Navel orange Blemish identification for quality grading system

Minghui Liu; Gadi Ben-Tal; Napoleon H. Reyes; Andre L.C. Barczak

doi:10.1007/978-3-642-10684-2_75

Navel orange Blemish identification for quality grading system

Minghui Liu^*, Gadi Ben-Tal, Napoleon H. Reyes, Andre L.C. Barczak

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

2 Citations (Scopus)

Abstract

A novel automated blemish detection system for ripe and unripe oranges is proposed in this paper. The algorithm is unique in that it does not rely on the global variations between pixels depicting the colours of an orange. By utilizing a priori knowledge of the properties of rounded convex objects, we introduce a set of colour classes that effectively 'peels-off' the orange skin in order of increasing intensity layers. These layers are then examined independently, allowing us to scrutinize the skin more accurately for any blemishes present locally at the layer's intensity variation range. The efficacy of the algorithm is demonstrated using 170 images captured with a commercial fruit sorting machine as the benchmarking test set. Our results show that the system correctly classified 96% of good oranges and 97% of blemished oranges. The proposed system does not require any training.

Original language	English
Title of host publication	Neural Information Processing - 16th International Conference, ICONIP 2009, Proceedings
Editors	Chi Sing Leung, Minho Lee, Jonathan H. Chan
Publisher	Springer
Pages	675-682
Number of pages	8
Volume	PART 2
ISBN (Print)	364210682X, 9783642106828
DOIs	https://doi.org/10.1007/978-3-642-10684-2_75
Publication status	Published - 2009
Externally published	Yes
Event	16th International Conference on Neural Information Processing, ICONIP 2009 - Bangkok, Thailand Duration: 1 Dec 2009 → 5 Dec 2009 Conference number: 16th

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	5864 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Neural Information Processing, ICONIP 2009
Abbreviated title	ICONIP 2009
Country/Territory	Thailand
City	Bangkok
Period	1/12/09 → 5/12/09

Access to Document

10.1007/978-3-642-10684-2_75

Cite this

Liu, M., Ben-Tal, G., Reyes, N. H., & Barczak, A. L. C. (2009). Navel orange Blemish identification for quality grading system. In C. S. Leung, M. Lee, & J. H. Chan (Eds.), Neural Information Processing - 16th International Conference, ICONIP 2009, Proceedings (Vol. PART 2, pp. 675-682). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5864 LNCS, No. PART 2). Springer. https://doi.org/10.1007/978-3-642-10684-2_75

Liu, Minghui ; Ben-Tal, Gadi ; Reyes, Napoleon H. et al. / Navel orange Blemish identification for quality grading system. Neural Information Processing - 16th International Conference, ICONIP 2009, Proceedings. editor / Chi Sing Leung ; Minho Lee ; Jonathan H. Chan. Vol. PART 2 Springer, 2009. pp. 675-682 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "Navel orange Blemish identification for quality grading system",

abstract = "A novel automated blemish detection system for ripe and unripe oranges is proposed in this paper. The algorithm is unique in that it does not rely on the global variations between pixels depicting the colours of an orange. By utilizing a priori knowledge of the properties of rounded convex objects, we introduce a set of colour classes that effectively 'peels-off' the orange skin in order of increasing intensity layers. These layers are then examined independently, allowing us to scrutinize the skin more accurately for any blemishes present locally at the layer's intensity variation range. The efficacy of the algorithm is demonstrated using 170 images captured with a commercial fruit sorting machine as the benchmarking test set. Our results show that the system correctly classified 96% of good oranges and 97% of blemished oranges. The proposed system does not require any training.",

author = "Minghui Liu and Gadi Ben-Tal and Reyes, {Napoleon H.} and Barczak, {Andre L.C.}",

year = "2009",

doi = "10.1007/978-3-642-10684-2_75",

language = "English",

isbn = "364210682X",

volume = "PART 2",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

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editor = "Leung, {Chi Sing} and Minho Lee and Chan, {Jonathan H.}",

booktitle = "Neural Information Processing - 16th International Conference, ICONIP 2009, Proceedings",

address = "Germany",

note = "16th International Conference on Neural Information Processing, ICONIP 2009, ICONIP 2009 ; Conference date: 01-12-2009 Through 05-12-2009",

