Advertisement

On-Site Testing: Moving Decision Making from the Lab to the Field

Chapter
Part of the Plant Pathology in the 21st Century book series (ICPP, volume 5)

Abstract

On-site testing is a term that is often used to describe two distinct activities, firstly detection is the initial locating of the pest or pathogen infected sample which in most instances is performed visually. The second activity is identification, usually this is achieved by sending suspected samples to a laboratory. In recent years there has been significant research activity in each of the areas to provide technological solutions to enable more rapid decision making. Of course it is not necessarily just inspection services who benefit from these techniques, they can be deployed throughout the farm to fork, agri-production chain by seed producers, growers, processors, pack-houses etc. to limit losses caused by pathogens and pests. How best to deploy detection methods however may provide a potential conundrum for policy makers and other stakeholders. Deploying simplified detection and identification methods remotely helps to speed up inspection and facilitates trade. However, without care this approach may risk a blinkered, targeted inspection approach and a ‘winding down’ of laboratory expertise which is needed during outbreaks of new pests.

Keywords

Field-testing Detection LAMP Acoustics Volatiles Remote imaging Inspection 

Notes

Acknowledgments

We acknowledge funding from Framework 7 of the European Union under grant agreement 245047 for the project Q-detect: Developing quarantine pest detection methods for use by national plant protection organisations (NPPO) and inspection services. NB would in particular like to acknowledge helpful discussions during the course of the project with Maja Ravnikar, Juerg Frey, Andrea Battisti, Francoise Petter, Martin Brandstetter, Maja Zorovich, Cor Schoen, Hugh Mortimer, Jenny Tomlinson, Michael Andreou and Andreas Buhlmann.

