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Screening for Plant Features

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Abstract

In this chapter, an overview of different plant features is given, from (sub)cellular to canopy level. A myriad of methods is available to measure these features using image analysis, and often, multiple methods can be used to measure the same feature. Several criteria are listed for choosing a certain (set of) image descriptor(s) to measure a plant feature. The choice is dependent on a variety of reasons, including accuracy, robustness, recording time, throughput, costs and flexibility. We conclude that hyperspectral imaging can provide a powerful set of image descriptors, which can be used to measure numerous plant features using multivariate statistical models. However, care should be taken that the estimates obtained with these statistical models provide the right measurement for the plant feature under all circumstances of interest.

Keywords

  • Hyperspectral Imaging
  • Principal Component Regression
  • Total Leaf Area
  • Image Descriptor
  • Plant Feature

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Gerie W. A. M. van der Heijden .

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van der Heijden, G.W.A.M., Polder, G. (2015). Screening for Plant Features. In: Kumar, J., Pratap, A., Kumar, S. (eds) Phenomics in Crop Plants: Trends, Options and Limitations. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2226-2_6

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