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

@article{Heckmann2017MachineLT,
  title={Machine Learning Techniques for Predicting Crop Photosynthetic Capacity from Leaf Reflectance Spectra.},
  author={David Heckmann and Urte Schl{\"u}ter and Andreas P M Weber},
  journal={Molecular plant},
  year={2017},
  volume={10 6},
  pages={
          878-890
        }
}
Harnessing natural variation in photosynthetic capacity is a promising route toward yield increases, but physiological phenotyping is still too laborious for large-scale genetic screens. Here, we evaluate the potential of leaf reflectance spectroscopy to predict parameters of photosynthetic capacity in Brassica oleracea and Zea mays, a C3 and a C4 crop, respectively. To this end, we systematically evaluated properties of reflectance spectra and found that they are surprisingly similar over a… CONTINUE READING
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