Sorting of Raisins using Computer Vision Approach

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 06 | June -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 2540
Sorting of raisins using computer vision approach
Kiran Gajanan Shinde1, Dr. B.G.Patil2
1Department Of Electronics Engineering, Walchand College Of Engineering,
Sangli,Maharashtra,India
2Associate Professor, Department Of Electronics Engineering, Walchand College Of Engineering
Sangli,Maharashtra ,India,
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Abstract - In this research, an apparatus for sorting
raisins has been designed and fabricated based on computer
vision approach. This system was composed of conveyor belt,
lighting box, controlling and processing system unit. Color
feature is the most important parameter in classification
and sorting of raisins. In order to carry out image
processing and to extract useful features of captured images
by computer vision a highly efficient algorithm was
developed and implemented in MATLAB as well as Python
environment. The algorithm was consisted of background
segmentation, raisin selection and feature extraction. The
developed algorithm initially extracts the raisins by
removing the background from the taken images. It then
sorts the raisins according to their RGB color. The final step
in the algorithm was making ON and OFF the sorting
mechanism used for automatic sorting. The system can be
easily adapted for sorting other agricultural products such
as cashew and almond.
Key Words: Raisin sorting, Color image segmentation,
Computer Vision, Feature Extraction
1. INTRODUCTION
The ability to sort agricultural produce automatically is
more efficient compared to the manual inspection which is
slow, labour intensive, tedious and error prone. However,
automatic sorting of agri-produce requires an intelligent
system that can identify the agri-produce based on its
characteristics.
Our research focuses on the development of a software
package and hardware based on the identification of
features for agri-produce.
In development of Indian economy Agriculture plays a
major role. Production of raisins is in large scale, in fast
growing countries like India. Manual procedures or some
machinery based on size sieves are used for Sorting of
raisins for the export and internal usage. These machines
used are very costly and are equal to a size of room. These
machineries are called sorter machines. Sorter machines
are different for different fruits and grains based on their
sizes. Farmers feel it better for hand picking of fruits.
Manual sorting is laborious, tedious, time consuming and
inaccurate. The poor classification and sorting will lead to
reduction in quality assurance.
Due to these drawbacks of manual sorting an automated
sorting system needs to be developed which is affordable
by small traders and farmers. Automatic sorting system
increases production and also reduces the dependency on
manpower. The manual sorting and sorting has been
replaced by computer vision system which has many
advantages like high accuracy, uniformity and processing
speed. Sorting of agricultural products is done to identify
the quality of the product and to separate the good quality
from poor. Raisins are graded to get the best quality
according to industry standard. Automated system uses
different types of algorithms to extract features of the
fruits and technologies for classification of the fruits.
Automatic sorting system is adopted in order to overcome
the problems of manual sorting.
Digital Image Processing techniques were utilized for
feature extraction from an image. In this we are using
image processing toolbox in MATLAB and the same is
done by using Python. So, instead of human vision,
computer vision system is used.
The author [2] and [4] developed such sorting machines
but that machines are having high cost and the algorithm
used is very complex.
So, the objective of this paper was to design an automatic
sorting machine and an efficient algorithm for sorting of
raisin based on computer vision at low cost.
The developed system mainly consists of three major
parts, namely Conveyor system, imaging system and
sorting system.
2. Types of Raisin
India produces raisins in bulk. There are many grades and
types in raisins. They differ with color shape and size. The
types given below are the industry standard grades. The
following are Varieties of raisins available and their
characteristics.

2.1 Green Raisin
 Usually green in colour.
 Long and round shaped berries.
 These are prepared through dipping in oil and
dried in shades to get the peculiar colour and
taste.
 These are similar to Chinese and Afghan variety.
 Mainly used as a table premium variety
2.2 Golden raisin
 Usually golden in colour and some are bright
sunshine gold in colour.
 Long and round shaped berries.
 These are prepared by dipping in oil and dried in
shade to get the colour and taste. Some are
 Prepared by treating in sulphur and dried in
racks.
 These are used as table variety, food additive and
high value food industry.
 These are similar to Iranian golden raisins.
2.3 Brown raisins
 These are brown and dark brown in colour.
 These are prepared through dipping in oil and
drying in shade some are directly dried in
sunlight.
 These are used in bakery, confectionery, ice-
cream, medicinal and other culinary products.
 These are similar to American Thomson seedless
raisins.
2.4 Black raisins
 These are black in colour made from black grapes.
 These are long and round in shape.
 These are prepared by dipping in oil and dried in
shade to get the colour and taste.
Fig-1: Types of raisin
3. Proposed System
The step-by-step procedure as shown below:
3.1 Image acquisition:
Firstly, the RGB color images are captured using a digital
camera with required resolution for good quality. Here we
have used raspberry pi camera as well as some other
cameras like mobile camera and USB camera. By using any
one camera from above mentioned cameras we are getting
near about same results.
3.2 Image Pre-processing:
In pre-processing we are thresholding the captured image
to detect the raisins by removing the background.
Fig-2: System Architecture
3.3 Image processing:
After image preprocessing some morphological operations
we have performed. Once raisins are clearly detected they
are labeled and after that mean color of that raisin is given
to that raisins particular label. And we are getting average
colored image. From that by using specific RGB values of
raisin which we want separately from other are used for
sorting. The overall flow from image capturing to feature
extraction is as shown in fig [3].

