Machine Learning Techniques for Predicting Crop Photosynthetic Capacity from Leaf Reflectance Spectra-Reference-Cited by-同舟云学术

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Machine Learning Techniques for Predicting Crop Photosynthetic Capacity from Leaf Reflectance Spectra

Published:2017-06 Issue:6 Volume:10 Page:878-890
ISSN:1674-2052
Container-title:Molecular Plant
language:en
Short-container-title:Molecular Plant

Author:

Heckmann David,Schlüter Urte,Weber Andreas P.M.

Funder

Deutsche Forschungsgemeinschaft

German Federal Ministry of Research and Education

Publisher

Elsevier BV

Subject

Plant Science,Molecular Biology

Reference62 articles.

1. Using leaf optical properties to detect ozone effects on foliar biochemistry;Ainsworth;Photosynth. Res.,2014

2. In-field assessment of single leaf nitrogen status by spectral reflectance measurements;Alchanatis;Precision Agric.,2005

3. From sunlight to phytomass: on the potential efficiency of converting solar radiation to phyto-energy;Amthor;New Phytol.,2010

4. Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat;Atzberger;Comput. Electron. Agr.,2010

5. Chlorophyll fluorescence: a probe of photosynthesis in vivo;Baker;Annu. Rev. Plant Biol.,2008

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