AN IMAGE PROCESSING APPROACHES ON FRUIT DEFECT DETECTION USING OPENCV

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 07 | July 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 507
AN IMAGE PROCESSING APPROACHES ON FRUIT DEFECT DETECTION
USING OPENCV
Dhivyabharathi.R [1], Dr.R.Nithya[2], Ms.A.Saranya [3]
Student [1], Dept. of Computer science and engineering, Vivekananda college of Engineering for Women, Namakkal,
Tamil Nadu, India.
Professor [2,3], Dept. of Computer science and engineering, Vivekananda college of Engineering for Women,
Namakkal, Tamil Nadu, India.
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Abstract – India is primarily a farming nation. India
grows a wide range of fruits and vegetables. India
produces the second most fruit, behind China. The image
processing method was developed becauseitisdifficultto
use a conventional method to classify the quality of fruits
in the industry. Automation of agriculture and related
industries is essential because agriculture is the
foundation of India's economy. Because the cashier must
indicate the categories for each fruit in order to
determine its price, it is difficult to recognize its various
varieties in supermarkets. The use of barcodeshassolved
most of this problem with packaged goods; However,
these items cannot be pre-packagedandmustbeweighed
because the majority of customers prefer to select their
own items. Giving codes for each organic product is one
choice, however this requires a ton of retentions and
could bring about evaluating botches. A booklet with
pictures and codes could also be given to the cashier, but
reading through it takes time. It's still hard to use
computer visiontoautomatically classify fruitsbecauseof
the many different characteristics of different kinds of
fruits. The strategy for deciding natural product quality
that depended on the shape, size,andshadeof thenatural
product as its outside attributes. In this project, the
computer vision-based method for determining fruit
quality is shown. This innovation is being involved
increasingly more in the organic product industry and
farming. Computer vision makes it possible to conduct
systematic, cost-effective, hygienic, consistent, and
objective evaluations. Organic product's appearance is
one significant quality trademark. Their external
appearance has an effect on their internal quality in
addition to influencing their market value and consumer
preferences.
Key Words: Barcodes, Automation and image processing
method.
I.INTRODUCTION
In particular for quality recognition, PC vision and picture
handling techniques have increased in value in the organic
product industry. According to research in this area,
computer vision systems could be used to improve product
quality. India is primarily an agricultural nation.India grows
a wide assortment of vegetables and organic products. In
terms of fruit production, India trails China. The industry
needed to come up with a way to process images because it
was hard to classify the quality of fruit. Since agribusiness is
India's financial establishment, computerization of farming
and related businesses is fundamental. This project
demonstrates the computer vision-based method for
determining fruit quality. This innovation is increasingly
being implemented in farming and the organic product
industry. Efficient, practical, sterile, reliable, and objective
assessments are presently potential on account of PC vision.
The appearance of an organic product is one important
quality indicator. Their external appearanceinfluencestheir
internal quality as well as their market value and consumer
preferences and choices. Indiaismostlya countrythatgrows
food. India grows a wide assortment of vegetables and
organic products. In terms of fruit production, India trails
China. The industry needed to come up with a way to
process images because it was hard to classify the quality of
fruit. Since agribusiness is the foundation of India's
economy, it is essential to automate horticulture andrelated
businesses. Organic products go through a few stages of
handling after being picked: washing, arranging, pressing,
evaluating, shipping, and once more arranging. Countries
with high cultivating effectiveness, like Israel and Australia,
have shown that they use this cutting-edge development an
incredible arrangement. The Indian fruit industry cannot
function without it.
II. PROBLEM STATEMENT
Variety, surface, and size are the necessary qualities. On the
procured picture, exact component pre-handling is
performed. The upgrade of a picture's elements that are
pivotal for ensuing handling and the concealment of
bothersome twists are the essential objectives of picture
handling. The first of the fundamental pre-processing steps
is to convert an RGB image to a grayscale one. The Dark
picture is then exposed to picture histogram evening out.
This makes it easier to adjust image intensities to increase
contrast. Eliminate noise; To get rid of noise, the median
filter is used; the Laplacian channel is utilized for edge
identification since it centres around the area with quick