3.4 Output module:
The classified data after image processing is given to the
output hardware which will further process that data to
activate the linear actuators for sorting of raisins.
4. Mechanical Assembly and Working
Our main objective was to develop a sorting machine with
LOW cost. And to achieve the speed of existing sorting
machine we require a high resolution camera. But these
cameras are having very high cost. Also the mechanism
used for such sorting machine is complex and costly.
Because it requires one air compressor also. So because of
this we designed a new mechanism to flow raisins from
the front of the camera. The complete mechanical
assembly is shown in fig [4] and [5].
Fig-3: Image Processing Flow
The mechanical assembly of the system consists of mainly
four sections: 1) Raisin feeding section, 2) Raisin imaging
section and 3) Raisin sorting section.
Fig-4: Side View of Conveyor belt and other Mechanical
Assembly
4.1 Raisin Feeding Section:
The first thing that all should know here is we have not
considered the size of raisins. We have taken all the raisins
of same size. So we are putting bulk of raisins from raisin
feeding section. From this section at a time only four
raisins are placed in the holes of conveyor belt. Here we
are using 10mm thick conveyor belt.
4.2 Raisin imaging section:
As raisin starts moving on conveyor belt, they are
captured by camera at imaging section. At a time only a
single row of four raisins is captured. The captured image
is then processed to distinguish the color of each raisin.
The image processing flow is as shown in fig [3].
Fig-5: Top View of Conveyor belt and other Mechanical
Assembly

4.3 Raisin Sorting Section:
In this section we have used small opening and closing exit
doors below the four holes of conveyor belt and that are
controlled by small dc motors. That is the raisin moving on
conveyor belt is dropped down from the hole at this stage
if door is open. Depending on color of raisin these doors
are controlled at image processing stage. That is the door
will be closed as it is or will it open should be decided
immediately after image processing in the same code.
5. Results:
Fig-6: Output after Image Processing [1]
Here we separated golden raisin from mixture of all
type of raisins. Other color also we can separate out
by changing the color threshold.
Fig-7: Output after Image Processing [2]
6. Conclusions:
Hence, without help of DSP processor the user friendly
raisin sorting system for domestic/industrial control has
developed using the concepts of Image Processing and
Robotics Mechanism.
We conclude that for fast manipulation the algorithm
implemented in MATLAB as well as in Python is suitable
for sorting of raisins and also for some other products.
Here we are getting less speed as that of big sorting
machines used in industries but it can be improved by
using high definition camera and high speed sorting
mechanism.

REFERENCES
[1] J.D.Gavade, P.K.Kharat, S.K.Laga, “Cost effective
approach for object sorting,” International Journal of
Computer Applications (0975 – 8887) Volume 52–
No.16, August 2012
[2] Mahdi Abbasgolipour, Mahmoud Omid, Alireza
Keyhani, Seyed Saeid Mohtasebi, “Sorting Raisins by
Machine Vision System,” modern applied science,
vol.4, No. 2, Feb-2010
[3] Shanmukhappa A. Angadi, Nirmala Hiregoudar, “A
Cost Effective Algorithm for Grading Raisins Using
Image Processing,” International Journal of Recent
Trends in Engineering & Research (IJRTER) Volume
02, Issue 07; July - 2016 [ISSN: 2455-1457]
[4] M. Abbasgholipour, M. Omid, A. Keyhani, S.S.
Mohtasebi, “Color image segmentation with genetic
algorithm in a raisin sorting system based on machine
vision in variable conditions,” Expert Systems with
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[5] www.mathworks.com
[6] M. Omid*, M. Abbasgolipour, A. Keyhani and S.S.
Mohtasebi, “Implementation of an Efficient Image
Processing Algorithm for Grading Raisins,”
International Journal of Signal and Image Processing
(Vol.1-2010/Iss.1) Abbasgolipour et al. /
Implementation of an Efficient Image Processing
Algorithm / pp. 31-34
[7] Avadhoot R.Telepatil, Prashant M. Jadhav, “Colour
Object Counting and Sorting Mechanism Using Image
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Trends in Engineering and Research

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