Pollen image classification using the classifynder system: Algorithm comparison and a case study on New Zealand honey

Advances in Experimental Medicine and Biology

Volumn 823, Issue , 2015, Pages 207-226

  Lagerstrom, Ryan ^a   Holt, Katherine ^b   Arzhaeva, Yulia ^a   Bischof, Leanne ^a   Haberle, Simon ^c   Hopf, Felicitas ^c   Lovell, David ^a  

^a CSIRO   (Australia)

^b MASSEY UNIVERSITY   (New Zealand)

^c AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

Automation; Image analysis; Machine learning; Palynology; Pollen; Statistical classification

Indexed keywords

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; CASE STUDY; CLASSIFICATION ALGORITHM; CLASSIFYNDER SYSTEM; COMPUTER MODEL; COMPUTER SYSTEM; DISCRIMINANT ANALYSIS; IMAGE ANALYSIS; INTERMETHOD COMPARISON; MICROSCOPY; NEW ZEALAND; POLLEN ANALYSIS; STATISTICAL MODEL; SUPPORT VECTOR MACHINE; TAXONOMIC IDENTIFICATION; ALGORITHM; ANALYSIS; ANGIOSPERM; BIOLOGICAL MODEL; BIOLOGY; CLASSIFICATION; COMPARATIVE STUDY; CYTOLOGY; HONEY; IMAGE PROCESSING; PLANT; POLLEN; PROCEDURES; REPRODUCIBILITY; SPECIES DIFFERENCE;

ALGORITHMS; ANGIOSPERMS; COMPUTATIONAL BIOLOGY; HONEY; IMAGE PROCESSING, COMPUTER-ASSISTED; MODELS, BIOLOGICAL; NEW ZEALAND; PLANTS; POLLEN; REPRODUCIBILITY OF RESULTS; SPECIES SPECIFICITY;

EID: 84913535745     PISSN: 00652598     EISSN: 22148019     Source Type: Book Series    
DOI: 10.1007/978-3-319-10984-8_12     Document Type: Article

References (33)

