22
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Charcoal rot is a fungal disease that thrives in warm dry conditions and affects the yield of soybeans and other important agronomic crops worldwide. There is a need for robust, automatic and consistent early detection and quantification of disease symptoms which are important in breeding programs for the development of improved cultivars and in crop production for the implementation of disease control measures for yield protection. Current methods of plant disease phenotyping are predominantly visual and hence are slow and prone to human error and variation. There has been increasing interest in hyperspectral imaging applications for early detection of disease symptoms. However, the high dimensionality of hyperspectral data makes it very important to have an efficient analysis pipeline in place for the identification of disease so that effective crop management decisions can be made. The focus of this work is to determine the minimal number of most effective hyperspectral wavebands that can distinguish between healthy and diseased soybean stem specimens early on in the growing season for proper management of the disease. 111 hyperspectral data cubes representing healthy and infected stems were captured at 3, 6, 9, 12, and 15 days after inoculation. We utilized inoculated and control specimens from 4 different genotypes. Each hyperspectral image was captured at 240 different wavelengths in the range of 383–1032 nm. We formulated the identification of best waveband combination from 240 wavebands as an optimization problem. We used a combination of genetic algorithm as an optimizer and support vector machines as a classifier for the identification of maximally-effective waveband combination.

          Results

          A binary classification between healthy and infected soybean stem samples using the selected six waveband combination (475.56, 548.91, 652.14, 516.31, 720.05, 915.64 nm) obtained a classification accuracy of 97% for the infected class. Furthermore, we achieved a classification accuracy of 90.91% for test samples from 3 days after inoculation using the selected six waveband combination.

          Conclusions

          The results demonstrated that these carefully-chosen wavebands are more informative than RGB images alone and enable early identification of charcoal rot infection in soybean. The selected wavebands could be used in a multispectral camera for remote identification of charcoal rot infection in soybean.

          Related collections

          Most cited references56

          • Record: found
          • Abstract: found
          • Article: not found
          Is Open Access

          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.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Plant Disease Detection by Imaging Sensors – Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging

                Bookmark

                Author and article information

                Contributors
                koushkn@iastate.edu
                sejones2@iastate.edu
                soumiks@iastate.edu
                singhak@iastate.edu
                arti@iastate.edu
                baskarg@iastate.edu
                Journal
                Plant Methods
                Plant Methods
                Plant Methods
                BioMed Central (London )
                1746-4811
                3 October 2018
                3 October 2018
                2018
                : 14
                : 86
                Affiliations
                [1 ]ISNI 0000 0004 1936 7312, GRID grid.34421.30, Department of Electrical and Computer Engineering, , Iowa State University, ; Ames, IA USA
                [2 ]ISNI 0000 0004 1936 7312, GRID grid.34421.30, Department of Agronomy, , Iowa State University, ; Ames, IA USA
                [3 ]ISNI 0000 0004 1936 7312, GRID grid.34421.30, Department of Mechanical Engineering, , Iowa State University, ; Ames, IA USA
                [4 ]ISNI 0000 0004 1936 7312, GRID grid.34421.30, Plant Sciences Institute, , Iowa State University, ; Ames, IA USA
                Article
                349
                10.1186/s13007-018-0349-9
                6169113
                29321806
                6352d3dc-6356-4570-8ac7-391554f87346
                © The Author(s) 2018

                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
                : 22 November 2017
                : 16 September 2018
                Funding
                Funded by: Iowa Soybean Association (US)
                Funded by: National Institute of Food and Agriculture (US)
                Funded by: ISU Plant Science Institute fellow
                Funded by: ISU Presidential grant
                Funded by: Monsanto Chair in Soybean breeding
                Categories
                Research
                Custom metadata
                © The Author(s) 2018

                Plant science & Botany
                charcoal rot,soybean disease,precision agriculture,band selection,genetic algorithm,support vector machines,hyperspectral

                Comments

                Comment on this article