power changes. This enhanced, noise-free, filtered image is
now ready for further processing.
III. EXISTING SYSTEM
Fruits are processed in several steps after they are picked:
sorting, packing, grading, transporting, and washing Fruit's
appearance is one important quality characteristic. In
addition to influencing their market value, consumer
preferences, and choice, appearance has an impact on their
internal quality to some extent. Fruit quality evaluation has
become increasingly significant in recent years in order to
satisfy the desires of consumers as well as socioeconomic
needs. Sensory properties (appearance, texture, flavor, and
aroma), nutritive values, chemical components, mechanical
properties, functional properties, and defects are all
components of produce quality. All fruits undergostructural
and chemical changes during theirshortshelflife.Asa result,
knowing how good fruits are during their shelf life iscritical.
The underlying activity of thebiological sampleisreferredto
as bio-activity. The climatic conditions also have an impact
on the fruit's bioactivity during its shelf life.
3.1 Disadvantages
 We Indians, in particular, are unable to afford the
current costs of fruit processing facilities.
 Because a fruit's physical appearance hasanimpact
on its market value, proper handlingafterharvestis
essential.
IV. PROPOSED SYSTEM
The main boundaries for deciding organic product quality
are its surface, variety, and size. Computer vision and image
processing methods have become increasingly useful in the
fruit industry, particularlyforqualitydetection.Accordingto
research in this area, computer vision systemscouldbeused
to improve product quality. Lately, the utilization of PC
vision for natural product review has developed. Computer
vision inspection systems have been enhanced with
additional features as a result of the market's constant
demand for higher-quality goods. In the space of new item
examination, PC innovation has been used in the food and
horticultural enterprises. In view of its quality, it lets you
know whether the natural product is fortunate or
unfortunate.
To fulfill the buyer's desire and socio - money related need,
natural item quality evaluation ends up being fundamental
now day to day. Nature of produce wraps unmistakable
properties (appearance, surface, taste and smell), nutritive
characteristics, compound constituents, mechanical
properties, utilitarian properties and flaws. Each natural
product undergoes underlying and compound changes
within a limited time period of their usability. Along these
lines, knowing the idea of natural items during time span of
sensible convenience is huge. Bio-activity insinuates the
secret activity of the natural model. The natural product's
bioactivity changes over the course of its usability period
and is also influenced by the environment. Different
methodologies for quality evaluation of agricultural things
have been made by different researchers. The laser bio dot
method is a non-contact, non-damaging method for
determining the fundamental action of organicproductsand
providing quality data as a result. Surface, Assortment and
Size are the critical limits for natural item quality distinctive
evidence. The assortment affirmation is essential cycle in
preparation area. The status disclosure is external quality
part. Regardless, surface is also important. Organic product
that has been abandoned can be seen on the surface. Surface
assessment perceives the non-consistency of natural item
outer surface. The size is another important boundary. It is
obvious that every customerchoosesnatural productsbased
on size.
4.1 Advantages of Proposed System
 The fruit's quality is simple to ascertain.
 A PC vision framework is utilized to supplant
manual food review by giving valid, impartial
V. RELATED WORK
5.1 ACQUIRING FRUIT-RELATED IMAGES
We compiled a list of fruit image databases, both of high and
low quality. For improved results, these natural product
picture data sets are useful.
5.2 DETECTION PROCESS
A RGB picture is changed utilizing the HSV variety space.
After that, the lower and upper ranges are determined. The
ranges of binary images are then established. After that,
transform the mask back intoa one with three channels. 3.In
this occurrence, the HSV tone thresholder script is utilizedto
decide the lower and upper edges for separating a red item.
Furthermore, the HSV variety space uncovers whether a
picture is available in this framework
5.3 DETECTION OF DEFECTIVE FRUITS
A crucial step in the pre-processing process is locating the
damaged tomato. The tomato's color image was used for the
analysis. Fruit of poor quality is deemed to have defective
skin if the pixel value is below the specified threshold. Pure
skin, also known as high-quality fruit, is represented by any
pixel value that is greater than the threshold value chosen.
After that, the total number of white pixels, which
corresponds to the total number of pixels that represent
damaged skin, is calculated.

