About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Innovations and Interdisciplinary Solutions for Underserved Areas. 4th EAI International Conference, InterSol 2020, Nairobi, Kenya, March 8-9, 2020, Proceedings

Research Article

A Multi-level Smart Monitoring System by Combining an E-Nose and Image Processing for Early Detection of FAW Pest in Agriculture

Download(Requires a free EAI acccount)
10 downloads
Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-030-51051-0_2,
        author={S\'{e}m\'{e}vo Arnaud R. M. Ahouandjinou and Manhougb\^{e} P. A. F. Kiki and Prince E. N. Amoussouga Badoussi and Kokou M. Assogba},
        title={A Multi-level Smart Monitoring System by Combining an E-Nose and Image Processing for Early Detection of FAW Pest in Agriculture},
        proceedings={Innovations and Interdisciplinary Solutions for Underserved Areas. 4th EAI International Conference, InterSol 2020, Nairobi, Kenya, March 8-9, 2020, Proceedings},
        proceedings_a={INTERSOL},
        year={2020},
        month={8},
        keywords={Smart farming Fall Armyworm (FAW) Multi-level monitoring Early detection E-nose Image processing},
        doi={10.1007/978-3-030-51051-0_2}
    }
    
  • Sèmèvo Arnaud R. M. Ahouandjinou
    Manhougbé P. A. F. Kiki
    Prince E. N. Amoussouga Badoussi
    Kokou M. Assogba
    Year: 2020
    A Multi-level Smart Monitoring System by Combining an E-Nose and Image Processing for Early Detection of FAW Pest in Agriculture
    INTERSOL
    Springer
    DOI: 10.1007/978-3-030-51051-0_2
Sèmèvo Arnaud R. M. Ahouandjinou1,*, Manhougbé P. A. F. Kiki2, Prince E. N. Amoussouga Badoussi1, Kokou M. Assogba2
  • 1: Institut de Formation et de Recherche en Informatique (IFRI)
  • 2: Ecole Polytechnique d’Abomey-Calavi, Université d’Abomey-Calavi (UAC), Laboratoire LETIA
*Contact email: ahou.arn@gmail.com

Abstract

Fall Armyworm whose scientific name is Spodoptera frugiperda is a pest which have a large destructive activity of cornfields in sub-Saharan Africa. Fall Armyworm is a pest causing significant economic harm in Africa. In this work, we proposed to develop a smart monitoring system through several level. Each level of the proposed monitoring system is used to control and to detect the pest early. The aim is therefore to develop a system for the early detection of fall armyworm, these eggs, larvae and its adult form on image in order to anticipate the damage it can cause and to prevent its proliferation. First of all, the proposed monitoring system is based on an e-nose to analyze the odors that are released in the environment by fall armyworm. Then, we use image processing techniques based on image segmentation to detect the presence of pest through the damage caused to the plants and leaves its environment. We offers through this work, a smart monitoring system for Early Detection of FAW (EDFaw) by combining an e-nose and the plant leaf image segmentation. Several experiments have been done to test the proposed system and the results of the image segmentation.

Keywords
Smart farming Fall Armyworm (FAW) Multi-level monitoring Early detection E-nose Image processing
Published
2020-08-06
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-51051-0_2
Copyright © 2020–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL