Case Based Interpretation of Soil Chromatograms

  • Deepak Khemani
  • Minu Mary Joseph
  • Saritha Variganti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5239)

Abstract

This paper focuses on the application of CBR to soil analysis from chromatograms. The shape, size and colour of the chromatogram image are hypothesized to contain important information of the mineral content in the soil. Since chromotogram preparation is cheaper than chemical analysis the goal is to predict the nutrients from the chromatogram image features in the future rather than by direct chemical analysis. The method proposed in this paper will be new, as the current process of chemical analysis of soil is done manually, which is an expensive, time consuming and laborious process. This method of analysis will benefit farmers all across the globe, who are looking for innovative means to obtain their soil characteristics during the process of farming. In this application, the key assumption is that – similar chromatograms have similar soil properties. This paper focuses on the definition of similarity measure and determining the weight model for the feature set needed for the application.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pfeiffer, E.E.: Chromatography applied to quality testing, Biodynamic Literature, Wyoming, Island, pp. 1–44 (1984)Google Scholar
  2. 2.
    Perumal, K., Vatsala, T.M.: Utilisation of local alternative materials in cow horn manures (BD500): A case study on biodynamic vegetable cultivation. Journal of Biodynamic Agriculture - Australia 52, 16–21 (2002)Google Scholar
  3. 3.
    Bio Dynamic Association of India, http://www.biodynamics.in/chrom.htm
  4. 4.
    Saritha, V., Joseph, M.M., Das, S., Khemani, D.: Chromatogram Image Pre-Processing and Feature Extraction for Automatic Soil Analysis. In: Proceedings of the International Conference on Computing: Theory and Applications ICCTA 2007, Kolkata, India, March 5-7 (2007)Google Scholar
  5. 5.
    A tutorial on Color Based Segmentation using CIELAB Colorspace, http://www.mathwork-s.com/products/demos/image/color_seg_lab/ipexfabric.html
  6. 6.
    Lu, C., Chung, P., Chen, C.: Unsupervised Texture Segmentation Via Wavelet Transform. Pattern Recognition, 729–742 (1997)Google Scholar
  7. 7.
  8. 8.
    Saritha, V., Joseph, M.M., Khemani, D.: Similarity Measures for Colour Patterns, A technical report, http://aidb.cs.iitm.ernet.in/tech-reports.html
  9. 9.
    Lamontagne, L.: Textual CBR Authoring using Case Cohesion, in TCBR 2006 -Reasoning with Text. In: Proceedings of the ECCBR 2006 Workshops, pp. 33–43 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Deepak Khemani
    • 1
  • Minu Mary Joseph
    • 1
  • Saritha Variganti
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology MadrasChennaiIndia

Personalised recommendations