Virtual Grader for Apple Qualityassessment using Fruit Size and Illumiation Features

Ajay Pal Singh Chuahan; Amar Partap Singh Pharwaha

Virtual Grader for Apple Qualityassessment using Fruit Size and Illumiation Features

Ajay Pal Singh Chuahan * §

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Amar Partap Singh Pharwaha §

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§ Sant Longowal Institute of Engineering & Technology,...

Virtual Grader for Apple Qualityassessment using Fruit Size and Illumiation Features

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Abstract

The present paper reports on the development of an intelligent virtual grader for assessing apple quality using machine vision. The heart of the proposed virtual grader was executed in the form of K-Nearest Neighbor (K-NN) classifier designed on the architecture of Euclidean distance metric. K-NN classifier is executed for this particular application due to its robustness to the noisy environment. The present study revealed that fruit surface illumination is one of the major deterministic parameters affecting accuracy substantially while assessing apple quality based on fruit size. The performance of the proposed virtual grader was examined experimentally under different conditions of fruit surface illumination. An industrial grade camera connected to an image grabber was used to implement the proposed industrial-grade virtual grader using machine vision. Results of this study are quite promising with an achievement of 99% efficiency at 100% repeatability when fruit surface is exposed to an optimal value of 310 lux. However, such an attempt has not been made earlier.

References

19 Cites in Article

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Ajay Pal Singh Chuahan, Amar Partap Singh Pharwaha. 2014. "Virtual Grader for Apple Qualityassessment using Fruit Size and Illumiation Features". Global Journal of Computer Science and Technology - G: Interdisciplinary GJCST-G Volume 14 (GJCST Volume 14 Issue G4).

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GJCST Volume 14 Issue G4

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

classifier hydraulic radius intensity machine vision perimeter

Classification

D.3.3

Submission ReceivedDecember 14, 2013
Peer Review Double Blind
Handling Editor
Accepted January 2, 2014
Published December 20, 2014

Version of record

v1.2

Issue date

December 20, 2014

Language

English

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Article Matrices

Total Score: 102

Country: India

Subject: Global Journal of Computer Science and Technology - G: Interdisciplinary

Authors: Ajay Pal Singh Chuahan, Amar Partap Singh Pharwaha (PhD/Dr. count: 0)

View Count (all-time): 493

Total Views (Real + Logic): 2049

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Publish Date: 2014 01, Wed

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Total Views: 2K

Total Downloads: 138

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