Abstract
The image segmentation of plant diseases is one of the critical technical aspects of digital image processing technology for Disease Recognition. This paper proposes an improved pulse coupled neural network based on an improved genetic algorithm. An objective evaluation function is defined based on linear weighted function with maximum Shannon entropy and minimum cross-entropy. Through adaptive adjustment of crossover probability and mutation probability, we optimized the parameters of pulse coupled neural network based on the improve genetic algorithm. The improved network is used to segment the color images of Maize melanoma powder disease in RGB color subspaces. Then combined with the results by color image merger strategy, we can get the terminal results of target area. The experimental results show that this method could segment the disease regions better and set complexity parameters simplier.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Li, G., Ma, Z., Huang, C., Chi, Y., Wang, H.: Segmentation of color images of grape diseases using K-means clustering algorithm. Transactions of the Chinese Society of Agricultural Engineering 26(12), 32–37 (2010)
Kurugollu, F., Sankur, B., Harmanci, A.E.: Color image segmentation using histogram multithresholding and fusing. Image and Vison Computing 19, 915–928 (2001)
Papamarkos, N., Strouthopoulos, C., Andreadis, I.: Multithresholding of color and gray-level images through a neural network technique. Image and Vison Computing 18, 213–222 (2000)
Rechenberg, I.: Cybemetic solution path of an experimental problem. Roy Airer Estable, lib trans 1222 Hants, UK; Farnborough (1985)
Johnson, J.L., Padgett, M.L.: PCNN Models and Application. IEEE Trans. on Neural Networks 10(3), 480–498 (1999)
Ma, Y., Dai, R., Li, L.: Image segmentation of embryonic plant cell using pulse-coupled neural network. Chinese Science Bulletin 47(2), 167–172 (2002)
Ma, Y., Dai, R., Li, L.: Automated image segmentation using pulse coupled neural networks and imgae’s entropy. Journal of China Institute of Communications (1), 46–51 (2002)
Gu, X., Guo, S., Yu, D.: A new approach for image segmentation based on unit-linking PCNN. In: Proceeding of the 2002 International Conference on Machine Learning and Cybernetics, vol. 1, pp. 175–178 (2002)
Ma, Y., Qi, C.: Study of Automated PCNN System Based on Genetic Algorithm. Journal of System Simulation (3), 722–725 (2006)
Wen, C., Wang, S., Yu, H., Su, H.: Improved genetic resonance matching network learning algorithm. Journal of Huazhong University of Science and Technology (Natural Science Edition) 39(11), 47–50 (2011)
Tan, Y., Zhou, D., Zhao, D., Nie, R.: Color image segmentation and edge detection using Unit-Lingking PCNN and image entropy. Computer Engineering and Applications 41(12), 174–178 (2009)
Li, C.H., Lee, C.K.: Minimum cross-entropy thresholding. Pattern Recognition 26, 617–625 (1993)
Camargo, A., Smith, J.S.: An image processing based algorithm to automatically identify plant disease visual symptoms. Biosystems Engineering 102(1), 9–12 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Changji, W., Helong, Y. (2013). An Image Segmentation Method for Maize Disease Based on IGA-PCNN. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_72
Download citation
DOI: https://doi.org/10.1007/978-3-642-39065-4_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39064-7
Online ISBN: 978-3-642-39065-4
eBook Packages: Computer ScienceComputer Science (R0)