1. L. Breiman, J.H. Friedman, R.A. Olschen, C.J. Stone, Classification and Regression Trees(Chapman Hall, New York, 1999)
2. L. Breiman, Random forests. Mach. Learn. 45(1), 5-32 (2001)
3. V.M. Bryant, D.C. Mildenhall, Forensic palynology in the United States of America. Palynology14, 193-208 (1990)
4. C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20(3), 273-297 (1995)
5. C. Chun, E.A. Hendriks, R.P.W. Duin, J.H.C. Reiber, P.S. Hiemstra, L.A. de Weger, B. Stoel, Feasibility study on automated recognition of allergenic pollen: grass, birch and mugwort.Aerobiologia. 22, 275-284 (2006)
6. A. Cristofori, F. Cristofolini, E. Gottardini, Twenty years of aerobiological monitoring inTrentino (Italy): assessment and evaluation of airborne pollen variability. Prog. Phys. Geogr.Aerobiol. 26, 553-261 (2010)
7. R. Dell’Anna, P. Lazzeri, M. Frisanco, F. Monti, F.M. Campeggi, E. Gottardini, M. Bersani, Pollen discrimination and classification by Fourier transform infrared (FT-IR) microspectroscopyand machine learning. Anal. Bioanal. Chem. 394, 1443-1452 (2009)
8. M.P. De Sa-Otero, A.P. Gonzalez, M. Rodriguez-Damian, E. Cernadas, Computer-aidedidentification of allergenic species of Urticaceae pollen. Grana 43, 224-230 (2004)
9. R.A. Fisher, The use of multiple measurements in taxonomic problems. Ann. Eugen. 7(2), 179-188 (1936)
10. J.R. Flenley, The problem of pollen recognition, in Problems of Picture Interpretation, ed. byM.B. Clowes, J.P. Penny (CSIRO, Canberra, 1968), pp. 141-145
11. I. France, A.W.G. Duller, G.A.T. Duller, H.F. Lamb, A new approach to automated pollenanalysis. Quat. Sci. Rev. 19, 537-546 (2000)
12. M.H.M. Groot, R.G. Bogota, L.J. Lourens, H. Hooghiemstra, M. Vriend, J.C. Berrio, E. Tuenter, J. Van der Plicht, B. Van Geel, M. Ziegler, S.L.Weber, A. Betancourt, L. Contreras, S. Gaviria, C. Giraldo, N. Gonzalez, J.H.F. Jansen, M. Konert, D. Ortega, O. Rangel, G. Sarmiento, J. Vandenberghe, T. Van der Hammen, M. Van der Linden, W.Westerhoff, Ultrahighresolution pollen record from the northern Andes reveals rapid shifts in montane climateswithin the last two glacial cycles. Clim. Past 7, 299-316 (2011)
13. J.A. Hertz, A. Krogh, R.G. Palmer, An Introduction to the Theory of Neural Computing(Addison-Wesley, Reading, 1991)
14. K. Holt, G. Allen, R. Hodgson, S. Marsland, J. Flenley, Progress towards an automatedtrainable pollen location and classifier system for use in the palynology laboratory. Rev.Palaeobot. Palynol. 167, 175-183 (2011)
15. C.A. Hopping, Palynology and the oil industry. Rev. Palaeobot. Palynol. 2, 372-399 (1967)
16. S. Hu, D.L. Dilcher, D.M. Jarzen, D.W. Taylor, Early steps of angiosperm-pollinator coevolution.Proc. Natl. Acad. Sci. 105, 240-245 (2007)
17. G.D. Jones, V.M. Bryant, Melissopalynology in the United States: a review and critique.Palynology 16, 63-71 (1992)
18. S. Kawashima, B. Clot, T. Fujita, Y. Takahashi, K. Nakamura, An algorithm and a devicefor counting airborne pollen automatically using laser optics. Atmos. Environ. 41, 7987-7993(2007)
19. R. Lagerstrom, Y. Arzhaeva, L. Bischof, S. Haberle, F. Hopf, D. Lovell, A comparisonof classification algorithms within the classifynder pollen imaging system, in InternationalSymposium on Computational Models for Life Sciences, Sydney, Nov 2013, vol. 1559, pp. 250-259
20. P. Li, W.J. Treloar, J.R. Flenley, L. Empson, Towards automation of palynology 2: the use oftexture measures and neural network analysis for automated identification of optical images ofpollen grains. J. Quat. Sci. 19, 755-762 (2004)
21. L.J. Maher, Nomograms for computing 0.95 confidence limits of pollen data. Rev. Palaeobot.Palynol. 13, 85-93 (1971)
22. M. McGlone, J.Wilmshurst, Dating initialMaori environmental impact in New Zealand. Quat.Int. 59, 5-16 (1999)
23. D.C. Mildenhall, R. Tremain, Pollen analysis of New Zealand honey. Institute of Geologicaland Nuclear Sciences science report, 2009, pp. 6-19
24. K. Mitsumoto, K. Yabusaki, K. Kobayashi, H. Aoyagi, Development of a novel real-timepollen-sorting counter using species-specific pollen autofluoresence. Aerobiologia 26, 99-111(2010)
25. B.V. Odgaard, Fossil pollen as a record of past biodiversity. J. Biogeogr. 26, 5-16 (1999)
26. S. Petersen, V.M. Bryant, The study of pollen and its role in the honey market. Am. Bee J. 151, 591-594 (2011)
27. R Development Core Team, R: A Language and Environment for Statistical Computing (RFoundation for Statistical Computing, Vienna, 2010)
28. O. Ronneberger, E. Schultz, H. Burkhardt, Automated pollen recognition using 3D volumeimages from fluourescence microscopy. Aerobiologia 18, 107-115 (2002)
29. H. Seppa, K.D. Bennett, Quaternary pollen analysis: recent progress in palaeoecology andpalaeoclimatology. Prog. Phys. Geogr. 27, 548-579 (2003)
30. E.C. Stillman, J.R. Flenley, The needs and prospects for automation in palynology. Quat. Sci.Rev. 15, 1-5 (1996)
31. J. Stockmarr, Tablets with spores used in absolute pollen analysis. Pollen et Spores 13, 615-621(1971)
32. W. Von der Ohe, L. Persano Oddo, M.L. Piana, M. Morlot, P. Martin, Harmonized methods ofmelissopalynology. Apidologie 35, S18-S25 (2004)
33. Y. Zhang, D.W. Fountain, R.M. Hodgson, J.R. Flenley, S. Gunetileke, Towards automation ofpalynology 3: pollen pattern recognition using Gabor transforms and digital moments. J. Quat.Sci. 19, 763-768 (2004)