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Pest Detection Method Using Multi-Fractal Analysis

Yan LI, Chun-Lei XIA, Yun-Ki KIM, Jang-myung LEE

 

(School of Electrical Engineering, Pusan National University, Pusan 609-735, Korea)

 

Abstract-A novel method for pest detection is proposed based on the theory of multi-fractal spectrum to extract pests on plant leaves. Both local and global information of the image regularity were obtained by multi-fractal analysis. By applying fractal dimension, the spots on leaf images can be extracted without loosing or introducing any information. The different parts of images are re-analysis by morphology operations to determine the candidate regions of pests. The performance of multi-fractal analysis of whitefly detection is investigated through greenhouse experiments. The experimental results show that the proposed method is robust to noise from light and is not sensitive to the complex environment.

 

Key words-multi-fractal analysis; image segmentation; pest detection

 

Manuscript Number: 1674-8042(2011)03-0240-04

 

doi: 10.3969/j.issn.1674-8042.2011.03.009

 

References

 

[1] W.A.Allen, E.G.Rajotte, 1990. The changing role of extension entomology. Annual Review of Entomology, 35(1): 379-397.
[2] S.Tang, R.Chepe, 2008.Models for integrated pest control and their biological implications. Math.Biosci, 215(1): 115-125.
[3] Yan Li, Chunlei Xia, Jangmyung Lee, 2009. Vision-based pest detection and automatic spray of greenhouse plant. IEEE International Symposium on Industrial Electronics, p.5-8.
[4] Paul Boissard, Vincent Martin, Sabine Moisan, 2008. A cognitive vision approach to early pest detection in greenhouse crops. Computers and Electronics in Agriculture, 62: 81-93.
[5] Jongman Cho, Junghyeon Choi, Mu Qiao, et al, 2007. Automatic identification of whiteflies, aphids and thrips in greenhouse based on image analysis. International Journal of Mathematics and Computers in Simulation.
[6] A.N.Pavlov, V.S.Anishchenko, 2007. Multifractal analysis of complex signals. Methodological Notes, 819-834.
[7] J.P.Bouchaud, M.Potters, M.Meyer, 2000. Apparent multifractality in financial time series. The European Physical Journal B-Condensed Matter and Complex Systems, 13(3): 595-599.
[8] Stojic Tomislav, Reljin Irini, Branimir Reljin, 2006. Adaptation of multifractal analysis to segmentation of microcalcifications in digital mammograms. Physica A, 494-508.
[9] B.B.Mandelbrot, 1983. The Fractal Geometry of Nature. WH Freman, Oxford.
[10] J.Levy Vehel, P.Mignot, 1994.Multifractal segmentation of images. Fractals, p.379-382.
[11] B.B.Chaudhuri, N.Sarkar, 1996. Texture segmentation using fractal dimension. IEEE Trans.Geosci.Remote Sensing, 34: 906-914.
[12] G.Du, T.S.Yeo, 2002. A novel multifractal estimation method and its application to remote image segmentation. IEEE Trans.Geosci.Remote Sensing, 40: 980-982.
[13] H.Chen, W.Kinsner, 1997. Texture segmentation using multifractal measures. Proc.WESCANEX, p.222-227.
[14] Ratnesh Kumar,Vincent Martin, Sabine Moisan, 2010. Robust insect classification applied to real time greenhouse infestation monitoring. Visual observation and analysis of animal and insect behavior workshop at ICPR, Istanbul, Turkey.
 

 

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