistan Agricultural News Chennal-:PAKISSAN.Com:- '. N.p., Web. 17 May Eap.mcgill.ca,. "Biological Control Of Weeds". N.p., Web. 31 sep

7 Issues with Weed Control Methods Difficult to harvest Disadvantage of uniform spraying Uneconomical Affects crop health Environmental Pollution Resistance to sprays

8 Resistance to sprays Survey website at on September 13 th, 2015.

9 Weeds are a Problem o Weed destroys 15-20% or in some cases up to 50% of the crop 1 o Uniform spraying is uneconomical o Control Period of weeds is first days 1. Pakissan.com,. 'Integrated Weed Management In Rice :: Pakistan Agricultural News Chennal-:PAKISSAN.Com:-'. N.p., Web. 17 May 2015.

10 Problem Statement

11 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.

12 Problem Statement Automated localized weed detection in rice fields to that will result in high, good quality yield with low production cost.

13 Problem Statement in rice fields to that will result in high, good quality yield with low production cost.

14

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

16 Initial Experimentation Three techniques Based on localized FFT and Edge Detection 1 Based on localized Entropy Based on Wavelet Transform 2 Accuracy: % Techniques Accuracy FPR FFT 74.85% 27.83% Entropy 76.66% 24.09% Wavelet 89.60% 17.50% Comparison Using Accuracy and FPR Localized FFT Localized Entropy 1. Nejati, Hossein, Zohreh Azimifar, and Mohsen Zamani. "Using fast fourier transform for weed detection in corn fields." Systems, Man and Cybernetics, SMC IEEE International Conference on. IEEE, Noor, Novaira, and Yasir Niaz Khan. 'Weed Detection In Wheat Fields Using Computer Vision'. Graduate. FAST-NU Lahore, Print. Discrete Wavelet Transform Accuracy FPR

17 Experimental setup & Dataset

18 Experimental Setup Setup MATLAB 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

19 Technique 1 Using Wavelet Transform involving Blur Detection

20 Overall Technique Video Extract Every Nth Frame(Image) Blur Blur Detection Module Trained SVM Model Non-Blur Weed Detection Module Output image Calculate Weed Coverage

21 Blur Detection Dataset blur/non-blur labelled images Get image one by one Convert RGB to Gray Calculate Discrete Laplacian Min, max, std Extract Features Train SVM (Batch Training) Linear SVM Model

22 Weed Detection Input image Excessive green image Wavelet Transform Thresholding on Diagonal Coefficients Inverse Wavelet Transform Dilation Remove small regions Output image

23 Steps 1-3 Original Image Excessive Green Image Diagonal Coefficient Diagonal Coefficient(Filtered)

24 Steps 4-5 Dilation

25 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%

26 Weakness Accuracy drops drastically when texture difference decreases with the growth of grass

27 Technique 2 Using SVM and Random forest with Moments

28 Dataset-2 Density Based

29 Weed Detection Density Based Dataset Extract Green channel from RGB Calculate Mean,variance,kurtosis,skew Calculate complex moments Train Classifier (Batch Training) Calculate n-fold cross validation

30 Accuracy Accuracy Using First Four Moments Linear kernel RBF kernel Random Forest No. of Iterations Accuracy: 82.22% RBF Kernel SVM C=8, g=0.25

31 Accuracy Accuracy Using Complex Moments Linear kernel RBF kernel Random Forest No. of Iterations Accuracy: 81.42% random forests with 300 trees

32 Accuracy Accuracy Using Combined Moments Linear kernel RBF kernel Random Forest No. of Iterations Accuracy: 86.06% RF With 300 trees

33 Comparisons Accuracy and Execution Time

34 Accuracy Accuracy Moments Feature set GLCM feature set Linear kernel RBF kernel Random Forest Type of classifiers Accuracy: 86.06% RF With 300 trees Moments Feature Set

35 Execution time in seconds Execution Time Linear SVM kernel RBF SVM kernel Random forests Wavelet Transform with blur detection Moments GLCM features Wavelet Transform Less feature extraction Time

36 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

37 Topic Plants Classification using Hough Line Transform & Support Vector Machine(SVM) Researcher: Umar Muzaffar 52

38 Tools & Technology Visual Studio Image Processing( Opencv, C++) 53

39 Data set Collection All dataset collected from: University of Central Punjab Fields Nurseries 54

40 Sample Images Kangi Palm s Plant Potato s Plant Pea s Plant (Captured from UCP) (Captured from Fields) (Captured from Nursery) 55

41 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

42 Techniques Used Hough Line Transform (To extract different shapes) SVM (Support Vector Machine) 57

43 Flow Chart Start Output plant s name Input Image If image s data matches Output It s not match to existing data End Apply Canny Edge Detector Apply SVM for classification If image s data doesn t match Apply Bilateral Filter to reduce noise Extract length & width of leaves Apply Hough Line Transform. Find different shapes of leaves Save Features in file 58

44 Cherry s Plant Results 59

45 Continue. Cauliflower s Plant 60

46 Continue. RedChilli s Plant 61

47 Continue. Potato s Plant 62

48 Continue. It s Wall Palm Tree 63

49 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

50 Disease Identification in Crops Researcher: Sibtain Abbas

51 Goals Increase in production. Quality crops. Reduce economic damage.

52 Losses in Punjab Crop Value of Damage ($ millions) Cost of Control ($ millions) Rice Wheat Cotton Totals

53 Common Diseases Fusarium Leaf Rust Leaf Blotch Wilt Chlorosis Scorch

54 Fusarium Blight Fungal Disease Causes Effect on US Economy

55 Leaf Scorch Browning of Leaf Tissues, Veins and Tips. Causes Effect

56 Basic Steps

57 Flow Chart

58 Histogram- Methodology Blurring the image. Blurred Image

59 Histogram HSV is used to improve color space accuracy.

60 Histogram Canny Edge Detection is used to further enhance the details.

61 Histogram Healthy and Diseased Histograms.

62 Histogram- Results

63 Multi Class SVM Converting RGB to Gray Scale Image Pre Processing Image Segmentation Feature Extraction Classification Testing

64 Multi Class SVM- Results

65 Multi Class SVM- Results

66 Multi Class SVM- Results Stage No of Images Execution Time (sec) Feature Extraction Training 25/ per class 3.3 Testing 20/ per class 0.7

67 Accuracy Maximum accuracy achieved after 500 iterations.

68 Future Work Improve the Accuracy. Parallel detection of Weeds and Diseases.

69 Thank You!