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

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          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.

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          Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences

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            Machine Learning for High-Throughput Stress Phenotyping in Plants.

            Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of the decision cycle in plant stress phenotyping and plant breeding activities where different ML approaches can be deployed are (i) identification, (ii) classification, (iii) quantification, and (iv) prediction (ICQP). We provide here a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.
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              Crop losses due to diseases and their implications for global food production losses and food security

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                Author and article information

                Contributors
                psxal4@nottingham.ac.uk
                Nicola.harrison.uk@gmail.com
                Andrew.p.french@nottingham.ac.uk
                Journal
                Plant Methods
                Plant Methods
                Plant Methods
                BioMed Central (London )
                1746-4811
                10 October 2017
                10 October 2017
                2017
                : 13
                : 80
                Affiliations
                [1 ]ISNI 0000 0004 1936 8868, GRID grid.4563.4, School of Biosciences, , University of Nottingham, ; Sutton Bonington, LE12 5RD UK
                [2 ]ISNI 0000 0004 1936 8868, GRID grid.4563.4, 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 ]GRID grid.420736.4, Agriculture and Horticulture Development Board, ; Stoneleigh Park, Kenilworth, Warwickshire CV8 2TL UK
                Author information
                http://orcid.org/0000-0001-6409-0541
                http://orcid.org/0000-0002-8313-2898
                Article
                233
                10.1186/s13007-017-0233-z
                5634902
                28053646
                ce544604-cd3a-43b5-a17b-3ee2bfaf3e22
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 25 January 2017
                : 3 October 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100008123, Agriculture and Horticulture Development Board;
                Categories
                Review
                Custom metadata
                © The Author(s) 2017

                Plant science & Botany
                hyperspectral imaging,image analysis techniques,vegetation indices,plant disease and stress,early detection of stress,hyperspectral image analysis

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