The Use of Micro Computers for the Identification of Plant-Parasitic Nematodes

  • Brian Boag
  • P. B. Topham
  • Derek J. F. Brown
  • P. Smith
Part of the Nato ASI Series book series (NSSA, volume 7)


Plant parasitic nematodes are a particularly suitable group of animals for the development of computers to assist in their identification. They are an important group of economic pests throughout the world and species identification is often a prerequisite if appropriate control measures are to be applied. Because of their small size they are almost invariably identified using a microscope in a laboratory where a micro computer can easily be accommodated. Most nematode species are identified using a number of morphometric and morphological characters, the former characters being particularly suitable for measurement and analysis by a computer.


Nematode Species Morphometric Character Weighting Character Reference File Visual Display Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Anderson, R.V., 1979. A supplemental key to species to Helicotylenchus Steiner, 1945 (Nematoda: Hoplolaimidae) described since 1972 and a description of H. oscephalus n. sp. Can. J. Zool., 57: 337–342.CrossRefGoogle Scholar
  2. Boag, B., 1981. Measuring nematodes using a digitising tablet and microcomputer. Syst. Parasitol., 2: 145–147.CrossRefGoogle Scholar
  3. Boag, B. & Jairajpuri, M.S., 1985. Helicotylenchus scoticus n. sp. and a conspectus of the genusHelicotylenchus Steiner, 1945 (Tylenchida: Nematoda). Syst. Parasitol., 7: 47–58.Google Scholar
  4. Boag, B. & Smith, P., 1980. Automated nematode identification. Proceedings of the XVth International Nematology Symposium of the European Society of Nematologists Bari 1980: 31–32.Google Scholar
  5. Boag, B. & Smith, P., 1983. Computer assisted identification of nematodes. Syst. Parasitol., 5: 235–239.CrossRefGoogle Scholar
  6. Boag, B. & Smith, P., 1984. Advances in the use of the computer as an aid to the identification of nematodes. Proceedings of the First International Congress of Nematology, Guelph 1984, p. 13.Google Scholar
  7. Boag, B., Smith, P. & Topham, P.B., 1986. The development of a national computer identification scheme for plant-parasitic nematodes. Revue Nématol., 19: 289–290.Google Scholar
  8. Boag, B., Topham, P.B., Smith, P. & Fong San Pin, G., 1988. Advances in computer identification of nematodes. Nematologica, 34 (in press).Google Scholar
  9. Cohn, E. & Sher, 1972. A contribution to the taxonomy of the genus xiphinema Cobb, 193. J. Nematol., 4: 36–65.Google Scholar
  10. Fortuner, R., 1979. Morphological variability inHelicotylenchus Steiner, 1945. I. The progeny of a single female. Revue Nématol., 2: 197–202.Google Scholar
  11. Fortuner, R., 1983. Computer assisted semi-automatic identification of Helicotylenchus species. The program NEMAID. Calif. PI. Pest Dis. Rept, 2: 45–48.Google Scholar
  12. Fortuner, R., 1984a. Morphometric variability inHelicotylenchus Steiner, 1945. 6. Value of the characters used for species identification. Revue Nématol., 7: 245–264.Google Scholar
  13. Fortuner, R., 1984b. Statistics in taxonomie descriptions. Nematologica, 30: 187–192.CrossRefGoogle Scholar
  14. Fortuner, R., 1986a. A better assessment of variability of qualitative characters for the computer identification program NEMAID. Revue Nématol., 9: 277–279.Google Scholar
  15. Fortuner, R., 1986b. NEMAID available. Nematology Newsletter, 32: 22.Google Scholar
  16. Fortuner, R. & Quénéhervé, P., 1980. Morphometrical variability in Helicotylenchus Steiner, 1945. 2. Influence of the host on H. dihystera (Cobb, 1893) Sher, 1961. Revue Nématol., 3: 291–296.Google Scholar
  17. Fortuner, R., Maggenti, A.R. & Whittaker, L.M., 1984. Morphometrical variability in Helicotylenchus Steiner, 1945: 4. Study of field populations ofH. pseudorobustus and related species. Revue Nématol., 7: 121–135.Google Scholar
