DETECTION OF DISEASES ON LEAVES AND ITS POSSIBLE DIAGNOSIS USING CBIR TECHNIQUE

Authors

  • Parash Mehta Department of Computer Engineering, Smt. Kashibai Navale College of Engineering
  • Lavanya Saoji Department of Computer Engineering, Smt. Kashibai Navale College of Engineering
  • Ankit Dodrajka Department of Computer Engineering, Smt. Kashibai Navale College of Engineering
  • Tushar Chug Department of Computer Engineering, Smt. Kashibai Navale College of Engineering

Keywords:

Image processing, farmer helping system, Smartphone, CBIR

Abstract

Image Retrieval has been an important achievement of technological advancement in today’s Age. In the process of retrieval, the features viz. colour, texture, entropy, densities are brought in a consideration for classification of images. The range of scope varies among various applications and proves to be useful for agricultural sector. This paper proposes the detection of diseases on the leaves of plants depicting radical changes over the surface (e.g. cotton plant). The difference between a normal cotton plant’s leaf and an infected one is visually forecasted based on size, texture, colour, and density. As previous Methodologies have shown lack of processing abilities to a large database, CBIR technique is proposed for handling of large operations. CBIR technique is provided out as a combination of content based retrieval, colour analysis and data-mining techniques. Thus, following information is used to segment leaf image into pixel format. The software analysis is further based upon the nature of image, producing respective results.

References

] RitendraDatta, Dhiraj Joshi, Jia Li and James Wang, “Image Retrieval: Ideas, Influences, and Trends of the New Age”, Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval, November 10-11, 2005, Hilton, Singapore.

] C. Carson, S. Belongie, H. Greenspan, and J. Malik, “Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying,” in IEEE Trans. On PAMI, vol. 24, No.8, pp. 1026-1038, 2002.

] Y. Chen and J. Z. Wang, “A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval,” in IEEE Trans. on PAMI, vol. 24, No.9, pp. 1252-1267, 2002.

] Natsev, R. Rastogi, and K. Shim, “WALRUS: A Similarity Retrieval Algorithm for Image Databases,” in Proc. ACM SIGMOD Int. Conf. Management of Data, pp. 395–406, 1999.

] J. Li, J.Z. Wang, and G. Wiederhold, “IRM: Integrated Region Matching for Image Retrieval,” in Proc. of the 8th ACM Int. Conf. on Multimedia, pp. 147-156, Oct. 2000.

] V. Mezaris, I. Kompatsiaris, and M. G. Strintzis, “Region-based Image Retrieval Using an Object Ontology and Relevance Feedback,” in Eurasip Journal on Applied Signal Processing, vol. 2004, No. 6, pp. 886-901, 2004.

] W.Y. Ma and B.S. Manjunath, “NETRA: A Toolbox for Navigating Large Image Databases,” in Proc. IEEE Int. Conf. on Image Processing, vol. I, Santa Barbara, CA, pp. 568–571, Oct. 1997.

] W. Niblacket al., “The QBIC Project: Querying Images by Content Using Color, Texture, and Shape,” in Proc. SPIE, vol. 1908, San Jose, CA, pp. 173–187, Feb. 1993.

] Pentland, R. Picard, and S. Sclaroff, “Photobook: Content-based Manipulation of ImageDatabases,” in Proc. SPIE Storage and Retrieval for Image and Video Databases II, SanJose, CA, pp. 34–47, Feb. 1994.

] M. Stricker, and M. Orengo, “Similarity of Color Images,” in Proc. SPIE Storage and Retrieval for Image and Video Databases, pp. 381-392, Feb. 1995.

] DOI:10.2298/CSIS120122047C /A Novel Content Based Image Retrieval System using K-means/KNN with Feature Extraction Ray-I Chang 1, Shu-Yu Lin 1,2, Jan-Ming Ho 2, Chi-Wen Fann 2, and Yu-Chun Wang 2.

Additional Files

Published

15-02-2016

How to Cite

Parash Mehta, Lavanya Saoji, Ankit Dodrajka, & Tushar Chug. (2016). DETECTION OF DISEASES ON LEAVES AND ITS POSSIBLE DIAGNOSIS USING CBIR TECHNIQUE. International Education and Research Journal (IERJ), 2(2). Retrieved from https://ierj.in/journal/index.php/ierj/article/view/129