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Detection of Diseases and Volatile Discrimination of Plants: An Electronic Nose and Self-Organizing Maps Approach

Detection of Diseases and Volatile Discrimination of Plants: An Electronic Nose and Self-Organizing Maps Approach

Reza Ghaffari, Fu Zhang, D. D. Iliescu, Evor L. Hines, Mark S. Leeson, Richard Napier
Copyright: © 2011 |Pages: 17
ISBN13: 9781615209156|ISBN10: 1615209158|EISBN13: 9781615209163
DOI: 10.4018/978-1-61520-915-6.ch008
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MLA

Ghaffari, Reza, et al. "Detection of Diseases and Volatile Discrimination of Plants: An Electronic Nose and Self-Organizing Maps Approach." Intelligent Systems for Machine Olfaction: Tools and Methodologies, edited by Evor L. Hines and Mark S. Leeson, IGI Global, 2011, pp. 214-230. https://doi.org/10.4018/978-1-61520-915-6.ch008

APA

Ghaffari, R., Zhang, F., Iliescu, D. D., Hines, E. L., Leeson, M. S., & Napier, R. (2011). Detection of Diseases and Volatile Discrimination of Plants: An Electronic Nose and Self-Organizing Maps Approach. In E. Hines & M. Leeson (Eds.), Intelligent Systems for Machine Olfaction: Tools and Methodologies (pp. 214-230). IGI Global. https://doi.org/10.4018/978-1-61520-915-6.ch008

Chicago

Ghaffari, Reza, et al. "Detection of Diseases and Volatile Discrimination of Plants: An Electronic Nose and Self-Organizing Maps Approach." In Intelligent Systems for Machine Olfaction: Tools and Methodologies, edited by Evor L. Hines and Mark S. Leeson, 214-230. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-61520-915-6.ch008

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Abstract

The diagnosis of plant diseases is an important part of commercial greenhouse crop production and can enable continued disease and pest control. A plant subject to infection typically releases exclusive volatile organic compounds (VOCs) which may be detected by appropriate sensors. In this work, an Electronic Nose (EN) is employed as an alternative to Gas Chromatography - Mass Spectrometry (GC-MS) to sample the VOCs emitted by control and artificially infected tomato plants. A case study in which powdery mildew and spider mites may be present on tomato plants is considered. The data from the EN was analyzed and visualized using Fuzzy C-Mean Clustering (FCM) and Self-Organizing Maps (SOM). The VOC samples from healthy plants were successfully distinguished from the infected ones using the clustering techniques. This study suggests that the proposed methodology is promising for enhancing the automated detection of crop pests and diseases and may be an attractive tool to be deployed in horticultural settings.

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