The Comparative Analysis of The Filtering Schemes In Crop Disease Detection

Authors(2) :-Sampathkumar S, Rajeswari R

The rice plants are affected by various rice diseases such as rice impression, Bacterial scourge, Black kernel, Brown spot, Leaf smut, Seeding blight in different growth stages of the rice crops. The emerging trend in the agriculture is Computer Aided Detection (CAD) based on the digital imaging of the rice crops provides the detailed analysis about the crop disease by applying the image mining process. In order to detect the crop disease, CAD system applies the image segmentation and image mining process by acquiring the image of the crop during the transplantation stage. The captured image is applied for the noise removal and segmentation process to characterize the domain of interest from the crop image. The noise removal process is applied by using the Cross Central Filter which combines the features of Gaussian filter and Hybrid Median Filter operations. Core Fuzzy C-Means algorithm is applied to Image segmentation process for the object division. The Cross Central Filter produces the least Mean Square Error approximation value as of 4.71 and eminent Peak Signal to Noise Ratio value as 62.615 dB. Core Fuzzy C-Means achieves precision value as 95% and specificity as 96% values.

Authors and Affiliations

Sampathkumar S
Assistant Professor, Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, India
Rajeswari R
Assistant Professor (Senior Grade), Department of Electrical and Electronics Engineering, Government college of Technology, Coimbatore, India

Computer Aided Detection (CAD), Cross Central Filter, Core Fuzzy C-Means algorithm, CFCM, FCM

  1. Abhishek B. Mankar et al., 'Data Mining - An Evolutionary View of Agriculture', International Journal of Application or Innovation in Engineering & Management, Volume 3, Issue 3, March 2014.
  2. Boureau Y.L, Bach F, LeCun Y & Ponce J, 'Learning mid-level features for recognition' Conference on Computer Vision and Pattern Recognition, pages 2559-2566, 2010.
  3. Chen Y, Su W, Li J & Sun Z, 'Hierarchical object oriented classification using very high resolution imagery and lidar data over urban areas', Advances in Space Research 43(7):1101 - 1110, 2009.
  4. Faria F, Dos Santos J.A, Torres S Rocha A 2012, 'Automatic classifier fusion for produce recognition', In SIBGRAPI 2012, Ouro Preto MG, Brazil, August 2012.
  5. Gonzalez R.C Woods R.E, 'Digital Image Processing', Addison Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1992.
  6. Guigues L, Cocquerez J, Le Men H 2006, 'Scale-sets image analysis' International Journal of Computer Vision, 68:289-317,2006.
  7. Hill, G J et al 'Open and reconfigurable hypermedia systems: a filter-based model' Hypermedia 5(2), 103-118, 1993.
  8. Jie ZHANG1,2, Rujing WANG1,2, Chengjun XIE1, Rui LI1 , 'Crop Pests Image Recognition Based on Multi-features Fusion', June 2014.
  9. J M. Slingo, A. J. Challinor, B. J. Hoskins, and T. R. Wheeler, 'Introduction: Food crops in a changing climate,' Philos. Trains. Roy. Soc. Lond. B Biol. SCI., vol. 360, no. 1463, pp. 1983-1989, Nov. 2005.
  10. Zhiwei Jiang, Zhongxin Chen, Jin Chen, Jia Liu, Jianqiang Ren, Zongnan Li, Liang Sun, and He Li, 'Application of Crop Model Data Assimilation With a Particle Filter for Estimating Regional Winter Wheat Yields', March 2014.

Publication Details

Published in : Volume 4 | Issue 7 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 403-411
Manuscript Number : IJSRST184897
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Sampathkumar S, Rajeswari R, " The Comparative Analysis of The Filtering Schemes In Crop Disease Detection", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 4, Issue 7, pp.403-411, March-April-2018.
Journal URL :

Article Preview