Flying Insect Classification with Inexpensive Sensors

Journal of Insect Behavior

Volumn 27, Issue 5, 2014, Pages 657-677

  Chen, Yanping ^a   Why, Adena ^b   Batista, Gustavo ^c   Mafra Neto, Agenor ^d   Keogh, Eamonn ^a  

^a University of California Riverside   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF SÃO PAULO   (Brazil)

^d ISCA Technologies   (United States)

Author keywords

Automate insect classification; Bayesian classifier; flight activity circadian rhythm; insect flight sound; insect wingbeat

Indexed keywords

HEXAPODA;

EID: 84905586897     PISSN: 08927553     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10905-014-9454-4     Document Type: Article

References (47)

1. Banko M, Brill E (2001) Mitigating the paucity-of-data problem: Exploring the effect of training corpus size on classifier performance for natural language processing. Proceedings of the first international conference on Human language technology research (pp. 1-5). Association for Computational Linguistics.
2. Batista GE, Keogh EJ, Mafra-Neto A, Rowton E (2011) SIGKDD demo: sensors and software to allow computational entomology, an emerging application of data mining. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 761-764.
3. Belton P, Costello RA (1979) Flight sounds of the females of some mosquitoes of Western Canada. Entomologia experimentalis et applicata 26(1): 105-114.
4. Benedict M, Robinson A (2003) The first releases of transgenic mosquitoes: an argument for the sterile insect technique. TRENDS in Parasitology 19(8): 349-355, Accessed March 8, 2012.
5. Boll S (1979) Suppression of acoustic noise in speech using spectral subtraction. Acoustics, Speech and Signal Processing, IEEE Transactions on, 27 (2), 113-120.
6. Capinera, JL (2008). Encyclopedia of entomology. Springer. Epsky ND, Morrill WL, Mankin R (2005) Traps for capturing insects. In Encyclopedia of Entomology, pp. 2319-2329. Springer Netherlands.
7. Chen Y (2013) Supporting Materials https://sites. google. com/site/insectclassification/.
8. Chen Y, Hu B, Keogh E, Batista GE (2013) DTW-D: time series semi-supervised learning from a single example. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 383-391.
9. Chen Y, Why A, Batista G, Mafra-Neto A, Keogh E (2014) supporting technique report http://arxiv. org/abs/1403. 2654.
10. Cooperband MF, Hartness A, Lelito JP, Cosse AA (2013) Landing surface color preferences of Spathius agrili (Hymenoptera: Braconidae), a parasitoid of emerald ash borer, Agrilus planipennis (Coleoptera: Buprestidae). J Insect Behav 26(5): 721-729.
11. Deakin MA (2010) Formulae for insect wingbeat frequency. Journal of Insect Science, 10 (96): 1.
12. Devroye L (1996) A probabilistic theory of pattern recognition. Springer Vol 31.
13. Elkan, C (2001) The foundations of cost-sensitive learning. In international joint conference on artificial intelligence, vol. 17, No. 1, pp. 973-978. LAWRENCE ERLBAUM ASSOCIATES LTD.
14. Ephraim Y, Malah D (1984) Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator. Acoustics, Speech and Signal Processing, IEEE Transactions on 32(6): 1109-1121.
15. Frick TB, Tallamy DW (1996) Density and diversity of non-target insects killed by suburban electric insect traps. Entomological News 107: 77-82.
16. Fukunaga K (1990) Introduction to statistical pattern recognition. Online via Elsevier, Access.
17. Georghiou GP, Wirth MC (1997) Influence of exposure to single versus multiple toxins of bacillus thuringiensis subsp. israelensis on Development of Resistance in Mosquito Culex quinquefasciatus (Diptera: Culicidae). Appl Environ Microbiol 63(3): 1095-1101.
18. Grimaldi RP (1989) Discrete and Combinatoral Mathematics: An Applied Introduction 2nd Ed. Addison-Wesley Longman Publishing Co., Inc.
19. Halevy A, Norvig P, Pereira F (2009) The unreasonable effectiveness of data. IEEE Intell Syst 24(2): 8-12.
20. Hao Y, Campana B, Keogh EJ (2012) Monitoring and mining animal sounds in visual space. J Insect Behav 1-28.
21. Kahn MC, Celestin W, Offenhauser W (1945) Recording of sounds produced by certain disease-carrying mosquitoes. Science 101: 335-336.
