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

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 DOI: 10.4018/978-1-61520-915-6.ch008 Detection of Diseases and Volatile Discrimination of Plants 215 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. INTRODUCTION The main objectives of a rapid plant disease detection system are first to determine if the plant is unhealthy or stressed and second to find the reasons a plant is unhealthy which will lead to a proper diagnosis and treatments. Reliable disease detection is vitally important to assist disease and pest control within both large and medium scale commercial greenhouses for economic, production, and horticultural benefits. The process of plant disease diagnosis is a complex and multifaceted one but in recent years there have been diverse modifications of existing procedures for detection of plant pathogens and rapid advancement in research for new technologies and enhanced instrumentations. Visual inspections by growers as well as laboratory based methods such as Light Microscopy, Polymerase Chain Reaction (PCR), Serology, Double-Stranded RNA Analysis and Nucleic Acid probes are within the most widely used procedures of plants pest and disease diagnosis (Putnam, 1995). In spite of availability of these methods, the laboratory based techniques are often time consuming, expensive, require destructive sampling and necessitate skilled experts. Therefore, there is a demand for a fast and sensitive method for the rapid detection of plant diseases. More recent methods such as spectroscopic and imaging techniques as well as the application of analyzing the VOCs emitted by plant as possible biomarkers for disease detection have been studied by researchers (Laothawornkitkul et. al, 2008). These methods could be deployed in an agricultural setting for a rapid, reliable and real-time plant disease monitoring and management. The early detection of crop diseases prior the onset of disease symptoms and visual signs, may be a very valuable source of information for executing appropriate pest management strategies and disease control measures by growers to prevent the spread of diseases within the commercial greenhouses (Sankaran et al., 2010). In this chapter, we investigate plants behavior when infected by pest and disease and explore the possibility of employing gas sensor arrays coupled with suitable intelligent systems technique to categories the VOCs emitted by the plants. In next section we will debate the plant systematic approach in terms of emitting VOCs when under attacked by pest and diseases. 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. 15 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/chapter/detection-diseases-volatilediscrimination-plants/52454?camid=4v1 This title is available in InfoSci-Intelligent Technologies, InfoSci-Books, Science, Engineering, and Information Technology, InfoSci-Computer Science and Information Technology, InfoSci-Select, InfoSci-Select, InfoSci-Select. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=16

[1]  Antonella Macagnano,et al.  Electronic-nose modelling and data analysis using a self-organizing map , 1997 .

[2]  M. Haring,et al.  Jasmonic Acid Is a Key Regulator of Spider Mite-Induced Volatile Terpenoid and Methyl Salicylate Emission in Tomato1[w] , 2004, Plant Physiology.

[3]  D. Greenwood,et al.  A generic method to identify plant viruses by high-resolution tandem mass spectrometry of their coat proteins. , 2010, Journal of virological methods.

[4]  E. Gobbi,et al.  Early detection of microbial contamination in processed tomatoes by electronic nose. , 2009 .

[5]  D. Choudhary,et al.  Volatiles as priming agents that initiate plant growth and defence responses , 2008 .

[6]  Mark S. Leeson,et al.  Neural network based electronic nose for classification of tea aroma , 2008 .

[7]  N. Ancona,et al.  Support vector machines for olfactory signals recognition , 2003 .

[8]  Boudewijn P. F. Lelieveldt,et al.  A new cluster validity index for the fuzzy c-mean , 1998, Pattern Recognit. Lett..

[9]  Francisco de A. T. de Carvalho,et al.  Fuzzy c-means clustering methods for symbolic interval data , 2007, Pattern Recognit. Lett..

[10]  M. Putnam.  Evaluation of selected methods of plant disease diagnosis , 1995 .

[11]  M. Peris,et al.  A 21st century technique for food control: electronic noses. , 2009, Analytica chimica acta.

[12]  A. H. Gómez,et al.  Evaluation of tomato maturity by electronic nose , 2006 .

[13]  C. N. Hewitt,et al.  Discrimination of plant volatile signatures by an electronic nose: aA potential technology for plant pest and disease monitoring. , 2008, Environmental science & technology.

[14]  C. Deng,et al.  Investigation of Tomato Plant Defence Response to Tobacco Mosaic Virus by Determination of Methyl Salicylate with SPME-Capillary GC-MS , 2004, Chromatographia.

