A survey on vision based techniques for detection and classification of fruit diseases

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Indian economy depends heavily on its agriculture system. Horticulture sector has a major share in Indian agrarian economy. Therefore for agriculture industry to grow, the effective growth and improved yield of fruits is necessary. Diseased fruit production lays down various sensitive issues that can create various crises including reduced exports. To tackle such situations farmers need manual monitoring of fruits in all the development phases till harvest. But manual monitoring may not always give satisfactory results, owing to the subjective nature of the process. In order to reduce this stress, the technological support for such monitoring of fruit diseases was introduced. Image processing is one of the widely accepted areas for fruit disease detection and classification. With accurate disease diagnosis, the proper control actions can be taken at appropriate time. This paper is intended to aid in the analysis of various techniques and methodologies used in fruit disease detection so far.

     

     


  • Keywords


    Image Processing; Image Segmentation; Image Acquisition; Training and Classification.

  • References


      [1] MrunmayeeDhakate and Ingole A.B, “Diagnosis of Pomegranate Plant Diseases using Neural Network”, Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), PP: 1-4, 16-19 Dec. 2015.

      [2] PranjaliB.Padol and Prof. Anjali A.Yadav, “SVM Classifier Based Grape Leaf Disease Detection”, Conference on Advances in Signal Processing (CASP), PP: 175 – 179, 9-11 June 2016.

      [3] P.Revathi and M.Hemalatha, “Classification of Cotton Leaf Spot Diseases Using Image Processing Edge Detection Techniques”, International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET), PP: 169 – 173, 13-14 Dec. 2012.

      [4] Jun Lu, Pengfei Wu, JiweiXue, Ming Qiu and Fan Peng, “Detecting Defects on Citrus Surface Based on Circularity Threshold Segmentation”, 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), PP: 1543 – 1547, 15-17 Aug. 2015.

      [5] Swati Dewliya and Pratibha Singh, “Detection and classification for apple fruit diseases using support vector machine and chain code”, International Research Journal of Engineering and Technology (IRJET), Volume: 02 Issue: 04-Aug-2015.

      [6] AshwaniAwate, DaminiDeshmankar, GayatriAmrutkar, UtkarshaBagul and SamdhanSonavane, “Fruit Disease Detection using Color, Texture Analysis and ANN”, International Conference on Green Computing and Internet of Things (ICGCIoT), PP: 970 – 975, 8-10 Oct. 2015.

      [7] Muhammad Thaqif bin Mohamad Azmi and Naimah Mat Isa, “Orchid Disease Detection Using Image Processing and Fuzzy Logic”, International Conference on Electrical, Electronics and System Engineering (ICEESE), PP: 37 – 42, 4-5 Dec. 2013.

      [8] Shiv Ram Dubey and Anand Singh Jalal, “Detection and Classification of Apple Fruit Diseases Using Complete Local Binary Patterns”, Third International Conference on Computer and Communication Technology (ICCCT), PP: 346-351, 23-25 Nov. 2012.

      [9] ManishaBhange and H.A. Hingoliwala, “Smart Farming: Pomegranate Disease Detection Using Image Processing”, Second International Symposium on Computer Vision and the Internet (VisionNet’15), PP 280-288, 22 Aug. 2015.

      [10] Mendoza, O.; Melin, Patricia; Licea, G., "A New Method for Edge Detection in Image Processing Using Interval Type-2 Fuzzy Logic," Granular Computing, 2007. GRC 2007. IEEE International Conference, PP: 151-151, 2-4 Nov. 2007.

      [11] Choudhary, G.K.; Dey, S., "Crack detection in concrete surfaces using image processing, fuzzy logic, and neural networks," Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference, and PP: 404-411, 18-20 Oct. 2012.

      [12] Nithiya, N.; Imtiaz, R., "Fuzzy logic-based implementation of color image processing techniques in FPGA," Information Communication and Embedded Systems (ICICES), 2013 International Conference, PP: 1114-1117, 21-22 Feb. 2013.

      [13] J. Blasco, N. Aleixos, J. Gomez, E. Molto, "Citrus sorting by identification of the most common defects using multispectral computer vision", Journal of Food Engineering. 2007, PP: 384- 393, 2007.

      [14] Wen Zhiyuan, Cao Leping, "Machine Identification of Disease and Insect Pest of Citrus Fruit" in Chinese Journal of Engineering Mathematics, Chinese with English abstract, PP: 41-646, 2012.

      [15] P. Chaudhary, A. K. Chaudhari, A. N. Cheeran, S. Godara, "Color Transform Based Approach for Disease Spot", International Journal of Computer Science and Telecommunications, PP: 65-70, June 2012.

      [16] Tejal Deshpande, SharmilaSengupta, and K.S.Raghuvanshi, “Grading identification of disease in pomegranate leaf and fruit”, International Journal of Computer Science and Information Technologies, PP: 4638-4645, August 2014.

      [17] TimoOjala, MattiPietikainen, and TopiMaenpaa, “Multiresolution grayscale and rotation invariant texture classification with local binary pattern”, IEEE Trans. On Pattern Analysis and Machine Intelligence, PP: 971-987, 2002.

      [18] Simon Haykins, “An Introduction to Artificial Neural Networks”, Pearson Publications, 2005.

      [19] PradnyaRavindraNarvekar, Mahesh ManikKumbhar, and S. N. Patil, “Grape leaf diseases detection analysis using sgdm matrix method”, International Journal of Innovative Research inComputer and Communication Engineering, PP: 287-291, March 2014.

      [20] DaeGwan Kim, Thomas F. Burks, and Duke M. BulanonJianwei feature analysis”, International Journal on Agriculture and Biological Engineering, PP: 41-50, Sept 2009.


 

View

Download

Article ID: 21144
 
DOI: 10.14419/ijet.v7i4.5.21144




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.