Total views : 257

Performance Evaluation of Optimal Parameters for Pest Image Segmentation using FCM and ACO


  • Department of ECE, GMR Institute of Technology, Visakapattinam - 532127, Andhra Pradesh, India
  • Department of ECE, Pondicherry Engineering College, Pillaichavadi - 605014, Pondicherry, India
  • Department of Mechanical Engineering, GMR Institute of Technology, Visakapattinam - 532127, Andhra Pradesh, India


In gift agricultural subject, pest identity causes enormous discount in both fine and quantity of crop cultivation. If you want to growth the production price of crop, the presence of tiny pests such as aphids, whiteflies, and spider mites which purpose leaf deformation is the main hassle. Consequently early pest detection is a primary project in agricultural field. This research specializes in photograph processing, sensible set of rules and computer technology to expand a new pest detection device that’s vital and imperious to crop cultivation. In existing system, the pest detection using fuzzy c-manner clustering address the problem of overlapping of gadgets can purpose for hiding the pest. As cluster length will increase the rims are brittle and compactness of the clusters gets altered. Therefore the prevailing FCM segmentation now not able to cope with the constituent parts of the picture implicitly. The proposed technique known as ant colony optimization method resolves this hassle, and offers the implicit pest segmentation. The segmentation approach uses the swarm intelligence approach based totally at the behavior of the ant colonies. Ant colony optimization became used to extract the area of the insect pest and also to attain the most suitable constant parameters of recognized insect pest. In the beginning the captured pix are processed for pre-processing. Then photo segmentation is done based totally on ACO to get pest target location. Later constant parameters are measured for pest image segmentation the use of ACO which include structural content, top signal to noise ratio, normalized correlation coefficient, and average difference and normalized absolute mistakes. The matlab simulation experiments demonstrate that this proposed ACO technique is extra powerful than fuzzy c-way clustering consequently it can phase the pest photograph higher.


Ant Colony Optimization, Consistency Measures, Early Pest detection, Fuzzy C-means.

Full Text:

 |  (PDF views: 184)


  • Vibhute A. Application image processing in agricultural survey. Int Journal of computer application. 2012; 52.
  • Landge PS, Patil SA. Automatic detection and classification of plant disease through image processing. International Journal of Advanced Research in Computer Science and Software Engineering. 2013 Jul; 3(7).
  • Al-Hiary H, Bani-Ahmad S, Reyalat M, Braik M, Al Rahamneh Z. Fast and accurate detection and classification of plant disease. International Journal of computer Application. 2011; 17(1):31–8. 0975-8887.
  • Patil JK, Kumar R. Advances in image processing for detection of plant diseases. Journal of Advanced Bioinformatics Applications and Research. 2011; 2(2):135–41. ISSN: 0976-2604.
  • Chunlei YL, Lee XJ. Vision-based pest detection and automatic spray of greenhouse plant. Pusan National University Intelligent Robot Lab. IEEE International Symposium on Industrial Electronics, (ISIE 2009); Seoul Olympic Parktel, Seoul, Korea. 2009.
  • Maxwell JC. A treatise on electricity and magnetism. 3rd ed. Oxford: Clarendon; 1892. p. 68–73.
  • Kaur J, Agrawal S. A methodology for the performance analysis of cluster based image segmentation. International Journal of Engineering Research and Applications. 2012 Mar-Apr; 2(2):664–7.
  • Harikumar R, Kumar BV, Karthick G, Sneddon IN. Performance analysis for quality measures using k means clustering and em models in segmentation of medical images. International Journal of Soft computing and Engineering. 2012 Jan; 1(6):74–80.
  • Kratochvil T, Simicek P. Utilization of MATLAB for picture quality evaluation. Czech Republic: Institute of Radio Electronics, Brno University of Technology; 2005.
  • Dorigo M, Manjezzo V, Colorni A. The ant system: Optimization by a colony of cooperating agents. IEEE Transaction on Systems, Man and Cybernetics B. 1996; 2692:29–41.
  • Ouadfel S, Batouche M. Ant colony system with local search for Markovrandom field image segmentation [C]. International Conference on Image Processing; 2003. p. 133–6.
  • Feng YJ. Ant colony cooperative optimization and its application in image segmentation [Dissertation of PhD]. Xi’an Jiaotong University, China; 2005.
  • Han YF, Shi PF. An improved ant colony algorithm for fuzzy clustering inimage segmentation. Neurocomputing. 2006; 70 (2007):665–71.
  • Tao WB, Jin H, Liu LM. Object segmentation using ant colony optimization algorithm and fuzzy entropy. Pattern Recognition Letters. 2007; 28:788–96.
  • Ma L, Wang KQ, Zhang D. A universal texture segmentation and representation scheme based on ant colony optimization for iris image processing. Computers and Mathematics with Applications. 2009; 57(2009):1862–8.
  • Susmita G, Megha K, Anindya H, Ashish G. Use of aggregation pheromone density for image segmentation. Pattern Recognition Letters. 2009; 30(2009):939–49.
  • Mou Y, Zhao Q. Application of simulated annealing algorithm in pest image segmentation. IEEE 2nd International Symposium on Computational Intelligence and Design; 2009. DOI: 10.1109/ISCID.2009.12.
  • Kavita, Chawla HS, Saini JS. Parametric comparison of ant colony optimization for edge detection problem. IJCEM International Journal of Computational Engineering and Management. 2011 Jul; 13. ISSN (Online): 2230-7893.
  • Dorigo M, Gambardella LM. Ant colony system: A cooperative learning approach to the traveling salesman problem. Proc of IEEE Trans on Evolutionary Computation. 1997; 53–66.
  • Laptik R, Navakauskas D. Application of ant colony optimization for image segmentation. Electronics and Electrical Engineering – Kaunas: Technologija. 2007; 8(80):13–8.
  • Fernandes C, Ramos V, Rosa AC. Self-regulatedartificial ant colonies on digital image habitats. International Journal of Lateral Computing. 2005; 1(2):1–8.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.