Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress

Amy Lowe; Nicola Harrison; Andrew P French

doi:10.1186/s13007-017-0233-z

Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress

Plant Methods. 2017 Oct 10:13:80. doi: 10.1186/s13007-017-0233-z. eCollection 2017.

Authors

Amy Lowe^{1

2

3}, Nicola Harrison^{3

4}, Andrew P French^{1

2}

Affiliations

¹ School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD UK.
² School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham, NG8 1BB UK.
³ NIAB EMR, New Road, East Malling, Kent, ME19 6BJ UK.
⁴ Agriculture and Horticulture Development Board, Stoneleigh Park, Kenilworth, Warwickshire CV8 2TL UK.

Abstract

This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods. Imaging applications are discussed in relation to both field and glasshouse-based plants, and techniques are sectioned into 'healthy and diseased plant classification' with an emphasis on classification accuracy, early detection of stress, and disease severity. A central focus of the review is the use of hyperspectral imaging and how this is being utilised to find additional information about plant health, and the ability to predict onset of disease. A summary of techniques used to detect biotic and abiotic stress in plants is presented, including the level of accuracy associated with each method.

Keywords: Early detection of stress; Hyperspectral image analysis; Hyperspectral imaging; Image analysis techniques; Plant disease and stress; Vegetation Indices.

Publication types

Review