22. Kahn MC, Offenhauser W (1949) The identification of certain West African mosquitos by sound. Amer J trop IVied 29: 827-836.
23. Keogh E, Pazzani M (1999) Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches. In Proceedings of the seventh international workshop on artificial intelligence and statistics. pp. 225-230.
24. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In IJCAI (Vol. 14, No. 2, pp. 1137-1145.
25. Li Z, Zhou Z, Shen Z, Yao Q (2009) Automated identification of mosquito (diptera: Culicidae) wingbeat waveform by artificial neural network. Artificial Intell Applications and Innovations 187(2009): 483-489.
26. Mack YP, Rosenblatt M (1979) Multivariate k-nearest neighbor density estimates. J Multivar Anal 9(1): 1-15.
27. Mankin RW, Machan R, Jones R (2006) Field testing of a prototype acoustic device for detection of Mediterranean fruit flies flying into a trap. Proc. 7th Int. Symp. Fruit Flies of Economic Importance, pp. 10-15.
28. Moore A (1991) Artificial neural network trained to identify mosquitoes in flight. J Insect Behav 4(3): 391-396.
29. Moore A, Miller RH (2002) Automated identification of optically sensed aphid (Homoptera: Aphidae) wingbeat waveforms. Ann Entomol Soc Am 95: 1-8.
30. Moore A, Miller JR, Tabashnik BE, Gage SH (1986) Automated identification of flying insects by analysis of wingbeat frequencies. J Econ Entomol 79: 1703-1706.
31. Papathanos PA, Bossin HC, Benedict MQ, Catteruccia F, Malcolm CA, Alphey L, Crisanti A (2009) Sex separation strategies: past experience and new approaches. Malar J 8(Suppl 2).
32. Prechelt L (1995) A quantitative study of neural network learning algorithm evaluation practices. In proceedings of the 4th Int'l Conference on Artificial Neural Networks. pp. 223-227.
33. Raman DR, Gerhardt RR, Wilkerson JB (2007) Detecting insect flight sounds in the field: Implications for acoustical counting of mosquitoes. Trans ASABE 50(4): 1481.
34. Reed SC, Williams CM, Chadwick LE (1942) Frequency of wing-beat as a character for separating species races and geographic varieties of Drosophila. Genetics 27: 349-361.
35. Repasky KS, Shaw JA, Scheppele R, Melton C, Carsten JL, Spangler LH (2006) Optical detection of honeybees by use of wing-beat modulation of scattered laser light for locating explosives and land mines. Appl Opt 45: 1839-1843.
36. Rund SSC, Lee SJ, Bush BR, Duffield GE (2012) Strain- and sex-specific differences in daily flight activity and the circadian clock of Anopheles gambiae mosquitoes. J Insect Physiol 58: 1609-19.
37. Sawedal L, Hall R (1979) Flight tone as a taxonomic character in Chironomidae (Diptera). Entomol Scand Suppl 10: 139-143.
38. Schaefer GW, Bent GA (1984) An infra-red remote sensing system for the active detection and automatic determination of insect flight trajectories (IRADIT). Bull Entomol Res 74: 261-278.
39. Shotton J, Sharp T, Kipman A, Fitzgibbon A, Finocchio M, Blake A, Cook M, Moore R (2013) Real-time human pose recognition in parts from single depth images. Commun ACM 56(1): 116-124.
40. Sotavalta O (1947) The flight-tone (wing-stroke frequency) of insects (Contributions to the problem of insect flight 1.). Acta Entomol Fenn 4: 1-114.
41. Taylor B (1969) Geographical range and circadian rhythm. Nature 222: 296-297.
42. Tsymbal A (2004) The problem of concept drift: definitions and related work. Computer Science Department, Trinity College Dublin.
43. Unwin DM, Ellington CP (1979) An optical tachometer for measurement of the wing-beat frequency of free-flying insects. J Exp Biol 82(1): 377-378.
44. van Dam AR, Walton WE (2008) The effect of predatory fish exudates on the ovipositional behavior of three mosquito species: Culex quinquefasciatus, Aedes aegypti and Culex tarsalis. J Med Vet Entomol 22(4): 399-404.
45. Vapnik VN, Chervonenkis AY (1971) On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and Its Applications 16(2): 264-280.
46. Widmer G, Kubat M (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23(1): 69-101.
47. Zhan C, Lu X, Hou M, Zhou X (2005) A lvq-based neural network anti-spam email approach. ACM SIGOPS Oper Syst Rev 39 (1): 34-39 ISSN 0163-5980.