This document presents a computer vision based model for fruit sorting using a K-nearest neighbor classifier. It uses color and morphological features extracted from images to classify six types of fruits (red apples, green apples, golden apples, oranges, bananas, and carrots). The methodology involves image segmentation using K-means clustering, followed by extraction of color features from RGB and HSI color spaces and morphological features. A K-nearest neighbor classifier with Euclidean distance metric is then used for classification. The system achieved 100% accuracy in classifying the six fruit types based on the extracted features.

1. Computer Vision based Model for Fruit Sorting
using K-Nearest Neighbour classifier
Seema
Department of Physics
National Institute of Technology
Kurukshetra-136119, India
E-mail:er.seema5@gmail.com
A. Kumar
Department of Physics
National Institute of Technology
Kurukshetra-136119, India
E-mail: ashavani@yahoo.com
G. S. Gill
Department of Instrumentation
Kurukshetra University
Kurukshetra-136119, India
E-mail: gsgill@kuk.ac.in
Abstract— Food grading and estimation has been observed as
a key aspect in the field of food and agriculture. Increasing
awareness towards quality of food has opened new opportunities
of research in this area. Fruit grading and classification is also an
important procedure to increase the quality evaluation in fruits
grading which affects the export market. Computer vision plays
an important role in automation of fruit classification. Total six
varities of fruits and vegetable, i.e. red delicious apples, golden
apples, green apples, oranges, bananas and carrots are analyzed.
The system uses two image databases, one image database for
training on the system and other for implementation of query
images. In the packaging industry, color and morphological
features are the most important feature for classification of
fruits. After preprocessing, segmentation is done to extract the
region of interest. In this paper, k mean clustering method is used
for segmentation to extract region of interest from background.
Color features are extracted from the RGB image and HSI
image. Morphological features are calculated from RGB
segmented image. In this paper, fruits are classified using the
nearest neighbor classifier. Euclidean Distance Metric based k-
Nearest Neighbor Classifier is developed for this particular
application. The overall accuracy of the system is 100%.
Keywords— Computer Vision; HSI color model; Euclidean
distance; k means clustering; k-Nearest Neighbor
I. INTRODUCTION
Agro industry means industry, which is connected with
agriculture. These industries focus the post-harvest process
such as processing the agricultural products after harvest and
storing the products for domestic applications. This process
also includes cleaning, sorting, grading and packaging. Sorting
and grading is one of the post-harvest process which classifies
the products based on appearance, size and shape which
determines the quality of food products. Sorting is also done
by human experts, but is more tedious, time taking process.
The above mentioned disadvantages can be overcome by
automatic sorting technique through machine vision which is
fast, accurate and cost effective. Machine vision includes the
capturing the images, analysis and processing of images,
making easy to achieve the region of interest and make easy to
determine visual quality characteristics in food products. In
recent years, many of the agricultural and food industries
which include sorting and grading fields of fruits use the
image processing and machine vision techniques. The quality
attributes such as shape, size, color and other external features
are analyzed using machine vision techniques.
Computer Vision is used to capture images from the real
world and gather from these. It includes image acquisition,
preprocessing, analyzing and understanding the sample images
to gather the information in symbolic form or numerical value.
The main aim of computer vision is to reproduce the effect of
human vision by electronically perceiving and understanding
the images [1].
Color is the most striking feature for grading and sorting of
fruits and vegetables. Leemans et al. Suggested apple grading
method and two types of apple, Golden Delicious and
Jonagold were used. Shape and color features were extracted.
This method for apple external quality grading gave 72%
accuracy for Golden Delicious and 78% Jonagold apples. The
grading of healthy fruits was better and an error rate decreases
to 5 and 10%, respectively [2]. Liming et al. suggested a
system for automatic grading of strawberry. In this L*a*b*
color images were obtained from RGB images. Major axis
length was calculated and gave information about the size of
the sample and color features were extracted from the
dominant color model on a* channel. K-means clustering
method was used for classification purpose and it gave 90%
classification accuracy for shape and 88.8% accuracy for color
based grading [3]. Suresha et al. presented automatic grading
of apple by support vector machines. In this paper, apple
images were captured. These were in the RGB color space and
threshold based segmentation was used to extract the region of
interest from the background. HSV color model is obtained by
RGB color model and average red and green color
components were determined for classification. This classifier
gave 100% accuracy in grading [4].
Shape or size are also the most important feature for
sorting of fruits. Kavdir et al. suggested a method for apple
grading by color and size features extraction from apple
images. This gave 89% accuracy [5]. Khojastehnazhand et al.
presented an approach for sorting and classification of lemon
fruits in Visual Basic 6 based upon the color and size. Volume
of sample image had been estimated and HSI images were
obtained from RGB images. HSI values were extracted and
these values were stored in a database. During testing of query
image, calculated volume and color of testing image are
compared with the saved information in the
database. The system gave 94.04% accuracy [6].
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2. II. METHODOLOGY
The proposed method has training and testing sets. In the
training phase, from a given set of training images features are
extracted and used to train the system using the K-nearest
neighbor classifier. In classification phase a given test fruit
image is segmented and then the same features are extracted
which are used for training the system for classification
purpose. These features are queried to K-nearest neighbor
classifier to label an unknown fruit. The block diagram of the
method is given in Fig 1.
Fig 1 Block Diagram of Methodology
A. Image Database
In this paper, a total of 120 images of fruits is taken, twenty
images of each fruit sample, i.e. red apple, green apple, golden
apple, orange, carrot and banana. The images have a size of
260 and having an aspect ratio unchanged and all are in JPEG
format. Although they are on a white background, lighting
varied between different images and shadows are almost
always existed around the samples. In order to extract the
regions of interest (ROI), segmentation is necessary step.
B. Image Segmentation
K-means clustering method is an unsupervised clustering
method which classifies the input data objects into multiple
classes on the basis of their distance from each other. A vector
space is formed from the data features and clustering
algorithm identifies natural clustering with them. The objects
around the centroids µii = 1, 2…k are clustered which are
computed by minimizing the following objective
  

