IDEAS home Printed from https://ideas.repec.org/a/hin/complx/5137317.html
   My bibliography  Save this article

A Distributed -Means Segmentation Algorithm Applied to Lobesia botrana Recognition

Author

Listed:
  • José García
  • Christopher Pope
  • Francisco Altimiras

Abstract

Early detection of Lobesia botrana is a primary issue for a proper control of this insect considered as the major pest in grapevine. In this article, we propose a novel method for L. botrana recognition using image data mining based on clustering segmentation with descriptors which consider gray scale values and gradient in each segment. This system allows a 95 percent of L. botrana recognition in non-fully controlled lighting, zoom, and orientation environments. Our image capture application is currently implemented in a mobile application and subsequent segmentation processing is done in the cloud.

Suggested Citation

  • José García & Christopher Pope & Francisco Altimiras, 2017. "A Distributed -Means Segmentation Algorithm Applied to Lobesia botrana Recognition," Complexity, Hindawi, vol. 2017, pages 1-14, August.
    • Handle: RePEc:hin:complx:5137317
    DOI: 10.1155/2017/5137317
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2017/5137317.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2017/5137317.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2017/5137317?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Clifford Lynch, 2008. "How do your data grow?," Nature, Nature, vol. 455(7209), pages 28-29, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. José García & Paola Moraga & Matias Valenzuela & Hernan Pinto, 2020. "A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem," Mathematics, MDPI, vol. 8(4), pages 1-22, April.
    2. José García & Victor Yepes & José V. Martí, 2020. "A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem," Mathematics, MDPI, vol. 8(4), pages 1-22, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Claudio Vitari & Elisabetta Raguseo, 2016. "Big data value and financial performance: an empirical investigation [Digital data, dynamic capability and financial performance: an empirical investigation in the era of Big Data]," Post-Print halshs-01923271, HAL.
    2. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
    3. Daas Piet J.H. & Buelens Bart & Hurk Paul A.M. van den & Puts Marco J., 2015. "Big Data as a Source for Official Statistics," Journal of Official Statistics, Sciendo, vol. 31(2), pages 249-262, June.
    4. Elisabetta Raguseo & Claudio Vitari, 2017. "Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects," Grenoble Ecole de Management (Post-Print) halshs-01923259, HAL.
    5. Perrons, Robert K. & McAuley, Derek, 2015. "The case for “n«all”: Why the Big Data revolution will probably happen differently in the mining sector," Resources Policy, Elsevier, vol. 46(P2), pages 234-238.
    6. Carbone, Anna & Jensen, Meiko & Sato, Aki-Hiro, 2016. "Challenges in data science: a complex systems perspective," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 1-7.
    7. Shenzhen Tian & Xueming Li & Jun Yang & Hui Wang & Jianke Guo, 2023. "Spatiotemporal evolution of pseudo human settlements: case study of 36 cities in the three provinces of Northeast China from 2011 to 2018," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(2), pages 1742-1772, February.
    8. Nitin Sachdeva & Ompal Singh & P. K. Kapur & Diego Galar, 2016. "Multi-criteria intuitionistic fuzzy group decision analysis with TOPSIS method for selecting appropriate cloud solution to manage big data projects," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(3), pages 316-324, September.
    9. Deepa Mishra & Angappa Gunasekaran & Thanos Papadopoulos & Stephen J. Childe, 2018. "Big Data and supply chain management: a review and bibliometric analysis," Annals of Operations Research, Springer, vol. 270(1), pages 313-336, November.
    10. Xue-ming Li & Zhi-zhen Bai & Shen-zhen Tian & Jun Yang & Yu-jie Guo, 2020. "Human Settlement Assessment in Jinan From a Facility Resource Perspective," SAGE Open, , vol. 10(2), pages 21582440209, June.
    11. Elisabetta Raguseo & Claudio Vitari, 2017. "Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects," Post-Print halshs-01923259, HAL.
    12. Rita Yi Man Li & Herru Ching Yu Li, 2018. "Have Housing Prices Gone with the Smelly Wind? Big Data Analysis on Landfill in Hong Kong," Sustainability, MDPI, vol. 10(2), pages 1-19, January.
    13. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.
    14. Muhammad Haseeb & Hafezali Iqbal Hussain & Beata Ślusarczyk & Kittisak Jermsittiparsert, 2019. "Industry 4.0: A Solution towards Technology Challenges of Sustainable Business Performance," Social Sciences, MDPI, vol. 8(5), pages 1-24, May.
    15. Yu, Yan & Ibarra, Julio E. & Kumar, Kuldeep & Chergarova, Vasilka, 2021. "Coevolution of cyberinfrastructure development and scientific progress," Technovation, Elsevier, vol. 100(C).
    16. Yasset Perez-Riverol & Max Kuhn & Juan Antonio Vizcaíno & Marc-Phillip Hitz & Enrique Audain, 2017. "Accurate and fast feature selection workflow for high-dimensional omics data," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-14, December.
    17. Maddalena Favaretto & David Shaw & Eva De Clercq & Tim Joda & Bernice Simone Elger, 2020. "Big Data and Digitalization in Dentistry: A Systematic Review of the Ethical Issues," IJERPH, MDPI, vol. 17(7), pages 1-15, April.
    18. Claudio Vitari & Elisabetta Raguseo, 2016. "Big data value and financial performance: an empirical investigation [Digital data, dynamic capability and financial performance: an empirical investigation in the era of Big Data]," Grenoble Ecole de Management (Post-Print) halshs-01923271, HAL.
    19. Xintian Wang & Hai Wang, 2019. "A Study on Sustaining Corporate Innovation with E-Commerce in China," Sustainability, MDPI, vol. 11(23), pages 1-16, November.
    20. Torrecilla, José L. & Romo, Juan, 2018. "Data learning from big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 15-19.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:5137317. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.