[15]  Andrew M. Taylor,et al.  Volatile compounds found in expired air during eating of fresh tomatoes and in the headspace above tomatoes , 1994 .

[16]  Ubonrat Siripatrawan,et al.  Self-organizing algorithm for classification of packaged fresh vegetable potentially contaminated with foodborne pathogens , 2008 .

[17]  Julian W. Gardner,et al.  Detection of Vapours and Odours from a Multisensor Array Using Pattern Recognition: Self-Organising Adaptive Resonance Techniques , 1996 .

[18]  J. Peñuelas,et al.  Plant VOC emissions: making use of the unavoidable. , 2004, Trends in ecology & evolution.

[19]  Sebastián Maldonado,et al.  Probabilistic support vector machines for multi-class alcohol identification , 2007 .

[20]  K. R. Kashwan,et al.  Tea quality prediction using a tin oxide-based electronic nose: an artificial intelligence approach , 2003 .

[21]  Reza Ehsani,et al.  Review: A review of advanced techniques for detecting plant diseases , 2010 .

[22]  J. Carlson,et al.  Plant Defense Priming against Herbivores: Getting Ready for a Different Battle1 , 2008, Plant Physiology.

[23]  M. Pardo,et al.  Classification of electronic nose data with support vector machines , 2005 .

[24]  Alisdair R Fernie,et al.  Application of GC-MS for the detection of lipophilic compounds in diverse plant tissues , 2009, Plant Methods.

[25]  L. Kiss.  Occurrence of a new powdery mildew fungus (Erysiphe sp.) on tomatoes in Hungary. , 1996 .

[26]  J. Kesselmeier,et al.  Biogenic Volatile Organic Compounds (VOC): An Overview on Emission, Physiology and Ecology , 1999 .

[27]  Evor L. Hines,et al.  Identification of Staphylococcus aureus infections in hospital environment: electronic nose based approach , 2005 .

[28]  Tomasz Markiewicz,et al.  Classification of milk by means of an electronic nose and SVM neural network , 2004 .

[29]  Petra M. Bleeker,et al.  Plant Volatiles in Defence. , 2009 .

[30]  Da-Wen Sun,et al.  Pizza sauce spread classification using colour vision and support vector machines , 2005 .

[31]  B. Armstrong,et al.  First report of Columbia Lance Nematode (Hoplolaimus columbus) on cotton in Alabama , 1994 .

[32]  T. V. van Beek,et al.  Qualitative and Quantitative Variation Among Volatile Profiles Induced by Tetranychus urticae Feeding on Plants from Various Families , 2004, Journal of Chemical Ecology.

[33]  Cosimo Distante,et al.  Drift counteraction with multiple self-organising maps for an electronic nose , 2004 .

[34]  J. Whipps,et al.  The tomato powdery mildew fungus Oidium neolycopersici. , 2001, Molecular plant pathology.

[35]  W. Jarvis,et al.  Occurrence of powdery mildew (Erysiphe sp.) on greenhouse tomatoes in Canada. , 1994 .

[36]  V. Gaudin,et al.  3D fluorescent in situ hybridization using Arabidopsis leaf cryosections and isolated nuclei , 2009, Plant Methods.

[37]  M. Dicke,et al.  Jasmonic Acid and Herbivory Differentially Induce Carnivore-Attracting Plant Volatiles in Lima Bean Plants , 1999, Journal of Chemical Ecology.

[38]  Maja Ravnikar,et al.  Real-time quantitative PCR based sensitive detection and genotype discrimination of Pepino mosaic virus. , 2009, Journal of virological methods.

[39]  Giorgio Sberveglieri,et al.  Monitoring plants health in greenhouse for space missions , 2005 .

[40]  C. Natale,et al.  Electronic nose systems to study shelf life and cultivar effect on tomato aroma profile , 2004 .

[41]  M. Maffei,et al.  Sites of synthesis, biochemistry and functional role of plant volatiles , 2010 .

[42]  J. Pereira,et al.  Headspace solid-phase microextraction and gas chromatography/ion trap-mass spectrometry applied to a living system: Pieris brassicae fed with kale , 2010 .

[43]  Ingemar Lundström,et al.  Classification of complex gas mixtures from automotive leather using an electronic nose , 2000 .