  18. Fortuner, R. & Wong, Y., 1984. Review of the genusHelicotylenchus Steiner, 1945. 1. A computer program for identification of the species. Revue Nématol., 7: 385–392.Google Scholar
  19. Gower, J.C., 1971. A general coefficient of similarity and some of its properties. Biometrics, 27: 857–871.CrossRefGoogle Scholar
  20. Gyllenberg, H., 1963. A general method for deriving determination schemes for random collections of microbial isolates. Annls Acad. Scient. Fenn. Ser. A, Biol., 69: 1–23.Google Scholar
  21. Hall, A.V., 1970. A computer-based system for forming identification keys, Taxon, 19: 12–18.CrossRefGoogle Scholar
  22. Hall, A.V., 1973. The use of a computer-based system for aids for classification. Contr. Bollus Herbarium, 6: 1–110.Google Scholar
  23. Hall, A.V., 1975. A system for automatic key forming. In: R.J. Pankhurst (Ed), Biological identification with computers, London, UK, Academic Press: 55–63.Google Scholar
  24. Lamark, J.B.P., 1778. Flore Frangaise, Paris, Imprimerie Royale.Google Scholar
  25. Luc, M. & Dalmasso, A., 1975. Considerations on the genus Xiphinema Cobb, 1913 (Nematoda: Longidoridae) and a “lattice” for the identification of species. Cah. ORSTOM, Sér. Biol., 10: 303–327.Google Scholar
  26. Morse, L.E., 1971. Specimen identification and key construction with timesharing computers. Taxon, 20: 269–282.CrossRefGoogle Scholar
  27. Pankhurst, R.J., 1970. Key generation by computer. Nature, London, 227: 1269–1270.CrossRefGoogle Scholar
  28. Pankhurst, R.J., 1971. Botanical keys generated by computer. Watsonia, 87: 357–368.Google Scholar
  29. Pankhurst, R.J., 1975.Biological identification with computers. The Systematics Association Special Volume No. 7. London, New York, San Francisco, Academic Press, 333 p.Google Scholar
  30. Pearson, E.S. & Hartley, H.O., 1976. Biometrika tables for statisticians, Vol. 1. London, Biometrika Trust, 270 p.Google Scholar
  31. Pinkham, C.F.L. & Pearson, J.G., 1976. Applications of a new coefficient of similarity to pollution surveys. Water Poll. Contr. Fed., 48: 717–723.Google Scholar
  32. Plackett, R., 1947. Limits to the ratio of mean range and standard deviation.Biometrika, 34: 120.PubMedGoogle Scholar
  33. Rey, J.M., 1987. Missing values in Gowers Index and its use in nematode identification. Proceedings of the 7th Congress of Mediterranean Phytopathology Union, Granada 1987, 96–98.Google Scholar
  34. Rey, J.M., Andres, M.Fe & Arias, M., 1988. A computer method for identifying nematode species. 1. Genus Longidorus (Nematoda: Longidoridae). Revue Nématol., 11: 129–135.Google Scholar
  35. Rey, J.M. & Mahajan, R., 1988. Computer programs for the identification of the genera Tylenchorhynchus andMerlinius. Revue Nématol. (in press).Google Scholar
  36. Siddiqi, M.R., 1972. On the genus Helicotylenchus Steiner, 1945 (Nematoda: Tylenchida), with descriptions of nine new species. Nematologica, 18: 7491.Google Scholar
  37. Smith, P. & Boag, B., 1982. Programs for computer-aided identification of nematodes. In: Scottish Crop Research Institute, First Annual Report for 1981: 56.Google Scholar
  38. Sneath, P.H.A., 1957. Some thoughts on bacterial classification. J. gen. Microbiol., 17: 184–200.PubMedCrossRefGoogle Scholar
  39. Sneath, P.H.A. & Sokal, R.R., 1973.Numerical Taxonomy. San Francisco, Freeman and Company, 575 p.Google Scholar
  40. Stone, A.R., 1984. Changing approaches in nematode taxonomy. PI. Dis., 68: 551–554.Google Scholar
  41. Webster, T., 1970. Developments in the description of potato varieties Part I — Foliage. J.Natn. Inst. Agric. Bot., 12: 455–475.Google Scholar

Copyright information

© Plenum Press, New York 1988

Authors and Affiliations

  • Brian Boag
    • 1
  • P. B. Topham
    • 1
  • Derek J. F. Brown
    • 1
  • P. Smith
    • 1
  1. 1.Zoology DepartmentScottish Crop Research InstituteInvergowrie, DundeeScotland, UK

Personalised recommendations