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Plants in computer vision

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Edited by: Dr Hannah Dee

An article collection in Plant Methods.

New methods in plant biology have led to an explosion in data types and methods of data acquisition, and much of this data is image- or video- based in nature. Computer vision, image analysis and image processing techniques are being applied to more plant data than ever before.

Inspired by the BVMA Technical Meeting: Plants in Computer Vision, this Plant Methods article collection showcases original work on the boundary between computer vision and plant science; specifically concentrating on computer algorithms, methods and systems which analyse plant images, videos and scans.  

This collection includes work on plant detection, segmentation and modelling from image data, at many different scales (from microscopic images up to field scale measurements). Some articles describe complete software, ready for biologists to use today. Other articles explore algorithm development, pointing the way towards future software capabilities.

This collection of articles has not been sponsored and articles have undergone the journal's standard peer-review process overseen by the Editors. The Editors declare no competing interests.

  1. Content type: Methodology

    Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques ty...

    Authors: Jordan Ubbens, Mikolaj Cieslak, Przemyslaw Prusinkiewicz and Ian Stavness

    Citation: Plant Methods 2018 14:6

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  2. Content type: Methodology

    Miscanthus is a leading second generation bio-energy crop. It is mostly rhizome propagated; however, the increasing use of seed is resulting in a greater need to investigate germination. Miscanthus seed are small...

    Authors: Danny Awty-Carroll, John Clifton-Brown and Paul Robson

    Citation: Plant Methods 2018 14:5

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  3. Content type: Software

    Plants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with ...

    Authors: Ji Zhou, Christopher Applegate, Albor Dobon Alonso, Daniel Reynolds, Simon Orford, Michal Mackiewicz, Simon Griffiths, Steven Penfield and Nick Pullen

    Citation: Plant Methods 2017 13:117

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  4. Content type: Methodology

    Fine-grained recognition of plants from images is a challenging computer vision task, due to the diverse appearance and complex structure of plants, high intra-class variability and small inter-class differenc...

    Authors: Milan Šulc and Jiří Matas

    Citation: Plant Methods 2017 13:115

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  5. Content type: Software

    Developmental biology has made great strides in recent years towards the quantification of cellular properties during development. This requires tissues to be imaged and segmented to generate computerised vers...

    Authors: Annamária Kiss, Typhaine Moreau, Vincent Mirabet, Cerasela Iliana Calugaru, Arezki Boudaoud and Pradeep Das

    Citation: Plant Methods 2017 13:114

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  6. Content type: Methodology

    In maize, silks are hundreds of filaments that simultaneously emerge from the ear for collecting pollen over a period of 1–7 days, which largely determines grain number especially under water deficit. Silk gro...

    Authors: Nicolas Brichet, Christian Fournier, Olivier Turc, Olivier Strauss, Simon Artzet, Christophe Pradal, Claude Welcker, François Tardieu and Llorenç Cabrera-Bosquet

    Citation: Plant Methods 2017 13:96

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  7. Content type: Methodology

    Improvements in high-throughput phenotyping technologies are rapidly expanding the scope and capacity of plant biology studies to measure growth traits. Nevertheless, the costs of commercial phenotyping equipm...

    Authors: Andrei Dobrescu, Livia C. T. Scorza, Sotirios A. Tsaftaris and Alistair J. McCormick

    Citation: Plant Methods 2017 13:95

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  8. Content type: Methodology article

    Accurate and quantitative phenotypic data in plant breeding programmes is vital in breeding to assess the performance of genotypes and to make selections. Traditional strawberry phenotyping relies on the human...

    Authors: Joe Q. He, Richard J. Harrison and Bo Li

    Citation: Plant Methods 2017 13:93

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  9. Content type: Research

    Automated species identification is a long term research subject. Contrary to flowers and fruits, leaves are available throughout most of the year. Offering margin and texture to characterize a species, they a...

    Authors: Michael Rzanny, Marco Seeland, Jana Wäldchen and Patrick Mäder

    Citation: Plant Methods 2017 13:97

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  10. Content type: Methodology

    Hyperspectral imaging is a technology that can be used to monitor plant responses to stress. Hyperspectral images have a full spectrum for each pixel in the image, 400–2500 nm in this case, giving detailed inf...

    Authors: Dominic Williams, Avril Britten, Susan McCallum, Hamlyn Jones, Matt Aitkenhead, Alison Karley, Ken Loades, Ankush Prashar and Julie Graham

    Citation: Plant Methods 2017 13:74

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  11. Content type: Research

    Wheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs ...

    Authors: Nathan Hughes, Karen Askew, Callum P. Scotson, Kevin Williams, Colin Sauze, Fiona Corke, John H. Doonan and Candida Nibau

    Citation: Plant Methods 2017 13:76

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