Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence
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| Title: | Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence |
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| Authors: | Qin, Jianwei1 qinj@ufl.edu, Burks, Thomas F.1, Ritenour, Mark A.2, Bonn, W. Gordon3 |
| Source: | Journal of Food Engineering. Jul2009, Vol. 93 Issue 2, p183-191. 9p. |
| Subjects: | Imaging systems in biology, Canker (Plant disease), Citrus diseases & pests, Spectral reflectance, Grape diseases & pests, Food safety, Diagnosis |
| Abstract: | Abstract: Citrus canker is one of the most devastating diseases that threaten marketability of citrus crops. This research was aimed to develop a hyperspectral imaging approach for detecting canker lesions on citrus fruit. A hyperspectral imaging system was developed for acquiring reflectance images from citrus samples in the spectral region from 450 to 930nm. Ruby Red grapefruits with cankerous, normal and other common peel diseases including greasy spot, insect damage, melanose, scab, and wind scar were tested. Spectral information divergence (SID) classification method, which was based on quantifying the spectral similarities by using a predetermined canker reference spectrum, was performed on the hyperspectral images of the grapefruits for differentiating canker from normal fruit peels and other citrus surface conditions. The overall classification accuracy was 96.2% using an optimized SID threshold value of 0.008, which was determined under the condition that the errors of false negative and false positive were weighted equally. Considering the high economic impact of missing a cankerous fruit, zero false negative error was achieved by using a threshold value of 0.009, under which the classification accuracy was 95.2%. This research demonstrated that hyperspectral imaging technique coupled with the SID based image classification method could be used for discriminating citrus canker from other confounding diseases. [Copyright &y& Elsevier] |
| Copyright of Journal of Food Engineering 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: 37240652 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Qin%2C+Jianwei%22">Qin, Jianwei</searchLink><relatesTo>1</relatesTo><i> qinj@ufl.edu</i><br /><searchLink fieldCode="AR" term="%22Burks%2C+Thomas+F%2E%22">Burks, Thomas F.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ritenour%2C+Mark+A%2E%22">Ritenour, Mark A.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Bonn%2C+W%2E+Gordon%22">Bonn, W. Gordon</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Food+Engineering%22">Journal of Food Engineering</searchLink>. Jul2009, Vol. 93 Issue 2, p183-191. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Imaging+systems+in+biology%22">Imaging systems in biology</searchLink><br /><searchLink fieldCode="DE" term="%22Canker+%28Plant+disease%29%22">Canker (Plant disease)</searchLink><br /><searchLink fieldCode="DE" term="%22Citrus+diseases+%26+pests%22">Citrus diseases & pests</searchLink><br /><searchLink fieldCode="DE" term="%22Spectral+reflectance%22">Spectral reflectance</searchLink><br /><searchLink fieldCode="DE" term="%22Grape+diseases+%26+pests%22">Grape diseases & pests</searchLink><br /><searchLink fieldCode="DE" term="%22Food+safety%22">Food safety</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnosis%22">Diagnosis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: Citrus canker is one of the most devastating diseases that threaten marketability of citrus crops. This research was aimed to develop a hyperspectral imaging approach for detecting canker lesions on citrus fruit. A hyperspectral imaging system was developed for acquiring reflectance images from citrus samples in the spectral region from 450 to 930nm. Ruby Red grapefruits with cankerous, normal and other common peel diseases including greasy spot, insect damage, melanose, scab, and wind scar were tested. Spectral information divergence (SID) classification method, which was based on quantifying the spectral similarities by using a predetermined canker reference spectrum, was performed on the hyperspectral images of the grapefruits for differentiating canker from normal fruit peels and other citrus surface conditions. The overall classification accuracy was 96.2% using an optimized SID threshold value of 0.008, which was determined under the condition that the errors of false negative and false positive were weighted equally. Considering the high economic impact of missing a cankerous fruit, zero false negative error was achieved by using a threshold value of 0.009, under which the classification accuracy was 95.2%. This research demonstrated that hyperspectral imaging technique coupled with the SID based image classification method could be used for discriminating citrus canker from other confounding diseases. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Food Engineering 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.jfoodeng.2009.01.014 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 183 Subjects: – SubjectFull: Imaging systems in biology Type: general – SubjectFull: Canker (Plant disease) Type: general – SubjectFull: Citrus diseases & pests Type: general – SubjectFull: Spectral reflectance Type: general – SubjectFull: Grape diseases & pests Type: general – SubjectFull: Food safety Type: general – SubjectFull: Diagnosis Type: general Titles: – TitleFull: Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Qin, Jianwei – PersonEntity: Name: NameFull: Burks, Thomas F. – PersonEntity: Name: NameFull: Ritenour, Mark A. – PersonEntity: Name: NameFull: Bonn, W. Gordon IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 07 Text: Jul2009 Type: published Y: 2009 Identifiers: – Type: issn-print Value: 02608774 Numbering: – Type: volume Value: 93 – Type: issue Value: 2 Titles: – TitleFull: Journal of Food Engineering Type: main |
| ResultId | 1 |
