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FUZZY LOGIC BASED COMPUTER VISION SYSTEM

FORCLASSIFICATION OF WHOLE CASHEW KERNEL

Prepared By:

Mayur Thakkar

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OBJECTIVE

The aim of this research work is to

develop an intelligent computer vision

system using fuzzy logic, for theclassification (grading) of the whole

cashew kernel that can have potential to

eliminate inefficient manual grading and

inspection approach.

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EXISTING GRADING APPROACHES

Existing methods to grade the whole

cashew kernel are:

Manual

Mechanical

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PROPOSED GRADING SYSTEM

Computer Vision System

Computer Vision System is consists of:

Two digital cameras Computer hardware

Image processing and analysis software

Fluorescent lamp.

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SCOPE OF THE SYSTEM

The Cashew classification system will be

able to:

Reduce the processing cost Maintain product uniformity

24 hour processing

Full control of grading operation

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WHOLE CASHEW

GRADING SPECIFICATION

Cashew grading standard have been

designed by considering the color and the

weight of the cashew kernel as importantcharacteristic.

Therefore, in this system color and

morphological features (length, width,

thickness) are considered as important

features to classify the whole cashew

kernels based on their grades.

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SHEWG

RADING

SPECIFIC

ATION

Cashew Kernel Type Color Characteristic

White Whole(W)Cashew kernels shall be white

in color and free from damage.

Scorched Whole(SW)

Cashew kernels shall be light

brown in color and free from

damage.

Dessert Whole(DW)

Cashew kernels shall be dark

brown in color, it may show

deep black spot and free from

damage.

COLOR BASED SPECIFICATION

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WHOLE CASHEW KERNEL

WHITE WHOLE SCORCHED WHOLE DESSERT WHOLE

CA

SHEWG

RADING

SPECIFIC

ATION

Fig. 1 Color characteristics of the whole cashew kernel

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SHEWG

RADING

SPECIFIC

ATION

WEIGHT BASED SPECIFICATION

Cashew Kernel

Grade

Number of Kernels

Per 454 gm.(pound)White whole cashew kernel

W180 170-180

W210 200-210

W240 220-240W280 260-280

W320 300-320

W400 350-400

W450 400-450

W500 450-450

Scorched whole cashew kernel

SW180 170-180

SW210 200-210SW240 220-240

SW280 260-280

SW320 300-320

SW400 350-400

SW450 400-450

SW500 450-450

Dessert whole cashew kernel

DW No sepcification

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PROPOSED SOLUTION

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SYSTEM ARCHITECTURE

System Architecture of proposed System is asshown below:

Fig. 2 System Architecture of Cashew Classification System

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In this phase, two different

RGB front and top view

images of the each cashew

kernel under investigation are

acquired as shown below:

PROPO

SEDSO

LUTION

Fig. 3 RGB Front and Top View

Images

RGB Image Acquisition

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Preprocessing phase

smoothes the image using

3x3 average filter to handle

the distortion.

PROPO

SEDSO

LUTION

Fig. 4 Preprocessing and Segmentation

Process

Preprocessing

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Because of the black background

in the image, the histogram ofthis image is always bimodal asshown in Fig.6.

Therefore threshold

segmentation technique is usedwhich differentiate the cashewkernel region from backgroundand convert the gray scale imageinto the binary image as shownin Fig. 7.

PROPO

SEDSO

LUTION

Fig. 5 Bimodal Fig. 6 Binary images of

Histogram cashew kernel

Segmentation

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Color InformationExtraction:

To extract color

information, RGB image isconverted to gray-scaleimage, then the averageintensity of the pixels thatbelongs to the cashew part

in the gray-scale image isdetermined and thisintensity value will be usedto classify the cashewkernel based on the color.P

ROPO

SEDSO

LUTION

Feature Extraction

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Weight (Size) InformationExtraction:

To estimate the weight of thecashew kernel, quantitativeinformation of the morphologicalfeatures like Length(L), Width(W)

and Thickness(T) are consideredimportant and extracted bydividing the cashew kernel regionin the binary image, in n samplesas shown in Fig.8 and takingaverage of these sample using

equations as shown below.

PROPO

SEDSO

LUTION

(a) (b) (c)Fig. 7 Morphological feature extraction

of whole cashew kernel

Feature Extraction

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Weight Information

Extraction:

Length (L) = L1+L2 . Ln-1+Ln (1)

n

Width (W) = W1+W2 . Wn-1+Wn (2)

n

Thickness (T) = T1+T2 . +Tn-1+Tn (3)

n

Because of the irregular shapeof the cashew kernel, for betteraccuracy in the calculation ofthese features, sampling strategyhas been employed.P

ROPO

SEDSO

LUTION

Feature Extraction

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In order to decide the grade of the whole

cashew kernel, in the proposed system two level

of classification has been employed.

Color based Classification

Weight based classification

PROPO

SEDSO

LUTION

Classification

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Based on the color

information, cashew

kernel is classified as

whether it is white

whole, scorched whole

or dessert whole.

PROPO

SEDSO

LUTION

Color Based Classification

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In second level of

classification, Fuzzy

Inference System (FIS) is

designed to estimate theweight of the cashew

kernel.

PROPO

SEDSO

LUTION

Fig. 8 Block Diagram of FIS

Weight Based Classification

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Fuzzy Inference System:

2. Fuzzy Rules

3. Rule Aggregation

4. Defuzzification

PROPO

SEDSO

LUTION

Weight Based Classification

Fig.10 Rule Aggregation and Defuzzification

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Finally, the grade of thecashew is decided based

on the result of these

two classifications.

PROPO

SEDSO

LUTION

Final Grading

COLOR WEIGHT FINAL

GRADE

White(W) 240 W240

Scorched(SW) 240 SW240

DESSERT 240 DW

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CONCLUSION

Inspection and grading of the whole cashew kernels

using computer vision is an alternative to manual

and mechanical method which has the potential to

automate manual grading practices thusstandardizing techniques and eliminating tedious

human inspection tasks.

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REFERENCES

*1+ Balasubramanian, D. (2001). Physical properties of raw cashew nut. Journal

of agricultural Engineering Research, 78(3), 291-297.

*2+ B.S. Ogunsina, A.I. Bamgboye (2007), Effects of pre-shelling treatment on

the physical properties of cashew nut.

*3+ Ghazanfari, A., Moghaddam. (1996). Machine vision classification of

pistachio nuts using pattern recognition and neural network.

[4] Seyed M.A.Razzavi, M.Maazaherinasab, F.Nickfar, H. Sanacefard, (2007)

Physical properties and image analysis of wild pistachio nut.

*5+ Brosnan, T., and Sun, D. W. (2004). Improving quality inspection of food

products by computer vision, Journal of Food Engineering, 61, 3-16.

*6+ A. Ghazanfari, J. Irudayaraj and A. Kusalik, 1996b. Grading pistachio nuts

using a neural networks approach.

*7+ K. Tanaka, An Introduction to Fuzzy Logic for Practical

Applications.Springer, 1997.

*8+ Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing,Prentice-Hall International, 2002.

[9] MATLAB documentation on Image Processing Toolbox.

www.mathworks.com.

[10] Kerala State Cashew Development Corporation.

www.cashewcorporation.com

http://www.mathworks.com/http://j/Final_report/www.cashewcorporation.comhttp://j/Final_report/www.cashewcorporation.comhttp://www.mathworks.com/

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Thank You