Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves
Ten, widely-used vegetation indices (VIs), based on mathematical combinations of narrow-band optical reflectance measurements in the visible/near infrared wavelength range were evaluated for their ability to discriminate leaves of 1 month old wheat plants infected with yellow (stripe), leaf and stem...
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| Veröffentlicht in: | Precision agriculture 2009-12, Vol.10 (6), p.459-470 |
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| creator | Devadas, R Lamb, D. W Simpfendorfer, S Backhouse, D |
| description | Ten, widely-used vegetation indices (VIs), based on mathematical combinations of narrow-band optical reflectance measurements in the visible/near infrared wavelength range were evaluated for their ability to discriminate leaves of 1 month old wheat plants infected with yellow (stripe), leaf and stem rust. Narrow band indices representing changes in non-chlorophyll pigment concentration and the ratio of non-chlorophyll to chlorophyll pigments proved more reliable in discriminating rust infected leaves from healthy plant tissue. Yellow rust produced the strongest response in all the calculated indices when compared to healthy leaves. No single index was capable of discriminating all three rust species from each other. However the sequential application of the Anthocyanin Reflectance Index to separate healthy, yellow and mixed stem rust/leaf rust classes followed by the Transformed Chlorophyll Absorption and Reflectance Index to separate leaf and stem rust classes would provide for the required species discrimination under laboratory conditions and thus could form the basis of rust species discrimination in wheat under field conditions. |
| doi_str_mv | 10.1007/s11119-008-9100-2 |
| format | Article |
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However the sequential application of the Anthocyanin Reflectance Index to separate healthy, yellow and mixed stem rust/leaf rust classes followed by the Transformed Chlorophyll Absorption and Reflectance Index to separate leaf and stem rust classes would provide for the required species discrimination under laboratory conditions and thus could form the basis of rust species discrimination in wheat under field conditions.</description><identifier>ISSN: 1385-2256</identifier><identifier>EISSN: 1573-1618</identifier><identifier>DOI: 10.1007/s11119-008-9100-2</identifier><language>eng</language><publisher>Boston: Boston : Springer US</publisher><subject>Agriculture ; Atmospheric Sciences ; Biomedical and Life Sciences ; Chemistry and Earth Sciences ; Chlorophyll ; Computer Science ; Crop diseases ; Cultivars ; Discriminant analysis ; Flowers & plants ; Fungi ; Fungicides ; Infections ; Leaves ; Life Sciences ; Pathogens ; Pesticides ; Physics ; Pigments ; Plant diseases ; Plant tissues ; Plants ; Reflectance ; Remote sensing ; Remote Sensing/Photogrammetry ; Sensors ; Soil Science & Conservation ; Statistics for Engineering ; Studies ; Vegetation ; Vegetation mapping ; Wheat</subject><ispartof>Precision agriculture, 2009-12, Vol.10 (6), p.459-470</ispartof><rights>Springer Science+Business Media, LLC 2008</rights><rights>Springer Science+Business Media, LLC 2009</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c405t-a01e0947abad7803ac91c32491fcdebcd2c17c277c853f5b1cd127c4ca5fcc123</citedby><cites>FETCH-LOGICAL-c405t-a01e0947abad7803ac91c32491fcdebcd2c17c277c853f5b1cd127c4ca5fcc123</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11119-008-9100-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11119-008-9100-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,778,782,27911,27912,41475,42544,51306</link.rule.ids></links><search><creatorcontrib>Devadas, R</creatorcontrib><creatorcontrib>Lamb, D. 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| subjects | Agriculture Atmospheric Sciences Biomedical and Life Sciences Chemistry and Earth Sciences Chlorophyll Computer Science Crop diseases Cultivars Discriminant analysis Flowers & plants Fungi Fungicides Infections Leaves Life Sciences Pathogens Pesticides Physics Pigments Plant diseases Plant tissues Plants Reflectance Remote sensing Remote Sensing/Photogrammetry Sensors Soil Science & Conservation Statistics for Engineering Studies Vegetation Vegetation mapping Wheat |
| title | Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves |
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