References

  1. Andrea L, Joyce AL, Hunt RE, Bernal JS, Vinson SB (2008) Substrate influences mating success and transmission of courtship vibrations for the parasitoid. Cotesia marginiventris 127(1):39–47Google Scholar
  2. Apan A, Held A, Phinn S, Markley J (2004) Detecting sugarcane ‘orange rust’ disease using EO–1 Hyperion hyperspectral imagery. Int J Remote Sens 25(2):489–498CrossRefGoogle Scholar
  3. Augustin S, De Kogel WJ, Donner P, Faccoli M, Lees DC, Marini L, Mori N, Toffolo EP, Quilici S, Roques A, Yart A, Battisti A (2012a) A list of methods to detect quarantine arthropod pests in Europe. OEPP/EPPO Bull 42:93–94CrossRefGoogle Scholar
  4. Augustin S, Boonham N, De Kogel WJ, Donner P, Faccoli M, Lees DC, Marini L, Mori N, Petrucco Toffolo E, Quilici S, Roques A, Yart A, Battisti A (2012b) A review of pest surveillance techniques for detecting quarantine pests in Europe. EPPO Bull 42:515–551CrossRefGoogle Scholar
  5. Bawden FC (1933) Infra-red photography and plant virus diseases. Nature (Lond) 132:168CrossRefGoogle Scholar
  6. Boonham N, Glover R, Tomlinson J, Mumford R (2008) Exploiting generic platform technologies for the detection and identification of plant pathogens. Eur J Plant Pathol 121:355–363CrossRefGoogle Scholar
  7. Chamberlain K, Briens M, Jacobs JH, Clark SJ, Pickett JA (2012) Use of honey bees (Apis mellifera L.) to detect the presence of Mediterranean fruit fly (Ceratitis capitata Wiedemann) larvae in Valencia oranges. J Sci Food Agric 92(10):2050–2054PubMedCrossRefGoogle Scholar
  8. Chesmore D, Schofield J (2010) Acoustic detection of statutory pests in hardwood material. Bull Eur Plant Pathol Organ (EPPO) 40(1):46–51Google Scholar
  9. Chinellato F, Simonato M, Battisti A, Faccoli M, Hardwick S, Suckling DM (2013) Smart-traps combined with molecular on-site detection to monitor Monochamus spp. and associated pine wood nematode. In: Schröder T (ed) Pine wilt disease conference 2013, Braunschweig, pp 23–25. ISSN: 1866-590XGoogle Scholar
  10. Colebrook FM (1937) The aural detection of the larvae of insects in timber. J Sci Ins 14:119–121CrossRefGoogle Scholar
  11. Danks C, Barker I (2000) On-site detection of plant pathogens using lateral-flow devices. EPPO Bull/Bull OEPP 30(3–4):421–426CrossRefGoogle Scholar
  12. de Boer JG, Hordijk CA, Posthumus MA, Dicke M (2008) Prey and non-prey arthropods sharing a host plant: effects on induced volatile emission and predator attraction. J Chem Ecol 34(3):281–290PubMedCrossRefPubMedCentralGoogle Scholar
  13. EPPO (2010) PM 7/76 use of EPPO diagnostic protocols. EPPO Bull/Bull OEPP 40:350–352CrossRefGoogle Scholar
  14. Franke J, Menz G (2007) Multi-temporal wheat disease detection by multi-spectral remote sensing. Prec Agric 8(3):161–172CrossRefGoogle Scholar
  15. Gandelman O, Jackson R, Kiddle G, Tisi L (2011) Loop-mediated amplification accelerated by stem primers. Int J Mol Sci 12(12):9108–9124PubMedCrossRefPubMedCentralGoogle Scholar
  16. James HE, Ebert K, McGonigle R, Reid SM, Boonham N, Tomlinson JA, Hutchings GH, Denyer M, Oura CAL, Dukes JP, King DP (2010) Detection of African swine fever virus by loop-mediated isothermal amplification (LAMP). J Virol Methods 164(1–2):68–74PubMedCrossRefGoogle Scholar
  17. Jansen RMC, Hofstee JW, Wildt J, Verstappen FWA, Bouwmeester HJ, van Henten EJ (2009) Induced plant volatiles allow sensitive monitoring of plant health status in greenhouses. Plant Signal Behav 4(9):824–829PubMedCrossRefPubMedCentralGoogle Scholar
  18. Jansen RMC, Wildt J, Kappers IF, Bouwmeester HJ, Hofstee JW, van Henten EJ (2011) Detection of diseased plants by analysis of volatile organic compound emission. Ann Rev Phytopathol 49:157–174CrossRefGoogle Scholar
  19. Lelong CCD, Burger P, Jubelin G, Roux B, Labbé S, Baret F (2008) Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors 8(5):3557–3585CrossRefPubMedCentralGoogle Scholar
  20. Mankin RW, Smith MT, Tropp JM, Atkinson EB, Jong DY (2008) Detection of Anoplophora glabripennis (Coleoptera: Cerambycidae) larvae in different host trees and tissues by automated analyses of sound-impulse frequency and temporal patterns. J Econ Entomol 101(3):838–849PubMedCrossRefGoogle Scholar
  21. Miresmailli S, Gries R, Gries G, Zamar RH, Isman MB (2010) Herbivore-induced plant volatiles allow detection of Trichoplusia ni (Lepidoptera: Noctuidae) infestation on greenhouse tomato plants. Pest Manag Sci 66(8):916–924PubMedGoogle Scholar
  22. Notomi T, Okayama H, Masubuchi H, Yonekawa T, Watanabe K, Amino N, Hase T (2000) Loop-mediated isothermal amplification of DNA. Nucleic Acids Res 28(12):E63PubMedCrossRefPubMedCentralGoogle Scholar
  23. Potamitis I, Ganchev T, Fakotakis N (2008) Automatic bioacoustic detection of Rhynchophorus Ferrungineus. In: 16th European Signal Processing Conference (EU-SIPCO 2008), Lausanne, Switzerland, 25–29 Aug 2008Google Scholar
  24. Quilici S, Donner P, Battisti A (2012) Surveillance techniques for exotic insect pest detection. Bull OEPP/EPPO 42(1):95–101Google Scholar
  25. Schwarz, Kranz, Sicke (1935) A sound amplifier for the detection of infestations by the house beetle. Deutsche Bauzeitung 20:392–393Google Scholar
  26. Sharp EL, Perry CR, Scharen AL, Boatwright GO, Sands DC, Lautenschlager LF, Yahyaoui CM, Ravet FW (1985) Monitoring cereal rust development with a spectral radiometer. Phytopathology 75(8):936–939CrossRefGoogle Scholar
  27. Spinelli F, Cellini A, Vanneste JL, Rodriguez-Estrada MT, Costa G, Savioli S, Harren FJM, Cristescu SM (2012) Emission of volatile compounds by Erwinia amylovora: biological activity in vitro and possible exploitation for bacterial identification. Trees 26:141–152CrossRefGoogle Scholar
  28. Steddom K, Jones D, Rush C (2005) A picture is worth a thousand words. APSnet Feature. https://www.apsnet.org/publications/apsnetfeatures/Pages/RemoteSensing.aspx
  29. Tomlinson J, Boonham N (2008) Potential of LAMP for detection of plant pathogens. CAB Rev: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources 3(066):1–7CrossRefGoogle Scholar
  30. Tomlinson JA, Barker I, Boonham N (2007) Faster, simpler, more-specific methods for improved molecular detection of Phytophthora ramorum in the field. Appl Environ Microbiol 73(12):4040–4047PubMedCrossRefPubMedCentralGoogle Scholar
  31. Tomlinson JA, Dickinson M, Hobden E, Robinson S, Giltrap PM, Boonham N (2010a) A five-minute DNA extraction method for expedited detection of Phytophthora ramorum following prescreening using Phytophthora spp. lateral flow devices. J Microbiol Methods 81:116–120PubMedCrossRefGoogle Scholar
  32. Tomlinson JA, Dickinson MJ, Boonham N (2010b) Detection of Botrytis cinerea by loop-mediated isothermal amplification. Lett Appl Microbiol 51:650–657PubMedCrossRefGoogle Scholar
  33. Tomlinson J, Dickinson M, Boonham N (2010c) Rapid detection of Phytophthora ramorum and P kernoviae by two-minute DNA extraction followed by isothermal amplification, and amplicon detection by generic lateral flow device. Phytopathology 100(2):143–149PubMedCrossRefGoogle Scholar
  34. Torrance L (1987) Use of enzyme amplification in an ELISA to increase sensitivity of detection of barley yellow dwarf virus in oats and in individual vector aphids. J Virol Methods 15(2):131–138PubMedCrossRefGoogle Scholar
  35. Wallner WE, Ellis TL (1976) Olfactory detection of gypsy moth pheromone and egg masses by domestic canines. Environ Entomol 5:183–186Google Scholar
  36. Weißbecker B, Schütz S, Klein A, Hummel HE (1997) Analysis of volatiles emitted by potato plants by means of a Colorado beetle electroantennographic detector. Talanta 44(12):2217–2224PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Plant Protection ProgrammeFood and Environment Research AgencyYorkUK

Personalised recommendations