Tr 00047 by dbpublications

IDL - International Digital Library Of Technology & Research Volume 1, Issue 4,April 2017

Available at: www.dbpublications.org

Internati onal e-J ournal For Technol ogy And Research-2017

Ripeness Evaluation of Mango using Image Processing Sharadha shree 1 , Dr. Chandrappa D.N 2 1 2

Student ,M.Tech, DEC, GMIT Davangere.

Professor and HOD, Dept. Electronics Engineering, GM IT DA VANGERE.

1. INTRODUCTION

ABSTRACT Abstractâ&#x20AC;&#x201D; Mangoes are delicious seasonal fruits

Agriculture is one of the largest economic sector and it

grown in the tropics. They are harvested from i ts

plays a major role in the development of our country. Th e

grove when matured enough for the market. They do

ever-increasing population demands for higher quality of

not mature uniforml y in trees but stage by stage.

mangoes with good appearance. There is a need for the

Most farmers use manual experts for ri pening

development of accurate, fast and focused quality

evaluati on of the mang oes which is ti me consuming,

determination of mangoes.

inconsistent and inaccurate . To avoi d manual effort,

Harvesting of mangoes is performed in several steps like

cutting of fruits from the farm, washing, sorting, grading,

introduced in this paper. This includes preprocessing,

packing, transporting and finally storing. Out of these

Segmentation, Feature extraction and classification.

sorting and grading are major processing tasks associated

Here 24 color features are extracted from the mang o

for preserving the quality of mangoes.

image. Classifying the mango into two di fferent

Sorting of mangoes is done based on appearance of fru its,

classes according to their maturity level using k-NN

whereas grading is done based on the overall quality

Classifier and this proposed system resulted i n the

features of fruits by considering a number of attributes

accuracy of about 97% in evaluati on of ri peness of

like shape, size, color and texture etc.

Mangoes.

Classification is necessary for the quality evaluation of

automated

Computer

vision

techni que

mangoes. Fruit industry for its excellent trading purpose goes for highly selective ones in quality and standard. It

General Terms Quality Inspection, Feature Extraction, Color Feature.

demands the suppliers and the distributors the fruits of high standards of quality, package and presentation. So there is a increasing need to supply quality fruits within a

Keywords

short period of time has development of Co mputer Vis ion Feature

Extraction

Keywordsâ&#x20AC;&#x201D;Segmentati on,

Feature Extraction, k-NN classifier.

Techniques to improve the quality. In present scenario, sorting and grading of fruit according to maturity level are performed manually before transportation. This manual sorting by visual inspection is labour intensive, time consuming and

IDL - International Digital Library

1 |P a g e

IDL - International Digital Library Of Technology & Research Volume 1, Issue 4,April 2017

Available at: www.dbpublications.org

Internati onal e-J ournal For Technol ogy And Research-2017 suffers

from the

problem of inconsistency

and

Tajul Rosli et al.

Proposed and implemented

inaccuracy in judgement by different hu man and

methodologies and algorithms to determine the grade of

provides an opportunity to apply Computer Vision

local mango production in Perlis. The main contribution

based system to assess this problem

a design and development of an efficient algorith m for detecting and sorting the mango at more than 80% accuracy in grading co mpared to hu man expert sorting.

Problem statement

This work proposes a mango grading technique for

For exports, grading is done manually by the experts now

mangoes quality classification by fuzzy logic based

which is very time consuming and subjective. So,

image processing [3].

farmers need alternatives for sorting and grading

P. Sudhakara Rao et al. (2009) have adopted HSI

mangoes. An automated mango sorting system could be

model for sorting and grading of fruits by color and

more preferable as it can be cheaper, consistent and

developed a system for on-line sorting of Apples based

could result in better overall quality. Thus the scope of

on color, size and shape. Images are captured by a color

the project is to develop an automated computer vision

CCD camera and frames are separated by a frame grabber

system for sorting of mango based on maturity level.

card and it produced the image in RGB model. The image is analyzed by using advanced image processing

