Title |
HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging
|
---|---|
Published in |
Plant Methods, January 2015
|
DOI | 10.1186/s13007-015-0043-0 |
Pubmed ID | |
Authors |
Sergej Bergsträsser, Dimitrios Fanourakis, Simone Schmittgen, Maria Pilar Cendrero-Mateo, Marcus Jansen, Hanno Scharr, Uwe Rascher |
Abstract |
Combined assessment of leaf reflectance and transmittance is currently limited to spot (point) measurements. This study introduces a tailor-made hyperspectral absorption-reflectance-transmittance imaging (HyperART) system, yielding a non-invasive determination of both reflectance and transmittance of the whole leaf. We addressed its applicability for analysing plant traits, i.e. assessing Cercospora beticola disease severity or leaf chlorophyll content. To test the accuracy of the obtained data, these were compared with reflectance and transmittance measurements of selected leaves acquired by the point spectroradiometer ASD FieldSpec, equipped with the FluoWat device. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 3 | 43% |
Curaçao | 1 | 14% |
New Zealand | 1 | 14% |
Unknown | 2 | 29% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 43% |
Science communicators (journalists, bloggers, editors) | 3 | 43% |
Scientists | 1 | 14% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 2 | 1% |
Belgium | 2 | 1% |
France | 1 | <1% |
Chile | 1 | <1% |
Argentina | 1 | <1% |
Australia | 1 | <1% |
Unknown | 164 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 43 | 25% |
Researcher | 43 | 25% |
Student > Master | 18 | 10% |
Student > Bachelor | 7 | 4% |
Other | 7 | 4% |
Other | 20 | 12% |
Unknown | 34 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 80 | 47% |
Engineering | 14 | 8% |
Environmental Science | 10 | 6% |
Earth and Planetary Sciences | 9 | 5% |
Computer Science | 4 | 2% |
Other | 11 | 6% |
Unknown | 44 | 26% |