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An Image Combination Segmentation Method Based on Clustering Analysis and Edge Detection

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Published:21 February 2022Publication History

ABSTRACT

Considering that it is difficult to completely segment the spider mite image on the leaves of field crops from the leaf background, a combination segmentation method combining K-means clustering algorithm and Canny edge detection algorithm is proposed. This method first uses the K-means clustering algorithm to filter out most of the leaf background, then extracts the edge closed contour of the spider mite based on Canny edge detection, and implements the binarization segmentation of the spider mite image by algorithms such as seed filling and morphological opening operations. Experiments show that this method can achieve complete segmentation of spider mites images on leaves, which provides a new technique and method for spider mite pest analysis and insect number counting.

References

  1. Y. J. Li, Z. Y. Wang, G. H. Zhang, 2014. The effect of temperature on the growth and reproduction of the experimental population of eotetranychus kankitus ehara, Acta Ecologica Sinica, vol. 34, no. 4, pp. 862-868.Google ScholarGoogle Scholar
  2. J. Y. Gao, J. Guo, Z. R. Wang, 2012. Research on Pest Species and Occurrence Regularity of Main Pests in Dehong Lemon Garden, Yunnan, Acta Agriculturae Jiangxi, vol. 24, no. 6, pp. 70-73.Google ScholarGoogle Scholar
  3. H. Y. Kuang, L. S. Cheng. 1990. Study on distinguishing two similar species of Tetranychus cinnabarinus and Tetranychus urticae, Acta Entomologica Sinica, vol. 33, no. 1, pp. 109-116.Google ScholarGoogle Scholar
  4. R. J. Li, K. Y. Wang, X. Y. Jiang, 2005. Research progress in drug resistance of Tetranychus urticae, Journal of Shandong Agricultural University (Natural Science Edition), vol. 36, no. 4, pp. 637-639.Google ScholarGoogle Scholar
  5. P. C. Chen, J. H. Zhang, M. M. Li, 2007. Physiological changes and spectral characteristics of cotton leaves damaged by Tetranychus turkestani, Chinese Bulletin of Entomology, vol. 44, no. 1, pp. 61-64.Google ScholarGoogle Scholar
  6. H. L. Xiong, C. L. Wu. 2013. Image recognition of Eotetranychus Kankitus Ehara based on BP neural network, Hubei Agricultural Sciences, vol. 52, no. 23, pp. 5863-5865.Google ScholarGoogle Scholar
  7. D. Qiu, J. X. Li, L. T. Yang. 2014. Research on Jujube Red Spider Recognition Based on Neural Network, Electronic Science and Technology, vol. 27, no. 3, pp. 48-51.Google ScholarGoogle Scholar
  8. C. H. Wu, H. L. Xiong, Q. Wu. 2010. Matlab-based Edge Detection of Eotetranychus Kankitus Ehara, Microcomputer Information, vol. 26, no. 9, pp. 198-199.Google ScholarGoogle Scholar
  9. H. Zhang, G. C. Liu. 2014. Two-dimensional LWT wavelet lifting separation and recognition of field spider mite images, Bulletin of Science and Technology, vol. 30, no. 8, pp. 209-211.Google ScholarGoogle Scholar
  10. J. Wang, Z. Li. 2015. Tiansheng Hong, , “In-leaf Affected Area Identification from Hyper-spectral Image of Citrus Red Mite Infected Leaf”, Journal of Agricultural Mechanization Research, vol. 36, no. 7, pp. 18-22.Google ScholarGoogle Scholar
  11. H. Lan, X. Wang. 2013. Insect image segmentation method based on multiple linear regression, Computer Applications and Software, vol. 30, no. 7, pp. 193-195, 208.Google ScholarGoogle Scholar

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  • Published in

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    DMIP '21: Proceedings of the 2021 4th International Conference on Digital Medicine and Image Processing
    November 2021
    87 pages
    ISBN:9781450386487
    DOI:10.1145/3506651

    Copyright © 2021 ACM

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    Publication History

    • Published: 21 February 2022

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