Virtual Grader for Apple Qualityassessment using Fruit Size and Illumiation Features

1
Ajay Pal Singh Chuahan
Ajay Pal Singh Chuahan
2
Amar Partap Singh Pharwaha
Amar Partap Singh Pharwaha
1 Sant Longowal Institute of Engineering & Technology, Longowal

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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.

19 Cites in Articles

References

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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.

Ajay Pal Singh Chuahan. 2014. \u201cVirtual Grader for Apple Qualityassessment using Fruit Size and Illumiation Features\u201d. Global Journal of Computer Science and Technology - G: Interdisciplinary GJCST-G Volume 14 (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

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v1.2

Issue date

December 20, 2014

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English

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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.

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Virtual Grader for Apple Qualityassessment using Fruit Size and Illumiation Features

Ajay Pal Singh Chuahan
Ajay Pal Singh Chuahan Sant Longowal Institute of Engineering & Technology, Longowal
Amar Partap Singh Pharwaha
Amar Partap Singh Pharwaha

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