Table of Contents Author Guidelines Submit a Manuscript
Journal of Electrical and Computer Engineering
Volume 2018, Article ID 6070129, 7 pages
https://doi.org/10.1155/2018/6070129
Research Article

Plant Diseases Recognition Based on Image Processing Technology

Nankai University, College of Electronic Information and Optical Engineering, Tianjin 300350, China

Correspondence should be addressed to Guiling Sun; nc.ude.iaknan@lgnus

Received 23 November 2017; Revised 12 March 2018; Accepted 10 April 2018; Published 21 May 2018

Academic Editor: Jose R. C. Piqueira

Copyright © 2018 Guiling Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. V. Pooja, R. Das, and V. Kanchana, “Identification of plant leaf diseases using image processing techniques,” in Proceedings of the 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), pp. 130–133, Chennai, April 2017. View at Publisher · View at Google Scholar
  2. P. L. Cheong and S. D. Morgera, “Iterative methods for restoring noisy images,” Institute of Electrical and Electronics Engineers Transactions on Acoustics, Speech and Signal Processing, vol. 37, no. 4, pp. 580–585, 1989. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. Y. S. Tang, D. H. Xia, G. Y. Zhang, L. N. Ge, and X. Y. Yan, “The detection method of lane line based on the improved Otsu threshold segmentation,” Applied Mechanics and Materials, vol. 741, pp. 354–358, 2015. View at Publisher · View at Google Scholar
  4. Y. Geng, Study on Crop Disease Diagnosis Based on Image Recognition, University of Science and Technology of China, Anhui, China, 2009.
  5. Q. Wang, Image Histogram Features And Its Application, University of Science and Technology of China, 2014.
  6. R. Pydipati, T. F. Burks, and W. S. Lee, “Identification of citrus disease using color texture features and discriminant analysis,” Computers and Electronics in Agriculture, vol. 52, no. 1-2, pp. 49–59, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Huang, “A novel method of stone surface texture image recognition,” in Proceedings of 2016 IEEE International Conference on Signal and Image Processing (ICSIP '16), IEEE, Beijing, China, 2016.
  8. F. Peng, D.-L. Zhou, M. Long, and X.-M. Sun, “Discrimination of natural images and computer generated graphics based on multi-fractal and regression analysis,” AEUE - International Journal of Electronics and Communications, vol. 71, pp. 72–81, 2017. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Riccardi, G. Mele, C. Pulvento, A. Lavini, R. D'Andria, and S.-E. Jacobsen, “Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components,” Photosynthesis Research, vol. 120, no. 3, pp. 263–272, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. F. Y. Shih and S. Cheng, “Automatic seeded region growing for color image segmentation,” Image and Vision Computing, vol. 23, no. 10, pp. 877–886, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Su, R. Han, L. Shi, and X. Qian, “Improved minimum distance discrimination method used in image analysis of fabric wear resistance,” Applied Mechanics and Materials, vol. 263-266, no. 1, pp. 421–426, 2013. View at Publisher · View at Google Scholar · View at Scopus