Table of Contents Author Guidelines Submit a Manuscript
Journal of Optimization
Volume 2017, Article ID 4093973, 14 pages
Research Article

The Research of Disease Spots Extraction Based on Evolutionary Algorithm

1College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China
2School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China
3College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
4School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China

Correspondence should be addressed to Lu Xiong; moc.361@2280izul

Received 10 January 2017; Accepted 20 February 2017; Published 3 May 2017

Academic Editor: Maoguo Gong

Copyright © 2017 Kangshun Li 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.


According to the characteristics of maize disease spot performance in the image, this paper designs two-histogram segmentation method based on evolutionary algorithm, which combined with the analysis of image of maize diseases and insect pests, with full consideration of color and texture characteristic of the lesion of pests and diseases, the chroma and gray image, composed of two tuples to build a two-dimensional histogram, solves the problem of one-dimensional histograms that cannot be clearly divided into target and background bimodal distribution and improved the traditional two-dimensional histogram application in pest damage lesion extraction. The chromosome coding suitable for the characteristics of lesion image is designed based on second segmentation of the genetic algorithm Otsu. Determining initial population with analysis results of lesion image, parallel selection, optimal preservation strategy, and adaptive mutation operator are used to improve the search efficiency. Finally, by setting the fluctuation threshold, we continue to search for the best threshold in the range of fluctuations for implementation of global search and local search.