Citrus black spot detection using hyperspectral imaging
This paper describes the development of a hyperspectral imaging approach for identifying fruits infected with citrus black spot (CBS). Hyperspectral images were taken of healthy fruit and those with CBS symptoms or other potentially confounding peel conditions such as greasy spot, wind scar, or mela...
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| Veröffentlicht in: | International journal of agricultural and biological engineering 2014-12, Vol.7 (6), p.20 |
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| creator | Kim, Daegwan Burks, Thomas F Ritenour, Mark A Qin, Jianwei |
| description | This paper describes the development of a hyperspectral imaging approach for identifying fruits infected with citrus black spot (CBS). Hyperspectral images were taken of healthy fruit and those with CBS symptoms or other potentially confounding peel conditions such as greasy spot, wind scar, or melanose. Spectral angle mapper (SAM) and spectral information divergence (SID) hyperspectral analysis approaches were used to classify fruit samples into two classes: CBS or non-CBS. The classification accuracy for CBS with SAM approach was 97.90%, and 97.14% with SID. The combination of hyperspectral images and two classification approaches (SID and SAM) have proven to be effective in recognizing CBS in the presence of other potentially confounding fruit peel conditions. The study result can be a reference for the non-destructive detection of fruits infected with citrus black spot. |
| doi_str_mv | 10.3965/j.ijabe.20140706.004 |
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Hyperspectral images were taken of healthy fruit and those with CBS symptoms or other potentially confounding peel conditions such as greasy spot, wind scar, or melanose. Spectral angle mapper (SAM) and spectral information divergence (SID) hyperspectral analysis approaches were used to classify fruit samples into two classes: CBS or non-CBS. The classification accuracy for CBS with SAM approach was 97.90%, and 97.14% with SID. The combination of hyperspectral images and two classification approaches (SID and SAM) have proven to be effective in recognizing CBS in the presence of other potentially confounding fruit peel conditions. 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Hyperspectral images were taken of healthy fruit and those with CBS symptoms or other potentially confounding peel conditions such as greasy spot, wind scar, or melanose. Spectral angle mapper (SAM) and spectral information divergence (SID) hyperspectral analysis approaches were used to classify fruit samples into two classes: CBS or non-CBS. The classification accuracy for CBS with SAM approach was 97.90%, and 97.14% with SID. The combination of hyperspectral images and two classification approaches (SID and SAM) have proven to be effective in recognizing CBS in the presence of other potentially confounding fruit peel conditions. The study result can be a reference for the non-destructive detection of fruits infected with citrus black spot.</description><subject>Algorithms</subject><subject>Citrus fruits</subject><subject>Classification</subject><subject>Discriminant analysis</subject><subject>Methods</subject><subject>Power supply</subject><subject>Software</subject><subject>Vision systems</subject><subject>Wavelet transforms</subject><issn>1934-6344</issn><issn>1934-6352</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNo9jUtLxDAURoMoOI7-AxcB1633NjevpRRfMOBG10PSpGNrbWuTLvz3DiiuzsdZfIexa4RSWCVv-7LrnY9lBUigQZUAdMI2aAUVSsjq9H8TnbOLlHoARUbIDdN1l5c1cT-45oOneco8xByb3E0jX1M3Hvj79xyXNB_d4gbefbrD0V6ys9YNKV79ccveHu5f66di9_L4XN_tihmNyIX0wWJUVfCmAfBGGGqcDS5oDegaakOLXiMZb2U0BDJoVAjSOWmMdVJs2c3v77xMX2tMed9P6zIek3tUCkVVEQrxAya5Si4</recordid><startdate>20141201</startdate><enddate>20141201</enddate><creator>Kim, Daegwan</creator><creator>Burks, Thomas F</creator><creator>Ritenour, Mark A</creator><creator>Qin, Jianwei</creator><general>International Journal of Agricultural and Biological Engineering (IJABE)</general><scope>3V.</scope><scope>7QL</scope><scope>7QO</scope><scope>7SN</scope><scope>7ST</scope><scope>7T7</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BVBZV</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>RC3</scope><scope>SOI</scope></search><sort><creationdate>20141201</creationdate><title>Citrus black spot detection using hyperspectral imaging</title><author>Kim, Daegwan ; 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Hyperspectral images were taken of healthy fruit and those with CBS symptoms or other potentially confounding peel conditions such as greasy spot, wind scar, or melanose. Spectral angle mapper (SAM) and spectral information divergence (SID) hyperspectral analysis approaches were used to classify fruit samples into two classes: CBS or non-CBS. The classification accuracy for CBS with SAM approach was 97.90%, and 97.14% with SID. The combination of hyperspectral images and two classification approaches (SID and SAM) have proven to be effective in recognizing CBS in the presence of other potentially confounding fruit peel conditions. The study result can be a reference for the non-destructive detection of fruits infected with citrus black spot.</abstract><cop>Beijing</cop><pub>International Journal of Agricultural and Biological Engineering (IJABE)</pub><doi>10.3965/j.ijabe.20140706.004</doi><oa>free_for_read</oa></addata></record> |
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| ispartof | International journal of agricultural and biological engineering, 2014-12, Vol.7 (6), p.20 |
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| language | eng |
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| source | DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals |
| subjects | Algorithms Citrus fruits Classification Discriminant analysis Methods Power supply Software Vision systems Wavelet transforms |
| title | Citrus black spot detection using hyperspectral imaging |
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