k
i sx
ij
ij
xV
1
2
)(  (1)
Where k is the number of clusters, i.e. Si, i = 1, 2… k and
µi is the mean point or centroid of all the points xj ϵ Si [7].
Algorithm followed for K mean clustering for image
segmentation [8] is illustrated below:
Step1. The input image is read into MATLAB.
Step2. The image is transformed into L*a*b* color space
from RGB color space as all of the color information is
present in a* and b* layers only.
Step3. Colors are classified using K-means clustering in
a*b* space, with Euclidean distance to measure the distance
between two colors.
Step4. Each pixel is labeled in the images from the result
of K-means clustering with its cluster index.
Step5. Different images are generated from each cluster.
Segmentation results of banana image are shown by Fig 2.
(a) (b)
(c) (d)
Fig 2 (a) Original image, (b) segmented image from cluster 1, (c) segmented
image from cluster 2, (d) segmented image from cluster 3
C. Feature Extraction
Feature extraction is core of fruit grading and sorting
system. In this present work, color and morphological features
are extracted which are explained below:
1) Morphological Feature: Morphological feature plays
an important role in classification purpose. Analysis of
morphological features starts with detection of the fruit
boundary [9]. There are many morphological features that can
be extracted. Roundness is one of the morphological features
and it is dimensionless. It can be used to differentiate between
two categories. In first category, three types of apples and
orange lies and in second category, banana and carrot lies. But
in a category morphological features produce
misclassification. Hence color features are also extracted.
2) Color Feature: Color is also an important feature
which human uses for object discrimination. Roundness
produces error to identify the objects having same roundness
like to distinguish between banana and carrot and to
distinguish different types of apple and orange. In this paper,
both RGB and HSI color space are used.
a) RGB Model: RGB is the most common color model
in digital image processing and it is based on the primary color
components, i.e. red (R), green (G) and blue (B), which the
human eye can perceive. The RGB color space is shown as a
cube. It is based on a Cartesian coordinate system and each
color (red, green, blue) denotes one of the three orthogonal
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3. coordinate axes in 3D space as shown in Fig 3. Points along
the main diagonal are assigned to gray values from black at
origin to white at that point. Every image can be separated into
its respective red, green and blue planes and the mean of each
component is calculated [10]. This computation helps to
estimate the most dominant or least dominant primary color of
the image.
Fig 3. RGB color model
b) HSI Model: View of an object is described by its hue,
saturation and intensity by humans. Hue is a measure that
describes a pure color, whereas saturation tells about the
degree to which a pure color is diluted by white light. The HSI
color model is an ideal tool for developing digital algorithms
based on color descriptions that are natural and perceived by
human [10]. HSI model is shown in Fig 4.
Fig 4. HSI Color Model
RGB to HSI color transform:
Hue is represented by equation 2,






GifB
GifB
H


360
(2)
    
      













 
2
1
2
1 2
1
cos
BGBRGR
BRGR
 (3)
Saturation is represented by equation 4
  BGR
BGR
S ,,min
)(
3
1

 (4)
Intensity is represented by equation 5
 BGRI 
3
1 (5)
D. K-Nearest Neighbor (K-NN) Classifier
The main aim of a classifier is to assign a predefined class
to an object using the given features. Machine vision systems
usually use specially designed soft computing techniques to
accomplish the task of classification. Size and color are
considered to be important factors on the basis of which fruits
can be sorted. K-NN algorithm is a widely used technique that
is used for classification purpose and easy to implement. In the
present paper, the concept of classification is extended to
determine fruit type based on its size and color. However, it
may fail to produce adequate results in some applications due
to lack of in depth knowledge in its implementation, yet it is
the fact is that it is easy to train K-NN to a variety of situations
because it has only one parameter, that is, the number of
neighbors (k) [11].
K-NN algorithm is a typical distance based supervised
learning method. Its basic idea is that an object is classified
according to the proximity major of its neighbors and the
object being assigned to the class with whom most of its k
nearest neighbors belongs as shown in Fig 5.
Fig 5. Graphical Representation of K-NN
Although there are a number of invariants yet in this paper,
the Euclidean distance metric is used for similarity
computation. K-NN algorithm is described here. A training set
is given consisting of n pair (xi–yi). In this algorithm, first of
all distances between the sample x and the training set is
calculated and then finds the closest k training samples. In
which class most of the k training samples are classified, x can
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4. be assigned to that class. Euclidean distance between samples
is described below by mathematical formula equation:
 
2
1
),( 

k
i ii yxSMd (6)
Where M= [M1, M2, . . .MN ] and S= [S1, S2, . . . SN] are the
feartures vectors. In K-NN input feature vector is classified
into class CJ, based on a voting mechanism [12].
III. RESULT AND CONCLUSION
It was observed that the color profile for bananas, oranges,
carrots, green apples and golden apples were almost uniform
throughout the surface area for the object under evaluation.
However, in case of red apple, it has been observed that with
increase in surface area under yellow patches, the probability
of the fruit to be classified as an orange increase. This may be
due to the fact that the increase in yellow component shifts
towards the color profile of an orange. Since apple and orange
shares almost similar geometrical profile when viewed in two
dimensionally, the probability of erroneous classification
increases. On the contrary, if red apples have uniform profile
throughout as in case of test apples taken for present
experiment. The classification efficiency of 100% can be
achieved. In present work, 100% classification efficiency has
been achieved for above mentioned 6 varieties of fruits and
vegetable.
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