Machine vision based detection of ageratum enation virus infection using light microscopic images of poppy plants cells

Namita Sengar; Ashish Srivastava; Malay Kishore Dutta

doi:10.1109/ICETCCT.2017.8280306

Machine vision based detection of ageratum enation virus infection using light microscopic images of poppy plants cells

Namita Sengar
, Ashish Srivastava
, Malay Kishore Dutta

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Viral diseases cause major loss in crop produce and cause economic loss in agriculture. Monitoring of plant health is a tedious task and also requires expert man power. In this paper an automatic framework is proposed for identification of Ageratum enation virus (AEV) infection in poppy plants by using light microscopic images of its stem. Statistical and texture based features for healthy and infected stem samples were studied and analyzed block wise for discrimination which make this method efficient and computationally cheap. Designed framework is tested on microscopic images and results are encouraging. The maximum accuracy of contributed methodology is 92%.

Original language	English (US)
Title of host publication	2017 International Conference on Emerging Trends in Computing and Communication Technologies, ICETCCT 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781538611470
DOIs	https://doi.org/10.1109/ICETCCT.2017.8280306
State	Published - Jul 2 2017
Externally published	Yes
Event	2017 International Conference on Emerging Trends in Computing and Communication Technologies, ICETCCT 2017 - Dehradun, Uttarakhand, India Duration: Nov 17 2017 → Nov 18 2017

Publication series

Name	2017 International Conference on Emerging Trends in Computing and Communication Technologies, ICETCCT 2017
Volume	2018-January

Conference

Conference	2017 International Conference on Emerging Trends in Computing and Communication Technologies, ICETCCT 2017
Country/Territory	India
City	Dehradun, Uttarakhand
Period	11/17/17 → 11/18/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

image processing
Light-microscopy
Poppy

Publisher link

10.1109/ICETCCT.2017.8280306

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Cite this

Sengar, N., Srivastava, A., & Dutta, M. K. (2017). Machine vision based detection of ageratum enation virus infection using light microscopic images of poppy plants cells. In 2017 International Conference on Emerging Trends in Computing and Communication Technologies, ICETCCT 2017 (pp. 1-4). (2017 International Conference on Emerging Trends in Computing and Communication Technologies, ICETCCT 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICETCCT.2017.8280306

Machine vision based detection of ageratum enation virus infection using light microscopic images of poppy plants cells. / Sengar, Namita; Srivastava, Ashish; Dutta, Malay Kishore.
2017 International Conference on Emerging Trends in Computing and Communication Technologies, ICETCCT 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-4 (2017 International Conference on Emerging Trends in Computing and Communication Technologies, ICETCCT 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sengar, N, Srivastava, A & Dutta, MK 2017, Machine vision based detection of ageratum enation virus infection using light microscopic images of poppy plants cells. in 2017 International Conference on Emerging Trends in Computing and Communication Technologies, ICETCCT 2017. 2017 International Conference on Emerging Trends in Computing and Communication Technologies, ICETCCT 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 2017 International Conference on Emerging Trends in Computing and Communication Technologies, ICETCCT 2017, Dehradun, Uttarakhand, India, 11/17/17. https://doi.org/10.1109/ICETCCT.2017.8280306

Sengar N, Srivastava A, Dutta MK. Machine vision based detection of ageratum enation virus infection using light microscopic images of poppy plants cells. In 2017 International Conference on Emerging Trends in Computing and Communication Technologies, ICETCCT 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-4. (2017 International Conference on Emerging Trends in Computing and Communication Technologies, ICETCCT 2017). doi: 10.1109/ICETCCT.2017.8280306

Sengar, Namita ; Srivastava, Ashish ; Dutta, Malay Kishore. / Machine vision based detection of ageratum enation virus infection using light microscopic images of poppy plants cells. 2017 International Conference on Emerging Trends in Computing and Communication Technologies, ICETCCT 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-4 (2017 International Conference on Emerging Trends in Computing and Communication Technologies, ICETCCT 2017).

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abstract = "Viral diseases cause major loss in crop produce and cause economic loss in agriculture. Monitoring of plant health is a tedious task and also requires expert man power. In this paper an automatic framework is proposed for identification of Ageratum enation virus (AEV) infection in poppy plants by using light microscopic images of its stem. Statistical and texture based features for healthy and infected stem samples were studied and analyzed block wise for discrimination which make this method efficient and computationally cheap. Designed framework is tested on microscopic images and results are encouraging. The maximum accuracy of contributed methodology is 92\%.",

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Machine vision based detection of ageratum enation virus infection using light microscopic images of poppy plants cells

Abstract

Publication series

Conference

Bibliographical note

Keywords

Publisher link

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