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  A Survey on Different Image Processing Techniques for Pest Identification and Plant Disease Detection  
  Authors : Preetha Rajan; Radhakrishnan B
  Cite as:


Pest detection and identification of diseases in agricultural crops is essential to ensure good productivity. The productivity of plants will reduce due to diseases and presence of pests. Image processing can be used to identify the pests and thereby can reduce the use of pesticides. Image processing involves capturing the image and applying various preprocessing techniques and detect the pest in the image. By using the classifier we can classify the pests and plant diseases. This paper presents the study of various image processing techniques and applications for pest identification and plant disease detection.


Published In : IJCSN Journal Volume 5, Issue 1

Date of Publication : February 2016

Pages : 137-141

Figures :03

Tables : 01

Publication Link : A Survey on Different Image Processing Techniques for Pest Identification and Plant Disease Detection




Preetha Rajan : received her B. Tech (Computer Science & Engineering) from University of Kerala in 2009. She is currently pursuing her Masters in Computer Science & Engineering from University of Kerala. Her research interests include image processing.

Radhakrishnan B : is working as Asst. Professor in Computer Science department. He has more than 14 years experience in teaching and has published papers on data mining and image processing. His research interests include image processing, data mining, and image mining.








Image Processing


Feature Extraction


Pest Identification

Disease Detection

This paper addresses how the disease analysis and pest detection is possible in agriculture field. In this review different image processing techniques for pest detection and plant disease detection are studied. The image processing technique proved as an effective machine vision system for agriculture domain. In this paper various feature extraction techniques and classification techniques are compared. The SVM classification provides better result in the detection of pests in almost all the cases. It yields better result and execution time.










[1] Rupesh G. Mundada and Dr. V. V. Gohokar, "Detection and Classification of Pests in Greenhouse Using Image Processing", IOSR Journal of Electronics and Communication Engineering, Vol 5, Issue 6 (Mar- Apr) 2013, pp. 57-63. [2] Ganesh Bhadane, Sapana Sharma and Vijay B. Nerkar, "Early Pest Identification in Agricultural Crops using Image Processing Techniques", International Journal of Electrical, Electronics and Computer Engineering 2(2), 2013, pp. 77-82. [3] Gaurav Kandalkar, A.V.Deorankar and P.N.Chatur, "Identification of Agricultural Pests Using Radial Basis Function Neural Networks ", International Journal of Engineering Research and Applications, April 2014, pp.52-56. [4] Johnny L. Miranda, Bobby D. Gerardo and Bartolome T. Tanguilig III, "Pest Identification using Image Processing Techniques in Detecting Image Pattern through Neural Network", Advances in Computer and Electronics Technology, 2014. [5] Sushma R. Huddar, Swarna Gowri, Keerthana K., Vasanthi S. and Sudhir Rao Rupanagudi "Novel Algorithm for Segmentation and Automatic Identification of Pests on Plants using Image Processing ", Third International Conference Computing Communication & Networking Technologies(ICCCNT) , July 2012, pp. 26-28. [6] Johnny L. Miranda, Bobby D. Gerardo, and Bartolome T. Tanguilig III," Pest Detection and Extraction Using Image Processing Techniques", International Journal of Computer and Communication Engineering, Vol. 3, May 2014, pp. 189-192 [7] S. Arivazhagan, R. Newlin Shebiah, S. Ananthi and S. Vishnu Varthini, “Detection of unhealthy region of plant leaves and classification of plant leaf disease using texture features”, Agric Eng Int: CIGR Journal, Vol 15, March 2013, pp. 211-217 [8] Sabah Bashir and Navdeep Sharma, "Remote Area Plant Disease Detection Using Image Processing", IOSR Journal of Electronics and Communication Engineering,Vol 2, Issue 6 (Sep - Oct) 2013, pp. 31-34. [9] Prof. Sonal P. Patil and Ms. Rupali S.Zambre, "Classification of Cotton Leaf Spot Disease Using Support Vector Machine ", International Journal of Engineering Research and Applications, Vol. 4, Issue 5 ( Version 1), May 2014, pp.92-97 [10] Santanu Phadikar and Jaya Sil, “Rice disease identification using pattern recognition techniques” Proceedings of 11th International Conference On Computer and Information Technology, 2008, pp. 25- 27 [11] Alexander A. Doudkin, Alexander V. Inyutin, Albert I. Petrovsky, Maxim E. Vatkin "Three level neural network for data clusterization on images of infected crop field", Journal of research and applications in Agriculture Engineering , Vol.52(1),2007 [12] Pawan P. Warne and Dr. S. R. Ganorkar," Detection of Diseases on Cotton Leaves Using K-Mean Clustering Method", International Research Journal of Engineering and Technology, Vol 2, July 2015, pp. 425 - 431. [13] Kamaljot Singh Kailey and Gurjinder Singh Sahdra, "Content-Based Image Retrieval (CBIR) For Identifying Image Based Plant Disease ", International Journal of Computer Technology and Application, Vol 3 (3), pp. 1099-1104. [14] M. S. Prasad Babu and B. Srinivasa Rao, "Leaves Recognition Using Back propagation neural network - advice for pest and disease control on crops", India Kisan.Net:Expert Advisory System,2007. [15] Anand.H.Kulkarni and Ashwin Patil R.K."Applying image processing technique to detect plant diseases", International Journal of Modern Engineering Research, Vol.2, Issue.5, Sep-Oct. 2012 pp. 3661-3664. [16] Ajay A. Gurjar and Viraj A. Gulhane, "Disease Detection On Cotton Leaves by Eigenfeature Regularization and Extraction Technique " International Journal of Electronics, Communication & Soft Computing Science and Engineering, Volume 1, Issue 1. [17] Vincent Martin and Sabine Moisan, "Early Pest Detection in Greenhouses", Computer and Electronics in Agriculture journal, 2009. [18] P. Boissard, V. Martin and S. Mosain, "A Cognitive Vision Approach to Early Pest Detection in greenhouse crops", Computer and Electronics in Agriculture Journal, April 2008, pp. 83 -93.