Research Report: Image Analysis for Post-Harvest Carica Papaya Grading

This report explores the use of image segmentation and soft computing techniques for the post-harvest grading of Carica papaya fruits, aiming to improve the accuracy and efficiency of quality assessment. It highlights the limitations of manual grading methods and emphasizes the need for automated systems to reduce human effort and save time. The study discusses the application of the Artificial Bee Colony (ABC) algorithm in identifying key features for fruit classification, comparing its performance against existing techniques. The research concludes that the ABC approach offers better accuracy and time complexity, making it a viable solution for grading Carica papaya fruits, contributing to economic development in regions like India where papaya production is significant. Desklib provides access to this report and many other resources for students.

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Post-Harvest Grading of Carica Papaya Fruits Using Image Segment and Soft
Computing
Abstract
As a result of the increase in terms of technology new technologies have been advanced
which have higher accuracy than before. Therefore, the natural accuracy used to detect the
quality of fruits is no more being done using experts vision. Technology has made it possible
to analyse the quality fruits which has decline human effort and saved time. This paper
discuss various development which have been done in Carica papaya fruit analysis to
improve the quality of grading of fruits.
Keywords: Carica papaya fruit, and Artificial Beef Cology (ABC) algorithm
Introduction
The primary target of fruits grading is primarily for commercial purposes. Fruits found in the
same tree comes in different qualities for instance features like flavour since the growth is
affected by environmental aspects. Particularly, fruits from dissimilar regions vary
considerably in terms of quality as well as size. Therefore, the essence of grading is not only
to standardise fruit product but it to enhance management of the true producing the fruit in a
range of ways to promote quality (Panda, and Sethy, 2017). However, research has shown
that vegetables and fruits are challenging to grade specifically and promptly as a result of
their dissimilarity in terms of elements like colour, shape, and size due to the difference in
environmental conditions as well as manual work. Consequently, the grading of fruits and
vegetables has been undertaken mainly through visual exploration in various nations.
Therefore, in this context, manual grading has been found not to be more accurate due to
lower efficiency as a result of its distinct dissimilarity in visual examination. The reason as to
why manual inspection is not effective is because it has been influenced by human health

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conditions, fatigue, lightness, and psychological conditions among others. Accordingly,
grading of fruits is vital since the concern of consumers is driven by quality on daily basis.
Therefore, this approach has made quality analysis after harvesting a significant step in the
post-harvest chain (Sun, 2016).
The grading of fruits is done on the basis of shape, colour, and size as well as the freedom
against diseases resulting from agro-chemical conditions. Nonetheless, in order to overcome
such grading to be effective it calls for man power. Therefore, in order to overcome this there
is need to employ an automatic fruit system to be used in categorising various grades. So as
to improve the quality of fruits the system is supposed to decline the labour requirement. For
in the sense of Carica it is a scientific name for papaya fruits. Papaya is the main source of
vitamins as well as minerals which is primarily used for heath related reasons and for
medicinal purposes. Consequently, Papaya is not only used as for food purposes but is
commonly used for other reasons such as cosmetic, medicinal and household. For instance in
India papaya is the leading product internationally with a yield of approximately three million
tons every year. As a result, in India papaya fruit is classified as mature and immature fruits.
Certainly to be able to classify this fruit there are several standards used to classify the
papaya fruits.
The classification is based on the affected regions whereby the affected regions are groups
into three major parts that is grade A, B, and C. in this essence, grade A is the least affected
part of the fruit, grade B id the region of the fruit which is more affected while part C is
severely affected. With the improvement in quality analysis course, India has greatly
contributed to its economic development (Saldaña, Siche, Luján, and Quevedo, 2013).
However there are various challenges that India is still facing in terms of grading the papaya
fruits. As a result, the grading process uses a semi-automatic colour which is used for
commercial purpose which act as the differentiator used to differentiate between premium

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and standard process. However the remaining grades are identified in manual way using
human vision (Rong, Ying, and Rao, 2017). Subsequently, the most significant approach
which is a motivation whose target is to enhance the Carica papaya fruit grading process with
the help of Artificial Bee Colony (ABC) algorithm. This grading system entails the structure
for grading papaya fruits using ABC which identifies the best features for comparison
between ABC and other existing techniques of classification.
In conclusion, it has been found the Carica papaya fruit can be graded successfully using
ABC approach since it has better accuracy and time complexity which is better as compared
to soft computing. Therefore, it is possible for an individual to undertake this operation
within the shortest time possible and with high accuracy.

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Bibliography
Panda, S. and Sethy, P.K., 2017. Post-harvest grading of Carica papaya fruit using image
segmentation and soft computing. International Journal of Advanced Research in Computer
Science, 8(7).
Rong, D., Ying, Y. and Rao, X., 2017. Embedded vision detection of defective orange by fast
adaptive lightness correction algorithm. Computers and Electronics in Agriculture, 138,
pp.48-59.
Saldaña, E., Siche, R., Luján, M. and Quevedo, R., 2013. Computer vision applied to the
inspection and quality control of fruits and vegetables. Brazilian journal of food
technology, 16(4), pp.254-272.
Sun, D.W. ed., 2016. Computer vision technology for food quality evaluation. Academic
Press.