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The Analysis of Plant’s Organic Volatiles Compounds with Electronic Nose and Pattern Recognition Techniques
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Author(s): Reza Ghaffari (School of Engineering, University of Warwick, UK), Fu Zhang (School of Engineering, University of Warwick, UK), Daciana Iliescu (School of Engineering, University of Warwick, UK), Evor Hines (School of Engineering, University of Warwick, UK), Mark Leeson (School of Engineering, University of Warwick, UK)and Richard Napier (Warwick HRI, University of Warwick, UK)
Copyright: 2011
Pages: 12
Source title:
Applied Signal and Image Processing: Multidisciplinary Advancements
Source Author(s)/Editor(s): Rami Qahwaji (University of Bradford, UK), Roger Green (University of Warwick, UK)and Evor L. Hines (University of Warwick, UK)
DOI: 10.4018/978-1-60960-477-6.ch007
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Abstract
In this chapter, the authors introduce the principles of some of the most widely used supervised and unsupervised Pattern Recognition (PR) techniques and assess behaviour and performances. A dataset acquired from a set of experiments conducted at University of Warwick is employed to construct a case study in which the techniques will be applied. The chapter will also evaluate the integration of PR methods with an Electronic Nose (EN) device to develop and implement a plant diagnosis tool based on discriminating the Organic Volatile Compounds (VOC) released by plants when attacked by pest. The chapter concludes with a performance comparison and a brief discussion of how an appropriate PR technique can be coupled with an EN to produce a greenhouse plant pest and disease diagnosis system for day-to-day utilisation. Some consideration of further work is also presented.
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