Plant pest and disease diagnosis using electronic nose and support vector machine approach

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

Plant pest and disease diagnosis using electronic nose and support vector machine approach. / Ghaffari, Reza; Laothawornkitkul, Jullada; Iliescu, Daciana et al.
In: Journal of Plant Diseases and Protection, Vol. 119, No. 5-6, 12.2012, p. 200-207.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Ghaffari, R, Laothawornkitkul, J, Iliescu, D, Hines, E, Leeson, M, Napier, R, Moore, JP , Paul, ND , Hewitt, N & Taylor, JE 2012, 'Plant pest and disease diagnosis using electronic nose and support vector machine approach', Journal of Plant Diseases and Protection, vol. 119, no. 5-6, pp. 200-207. <http://www.jpdp-online.com/>

APA

Ghaffari, R., Laothawornkitkul, J., Iliescu, D., Hines, E., Leeson, M., Napier, R., Moore, J. P., Paul, N. D., Hewitt, N., & Taylor, J. E. (2012). Plant pest and disease diagnosis using electronic nose and support vector machine approach. Journal of Plant Diseases and Protection, 119(5-6), 200-207. http://www.jpdp-online.com/

Vancouver

Ghaffari R, Laothawornkitkul J, Iliescu D, Hines E, Leeson M, Napier R et al. Plant pest and disease diagnosis using electronic nose and support vector machine approach. Journal of Plant Diseases and Protection. 2012 Dec;119(5-6):200-207.

Author

Ghaffari, Reza ; Laothawornkitkul, Jullada ; Iliescu, Daciana et al. / Plant pest and disease diagnosis using electronic nose and support vector machine approach. In: Journal of Plant Diseases and Protection. 2012 ; Vol. 119, No. 5-6. pp. 200-207.

Bibtex

@article{92d545c74a41419098f3c6bf4e8463e5,

title = "Plant pest and disease diagnosis using electronic nose and support vector machine approach",

abstract = "In this paper, we evaluate the use of an electronic nose (EN) containing 13 conducting polymer gas sensors to discriminate between patterns of volatile organic compounds (VOCs) emitted by plants. The VOC patterns examined were produced by tomato, cucumber and pepper plants under both healthy and infected or infested conditions. Leaves from the plants were subjected to mechanical damage or pest and disease attacks (i.e. spider mites infested or mildew infected) and others were judged against undamaged healthy leaves. Support vector machines (SVMs) with linear, polynomial and Gaussian radial basis function (RBF) kernels were used to process and classify the raw data collected. The SVM illustrated an ability to discriminate between different VOC patterns and hence was able to classify correctly the infected leaves using the EN data. The results indicate that the array of 13 EN gas sensors can discriminate among VOC patterns from undamaged and artificially damaged leaves of the three plant species. This study demonstrates the potential application of such an EN technology coupled with suitable pattern recognition and signal processing methods to be used as a real time pest and disease detection system in the greenhouse environment.",

keywords = "gas sensors, plant disease detection, plant pest detection, VOCs, wounded plants, GC-MS, CLASSIFICATION, DISCRIMINATION, RECOGNITION, PRODUCTS, SYSTEM, ODORS",

author = "Reza Ghaffari and Jullada Laothawornkitkul and Daciana Iliescu and Evor Hines and Mark Leeson and Richard Napier and Moore, {Jason P.} and Paul, {Nigel D.} and Nick Hewitt and Taylor, {Jane E.}",

year = "2012",

month = dec,

language = "English",

volume = "119",

pages = "200--207",

journal = "Journal of Plant Diseases and Protection",

issn = "1861-3829",

publisher = "Verlag Eugen Ulmer",

number = "5-6",

}

RIS

TY - JOUR

T1 - Plant pest and disease diagnosis using electronic nose and support vector machine approach

AU - Ghaffari, Reza

AU - Laothawornkitkul, Jullada

AU - Iliescu, Daciana

AU - Hines, Evor

AU - Leeson, Mark

AU - Napier, Richard

AU - Moore, Jason P.

AU - Paul, Nigel D.

AU - Hewitt, Nick

AU - Taylor, Jane E.

PY - 2012/12

Y1 - 2012/12

N2 - In this paper, we evaluate the use of an electronic nose (EN) containing 13 conducting polymer gas sensors to discriminate between patterns of volatile organic compounds (VOCs) emitted by plants. The VOC patterns examined were produced by tomato, cucumber and pepper plants under both healthy and infected or infested conditions. Leaves from the plants were subjected to mechanical damage or pest and disease attacks (i.e. spider mites infested or mildew infected) and others were judged against undamaged healthy leaves. Support vector machines (SVMs) with linear, polynomial and Gaussian radial basis function (RBF) kernels were used to process and classify the raw data collected. The SVM illustrated an ability to discriminate between different VOC patterns and hence was able to classify correctly the infected leaves using the EN data. The results indicate that the array of 13 EN gas sensors can discriminate among VOC patterns from undamaged and artificially damaged leaves of the three plant species. This study demonstrates the potential application of such an EN technology coupled with suitable pattern recognition and signal processing methods to be used as a real time pest and disease detection system in the greenhouse environment.

AB - In this paper, we evaluate the use of an electronic nose (EN) containing 13 conducting polymer gas sensors to discriminate between patterns of volatile organic compounds (VOCs) emitted by plants. The VOC patterns examined were produced by tomato, cucumber and pepper plants under both healthy and infected or infested conditions. Leaves from the plants were subjected to mechanical damage or pest and disease attacks (i.e. spider mites infested or mildew infected) and others were judged against undamaged healthy leaves. Support vector machines (SVMs) with linear, polynomial and Gaussian radial basis function (RBF) kernels were used to process and classify the raw data collected. The SVM illustrated an ability to discriminate between different VOC patterns and hence was able to classify correctly the infected leaves using the EN data. The results indicate that the array of 13 EN gas sensors can discriminate among VOC patterns from undamaged and artificially damaged leaves of the three plant species. This study demonstrates the potential application of such an EN technology coupled with suitable pattern recognition and signal processing methods to be used as a real time pest and disease detection system in the greenhouse environment.

KW - gas sensors

KW - plant disease detection

KW - plant pest detection

KW - VOCs

KW - wounded plants

KW - GC-MS

KW - CLASSIFICATION

KW - DISCRIMINATION

KW - RECOGNITION

KW - PRODUCTS

KW - SYSTEM

KW - ODORS

M3 - Journal article

VL - 119

SP - 200

EP - 207

JO - Journal of Plant Diseases and Protection

JF - Journal of Plant Diseases and Protection

SN - 1861-3829

IS - 5-6

ER -

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