Literature Survey

techniques to estimate the color of image. Using image processing system achieved around 98 % accuracy in

Chandra Nandi et al. (2012), imp lemented a computer vision based system for automatic grading and sorting of mangoes based on maturity level fro m its RGB image frame, collected with the help of CCD camera. Parameters of different classes of mangoes are estimated using Gaussian Mixture Model. Graph contour tracking method based on chain code is adapted for finding the boundary of the mango. This automated technique is good but is further affected by ambient light intensity. Response time of system is on the order of 50 ms [1]. CCD camera was used to collect video image of Mangoes and several significant features of maturity

color inspection of apples [4]. Suzanawati Abu et al.

Proposed and imp lemented

Automated Mango Fruit Assessment Using Fuzzy Logic Approach. This work developed a new method of automated mango Size and grade assessment using RGB fiber optic sensor and fuzzy log ic approach. The calculation of maximu m, min imu m and mean values based on RGB fiber optic sensor. To analyse the data and make the classification for the mango fruit uses the minimu m entropy formu lation method. The automated mango grading system using fuzzy logic achieved 77.78% accuracy in overall categories [5].

level of Mango was obtained. Colour of Mango was estimated using Gaussian Mixture Model. Accuracy can

Methodology

be improved by using Support vector machine and neural network. Gaussian Mixture Model (GMM ) and fuzzy

The follo wing steps are followed to perform the ripeness

logic was comb ined for size based grading of Mango.

evaluation of mango

Size of Mango was calculated using pixel area covered

1. Select the mango image.

by Mango [2].

2. Crop the area of mango. 3. Find and calcu late the major axis.

IDL - International Digital Library

2 |P a g e

IDL - International Digital Library Of Technology & Research Volume 1, Issue 4,April 2017

Available at: www.dbpublications.org

Internati onal e-J ournal For Technol ogy And Research-2017 4. A lign the mango image.

Fig 1 .Fl owchat

5. Divide the 3 regions. 6. Extract the color features fro m the 3 reg ions. 7. Use nearest neighbour classification algorith m for database features. 8. Output results based on the maturity level.

The basic global threshold, T, is calculated as follows: 1.

Select

init ial

estimate

for

(typically the average grey level in the image) 2.

Segment the image using T to produce two groups of pixels: G1 consisting of pixels with grey levels >T and G2 consisting pixels with grey levels ≤ T

Co mpute the average grey levels of pixels in G1 to give μ1 and G2 to give μ2

Co mpute

new threshold

value:

Repeat steps 2 – 4 until the difference in T in successive iterations is less than a predefined limit T∞.

IDL - International Digital Library

3 |P a g e

IDL - International Digital Library Of Technology & Research Volume 1, Issue 4,April 2017

Available at: www.dbpublications.org

Internati onal e-J ournal For Technol ogy And Research-2017

Fig 2. Input Mango i mage

Fig 3. Conversion to g rays ca le

Fig 4. Image after threshol ding

Fig 5. Centroi d of mang o (red * )

IDL - International Digital Library

4 |P a g e

IDL - International Digital Library Of Technology & Research Volume 1, Issue 4,April 2017

Available at: www.dbpublications.org

Internati onal e-J ournal For Technol ogy And Research-2017 After the analysis it was found that categorizat ion into unripe, semi ripe and ripe is possible by the range as in Table 1.

So at this point the dominant color, spots and size features are extracted from the mango. All the features with corresponding values are shown in Fig 6. These are the feature parameters for the grading. Based on this the grading rules are created. Some of them are shown Table 2. Table 2: S ample rules of grading

Fig 6. Dominant bin in histogram

Rule 1 If spots are more and Size is less and Color is unripe then Grade is Rejected

Rule 4 If spots are average and Size is medium and Color is unripe then Grade is Grade 3

Rule 8 If spots are less and Size is medium and Color is semi ripe then Grade is Grade 2

Rule 10 If spots are less and Size is large Color is Ripe then Grade is Grade 1

The following range of the dominant bin over ‘a’ channel of Lab color space was observed Table 1: Dominant ‘a’ color range Dominant a