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Computer Vision and Less Complex Image Analyses to Monitor Potato Traits in Fields

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Solanum tuberosum

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2354))

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Abstract

Field phenotyping of crops has recently gained considerable attention leading to the development of new protocols for recording plant traits of interest. Phenotyping in field conditions can be performed by various cameras, sensors, and imaging platforms. In this chapter, practical aspects as well as advantages and disadvantages of aboveground phenotyping platforms are highlighted with a focus on drone-based imaging and relevant image analysis for field conditions. It includes useful planning tips for experimental design as well as protocols, sources, and tools for image acquisition, preprocessing, feature extraction, and machine learning highlighting the possibilities with computer vision. Several open and free resources are given to speed up data analysis for biologists.

This chapter targets professionals and researchers with limited computational background performing or wishing to perform phenotyping of field crops, especially with a drone-based platform. The advice and methods described focus on potato but can mostly be used for field phenotyping of any crops.

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Acknowledgments

This work was made possible through grants obtained from Nordic Council of Ministers (PPP #6P2), NordForsk (NordPlant #84597), UAS Ability (Danish Agency for Science, Technology and Innovation), Vinnova (#2016-04386), and SLU Grogrund.

Author information

Authors and Affiliations

  1. Lincoln Agri-Robotics, Lincoln Institute for Agri-Food Technology, University of Lincoln, Lincoln, UK

    Junfeng Gao

  2. Department of Plant and Environmental Sciences, University of Copenhagen, Taastrup, Denmark

    Jesper Cairo Westergaard

  3. Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, Sweden

    Erik Alexandersson

Authors
  1. Junfeng Gao
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  2. Jesper Cairo Westergaard
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  3. Erik Alexandersson
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Corresponding author

Correspondence to Erik Alexandersson .

Editor information

Editors and Affiliations

  1. Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia

    David Dobnik

  2. Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia

    Kristina Gruden

  3. Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia

    Živa Ramšak

  4. Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia

    Anna Coll

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Gao, J., Westergaard, J.C., Alexandersson, E. (2021). Computer Vision and Less Complex Image Analyses to Monitor Potato Traits in Fields. In: Dobnik, D., Gruden, K., Ramšak, Ž., Coll, A. (eds) Solanum tuberosum. Methods in Molecular Biology, vol 2354. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1609-3_13

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  • DOI: https://doi.org/10.1007/978-1-0716-1609-3_13

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1608-6

  • Online ISBN: 978-1-0716-1609-3

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