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Liu, M, Ben-Tal, G, Reyes, NH & Barczak, ALC 2009, Navel orange Blemish identification for quality grading system. in CS Leung, M Lee & JH Chan (eds), Neural Information Processing - 16th International Conference, ICONIP 2009, Proceedings. vol. PART 2, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 5864 LNCS, Springer, pp. 675-682, 16th International Conference on Neural Information Processing, ICONIP 2009, Bangkok, Thailand, 1/12/09. https://doi.org/10.1007/978-3-642-10684-2_75

Navel orange Blemish identification for quality grading system. / Liu, Minghui; Ben-Tal, Gadi; Reyes, Napoleon H. et al.
Neural Information Processing - 16th International Conference, ICONIP 2009, Proceedings. ed. / Chi Sing Leung; Minho Lee; Jonathan H. Chan. Vol. PART 2 Springer, 2009. p. 675-682 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5864 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

TY - GEN

T1 - Navel orange Blemish identification for quality grading system

AU - Liu, Minghui

AU - Ben-Tal, Gadi

AU - Reyes, Napoleon H.

AU - Barczak, Andre L.C.

N1 - Conference code: 16th

PY - 2009

Y1 - 2009

N2 - A novel automated blemish detection system for ripe and unripe oranges is proposed in this paper. The algorithm is unique in that it does not rely on the global variations between pixels depicting the colours of an orange. By utilizing a priori knowledge of the properties of rounded convex objects, we introduce a set of colour classes that effectively 'peels-off' the orange skin in order of increasing intensity layers. These layers are then examined independently, allowing us to scrutinize the skin more accurately for any blemishes present locally at the layer's intensity variation range. The efficacy of the algorithm is demonstrated using 170 images captured with a commercial fruit sorting machine as the benchmarking test set. Our results show that the system correctly classified 96% of good oranges and 97% of blemished oranges. The proposed system does not require any training.

AB - A novel automated blemish detection system for ripe and unripe oranges is proposed in this paper. The algorithm is unique in that it does not rely on the global variations between pixels depicting the colours of an orange. By utilizing a priori knowledge of the properties of rounded convex objects, we introduce a set of colour classes that effectively 'peels-off' the orange skin in order of increasing intensity layers. These layers are then examined independently, allowing us to scrutinize the skin more accurately for any blemishes present locally at the layer's intensity variation range. The efficacy of the algorithm is demonstrated using 170 images captured with a commercial fruit sorting machine as the benchmarking test set. Our results show that the system correctly classified 96% of good oranges and 97% of blemished oranges. The proposed system does not require any training.

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DO - 10.1007/978-3-642-10684-2_75

M3 - Conference contribution

AN - SCOPUS:76249101428

SN - 364210682X

SN - 9783642106828

VL - PART 2

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 675

EP - 682

BT - Neural Information Processing - 16th International Conference, ICONIP 2009, Proceedings

A2 - Leung, Chi Sing

A2 - Lee, Minho

A2 - Chan, Jonathan H.

PB - Springer

T2 - 16th International Conference on Neural Information Processing, ICONIP 2009

Y2 - 1 December 2009 through 5 December 2009

ER -

Liu M, Ben-Tal G, Reyes NH, Barczak ALC. Navel orange Blemish identification for quality grading system. In Leung CS, Lee M, Chan JH, editors, Neural Information Processing - 16th International Conference, ICONIP 2009, Proceedings. Vol. PART 2. Springer. 2009. p. 675-682. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-10684-2_75

Navel orange Blemish identification for quality grading system

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