VI. SYSTEM ARCHITECTURE
VII.OPENCV
OpenCV is the enormous open-source library for the PC
vision, computer based intelligence, and picturedealingwith
and as of now it expects a huge partconsistentlyactionwhich
is imperative in the current structures. It lets you work with
pictures and videos to identify things, faces, or, in any case,
the handwriting of a human.Right when it consolidated with
various libraries, for instance, Numpuy, python is prepared
for taking care of the OpenCV bunch structure for
examination. Utilizing vector space and carrying out a
numerical procedure on these elements, we are able to
distinguish picture design and its various highlights.
OpenCV's primary form was 1.0. OpenCV is free for both
personal and commercial use because it is distributed under
a BSD license. It is compatible with Windows, Linux, the
Macintosh operatingsystem,iOS,andAndroid,andithasC++,
Python, Java, and C++ points of interaction. When OpenCV
was first designed, the primary focus was on computational
proficiency-basedongoingapplications.Everythingiswritten
in upgraded C/C++ to take advantage of multi-focus dealing
with.
VIII. SYSTEM CONFIGURATIONS
The fruit sorting and gradingsystem'ssuccessfuloperationis
dependent on accurate image acquisition. The image is
subjected to image preprocessing because it was taken with
the camera, has noise, and its features are difficult to see. For
this project, color, texture, and size are required.
Preprocessing is performedontheacquiredimageinorderto
produce precise features. The primary objectives of image
processingare the removalofundesirabledistortionsandthe
enhancement of image features that are necessary for
subsequent processing. The first of the fundamental
preprocessing steps is to convertanRGBimagetoagrayscale
one. The dim picture is then exposed to picture histogram
balance. This makes it easier to adjust image intensities to
increase contrast. Remove noise with a filter, in this case the
median filter. The Laplacian is utilized for edge detectiondue
to the rapid intensity change that highlights the region. This
enhanced, noise-free, filtered image is now ready for further
processing. The photo has been taken. In the first place,
change the picture from RGB to grayscale. Following OSTU
thresholding, that picture is exposed to paired thresholding.
From that point forward, morphological tasks like
enlargement and disintegration are done. Opening isdoneso
that the boundaries can be seen. The major and minor axes'
lengths are then calculated.Afterthat,thesmall,medium,and
large sizes are chosen.
8.1 OBJECT DETECTION
Legitimate picture obtaining is essential to the effective
activity of the organic product arranging and evaluating
framework. The image is subjected to image preprocessing
because it was taken with the camera, has noise, and its
features are difficult to see. Variety, surface, and size are the
expected highlights for this venture. Preprocessing is
performed on the acquired image in ordertoproduceprecise
features. The primary objectives of image processing are the
removal of undesirable distortions and the enhancement of
image features thatare necessary forsubsequentprocessing.
Changing over aRGB pictureto a grayscale pictureis the first
of the essential preprocessing steps. After that, image
histogram equalization is applied to the gray image. This
makes it easier to adjust image intensities to increase
contrast. Remove noise with a filter, in this case the median
filter. The Laplacian is utilized for edge detection due to the
rapid intensity change that highlights the region. Thus, this
improved, commotion free, separated picture is ready for
additional handling. The image is taken. First, convert the
image to grayscale from RGB. That image is subjected to
binary thresholding following OSTU thresholding. Following
that, morphological operations like dilation and erosion are
performed. Opening is finished to identify the limits. The
lengths of the major and minor tomahawks are then
determined. After that, the small, medium,andlargesizesare
chosen.
IX. SCREENSHOTS
9.1 Spoiled

9.2 unspoiled
X.CONCLUSION
This project successfully and precisely identifies normal and
defective fruits based on quality using OPENCV/PYTHON.
Picture handling can be utilized to decide the nature of any
organic product, yet in addition to any organic product.
Additionally, vegetable quality can be more precisely
identified using this method. The technology will be able to
be applied to a wide range of products as a result. Manual
food inspection is being replacedbycomputervisionsystems
that provide authentic, equitable, and non-destructive
ratings. The image processing is carried out by OpenCV, a
Python-based program. The initial segment of the product is
for picture investigation, and the subsequent part is for
controlling equipment in light of the consequences ofpicture
handling. The framework works in two particular ways,with
the camera snapping the photo and the control module
playing out all picture handling. The cycles are all shown on
the screen and afterward founded on the control module's
choice. The conveyor assembly is operated.
REFERENCES
[1] Amitangshu Pal and Krishna Kant, “Magnetic Induction
Based Sensing and Localization for Fresh Food Logistics”,
2017 IEEE 42nd Conference on Local Computer Networks.
[2] Yating Chai, Howard C. Wikle, Zhenyu Wang, Shin
Horikawa, Steve Best et al., “Design of a surface-scanningcoil
detector fordirect bacteria detectiononfoodsurfacesusinga
magneto elastic biosensor”, journal of applied physics 114,
104504 (2013).
[3] Amitangshu pal, Krishna Kant, “Smart Sensing,
Communication, and Control in Perishable Food Supply
Chain”, ACM Transactions on Sensor Networks, Vol. 1, No. 1,
Article. Publication date: March 2018.
[4] Reiner Jedermann, Thomas Potsch and Chanaka Lloyd,
“Communication techniques andchallengesforwirelessfood
quality monitoring”, published by royal society 2014.
[5] AmitangshuPalandKrishnaKant,“IoT-BasedSensingand
Communications Infrastructure for the Fresh Food Supply
Chain”, published by IEEE computer society 2018.

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