An active contour computer algorithm for the classification of cucumbers
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| Title: | An active contour computer algorithm for the classification of cucumbers |
|---|---|
| Authors: | Clement, Javier1 jcg468@ual.es, Novas, Nuria1 nnovas@ual.es, Gazquez, José-Antonio1 jgazquez@ual.es, Manzano-Agugliaro, Francisco fmanzano@ual.es |
| Source: | Computers & Electronics in Agriculture. Mar2013, Vol. 92, p75-81. 7p. |
| Subjects: | Vegetable quality, Cucumbers, Algorithms, Plant classification, Iterative methods (Mathematics), Approximation theory, Error rates |
| Abstract: | Abstract: The cucumber is one of the most important crops worldwide and, because it is generally consumed fresh, it must be classified into quality categories. The European classification system includes a parameter that relates the degree of curvature relative to the length. Until now, this classification could not been be achieved with an automatic system due to the difficulty associated with correctly calculating the axis of a cucumber. This article describes a computer algorithm that uses active contours or “snakes” to classify cucumbers by length and curvature. This algorithm demonstrates an advantage in the determination of the central line of each cucumber, based on an iterative process that is quick and carries out the classification process efficiently. The method was validated against human classification for 360cucumbers and was also compared with an ellipsoid approximation method. The active contour method reduced the classification error by 15% points, compared with the ellipsoid approximation method, to 1%, with no serious errors (i.e., misclassification of Class Extra and I into Class II or vice versa). Meanwhile, the ellipsoid approximation method led to a 16% error rate, of which 2% were serious errors (an error of two classes). The developed approach is applicable to fresh cucumber commercial classification lines to meet the requirements of the European regulations for cucumber classification. [Copyright &y& Elsevier] |
| Copyright of Computers & Electronics in Agriculture is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 85901837 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An active contour computer algorithm for the classification of cucumbers – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Clement%2C+Javier%22">Clement, Javier</searchLink><relatesTo>1</relatesTo><i> jcg468@ual.es</i><br /><searchLink fieldCode="AR" term="%22Novas%2C+Nuria%22">Novas, Nuria</searchLink><relatesTo>1</relatesTo><i> nnovas@ual.es</i><br /><searchLink fieldCode="AR" term="%22Gazquez%2C+José-Antonio%22">Gazquez, José-Antonio</searchLink><relatesTo>1</relatesTo><i> jgazquez@ual.es</i><br /><searchLink fieldCode="AR" term="%22Manzano-Agugliaro%2C+Francisco%22">Manzano-Agugliaro, Francisco</searchLink><i> fmanzano@ual.es</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computers+%26+Electronics+in+Agriculture%22">Computers & Electronics in Agriculture</searchLink>. Mar2013, Vol. 92, p75-81. 7p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Vegetable+quality%22">Vegetable quality</searchLink><br /><searchLink fieldCode="DE" term="%22Cucumbers%22">Cucumbers</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+classification%22">Plant classification</searchLink><br /><searchLink fieldCode="DE" term="%22Iterative+methods+%28Mathematics%29%22">Iterative methods (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Approximation+theory%22">Approximation theory</searchLink><br /><searchLink fieldCode="DE" term="%22Error+rates%22">Error rates</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: The cucumber is one of the most important crops worldwide and, because it is generally consumed fresh, it must be classified into quality categories. The European classification system includes a parameter that relates the degree of curvature relative to the length. Until now, this classification could not been be achieved with an automatic system due to the difficulty associated with correctly calculating the axis of a cucumber. This article describes a computer algorithm that uses active contours or “snakes” to classify cucumbers by length and curvature. This algorithm demonstrates an advantage in the determination of the central line of each cucumber, based on an iterative process that is quick and carries out the classification process efficiently. The method was validated against human classification for 360cucumbers and was also compared with an ellipsoid approximation method. The active contour method reduced the classification error by 15% points, compared with the ellipsoid approximation method, to 1%, with no serious errors (i.e., misclassification of Class Extra and I into Class II or vice versa). Meanwhile, the ellipsoid approximation method led to a 16% error rate, of which 2% were serious errors (an error of two classes). The developed approach is applicable to fresh cucumber commercial classification lines to meet the requirements of the European regulations for cucumber classification. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computers & Electronics in Agriculture is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.compag.2013.01.006 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 7 StartPage: 75 Subjects: – SubjectFull: Vegetable quality Type: general – SubjectFull: Cucumbers Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Plant classification Type: general – SubjectFull: Iterative methods (Mathematics) Type: general – SubjectFull: Approximation theory Type: general – SubjectFull: Error rates Type: general Titles: – TitleFull: An active contour computer algorithm for the classification of cucumbers Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Clement, Javier – PersonEntity: Name: NameFull: Novas, Nuria – PersonEntity: Name: NameFull: Gazquez, José-Antonio – PersonEntity: Name: NameFull: Manzano-Agugliaro, Francisco IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2013 Type: published Y: 2013 Identifiers: – Type: issn-print Value: 01681699 Numbering: – Type: volume Value: 92 Titles: – TitleFull: Computers & Electronics in Agriculture Type: main |
| ResultId | 1 |
