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Conference Paper: Study on fruit quality measurement and evaluation based on color identification

TitleStudy on fruit quality measurement and evaluation based on color identification
Authors
Wang, YCui, YChen, SZhang, PHuang, HHuang, GQ
KeywordsBp neural network
Color identification
Fruit
Quality evaluation
Rgb color model
Issue Date2009
PublisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xml
Citation
International Conference on Optical Instruments and Technology (OIT): Optoelectronic Imaging and Process Technology, Shanghai, China, 19-21 October 2009. In Proceedings of SPIE, 2009, v. 7513, p. 277-286, article no. 75130F How to Cite?
DOI: http://dx.doi.org/10.1117/12.839698
AbstractA non-destructive measuring and evaluating method for fruits is proposed based on color identification. The color images of fruits are taken firstly. Then, images' RGB histograms are calculated and used as quality parameters for fruits. A BP neural network with three layers is established. Its input and output are the RGB histograms and evaluating results, respectively. After training, the qualities of fruits are identified by the BP network according to the histogram of RGB of fruits' images. For verifying the proposed method, the qualities of bananas are measured and evaluated. Experiment results show the reliability and feasibility of proposed method. © 2009 SPIE.
Persistent Identifierhttp://hdl.handle.net/10722/100151
ISBN
9780819478993
ISSN
0277-786X
2020 SCImago Journal Rankings: 0.192
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorWang, Yen_HK
dc.contributor.authorCui, Yen_HK
dc.contributor.authorChen, Sen_HK
dc.contributor.authorZhang, Pen_HK
dc.contributor.authorHuang, Hen_HK
dc.contributor.authorHuang, GQen_HK
dc.date.accessioned2010-09-25T18:58:44Z-
dc.date.available2010-09-25T18:58:44Z-
dc.date.issued2009en_HK
dc.identifier.citationInternational Conference on Optical Instruments and Technology (OIT): Optoelectronic Imaging and Process Technology, Shanghai, China, 19-21 October 2009. In Proceedings of SPIE, 2009, v. 7513, p. 277-286, article no. 75130Fen_HK
dc.identifier.isbn9780819478993-
dc.identifier.issn0277-786Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/100151-
dc.description.abstractA non-destructive measuring and evaluating method for fruits is proposed based on color identification. The color images of fruits are taken firstly. Then, images' RGB histograms are calculated and used as quality parameters for fruits. A BP neural network with three layers is established. Its input and output are the RGB histograms and evaluating results, respectively. After training, the qualities of fruits are identified by the BP network according to the histogram of RGB of fruits' images. For verifying the proposed method, the qualities of bananas are measured and evaluated. Experiment results show the reliability and feasibility of proposed method. © 2009 SPIE.en_HK
dc.languageengen_HK
dc.publisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xmlen_HK
dc.relation.ispartofProceedings of SPIE - The International Society for Optical Engineeringen_HK
dc.rightsCopyright 2009 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited. This article is available online at https://doi.org/10.1117/12.839698-
dc.subjectBp neural networken_HK
dc.subjectColor identificationen_HK
dc.subjectFruiten_HK
dc.subjectQuality evaluationen_HK
dc.subjectRgb color modelen_HK
dc.titleStudy on fruit quality measurement and evaluation based on color identificationen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailHuang, GQ:gqhuang@hkucc.hku.hken_HK
dc.identifier.authorityHuang, GQ=rp00118en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1117/12.839698en_HK
dc.identifier.scopuseid_2-s2.0-73849089908en_HK
dc.identifier.hkuros168598en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-73849089908&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume7513en_HK
dc.identifier.spage277, article no. 75130Fen_HK
dc.identifier.epage286, article no. 75130Fen_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridWang, Y=7601492781en_HK
dc.identifier.scopusauthoridCui, Y=35331683800en_HK
dc.identifier.scopusauthoridChen, S=23977387200en_HK
dc.identifier.scopusauthoridZhang, P=35175258500en_HK
dc.identifier.scopusauthoridHuang, H=35092692000en_HK
dc.identifier.scopusauthoridHuang, GQ=7403425048en_HK
dc.identifier.issnl0277-786X-

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