Skip to main content
Book cover

Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012) pp 123–131Cite as

Automatic Agricultural Leaves Recognition System

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 201)

Abstract

India is an agricultural country where large number of human beings are involved in cropping different plants for their living. But these plants may be affected by different diseases which are to be handled by the farmers within time to increase their productivity. An automatic plant disease identification system can be helpful for the farmers to identify the disease and their cures within time. Most of these diseases can be identified using the leaves of the plants. Therefore, an automatic classification of leaves would be the prior step for disease identification system. The leaf recognition system is a complex task due to the presence of large variations in the leaves. Therefore, this paper proposes a novel methodology for classification of agricultural leaves into their respective class based on principal component analysis (PCA) and support vector machine (SVM). The results show that the proposed method is accurate and fast enough to classify the leaves.

Keywords

  • Principal component analysis
  • Support vector machine
  • Leaf segmentation
  • Leaf classification

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-81-322-1038-2_11
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   219.00
Price excludes VAT (USA)
  • ISBN: 978-81-322-1038-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   279.99
Price excludes VAT (USA)
Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Z. Wang, Z. Chi, D. Feng, and Q. Wang, “Leaf image retrieval with shape features”, in Advances in Visual Information Systems, ser. Lecture Notes in Computer Science, R. Laurini, Ed. Springer Berlin / Heidelberg, 2000, vol. 1929, pp. 41–52.

    Google Scholar 

  • Z. Wang, Z. Chi, D. Feng, “Shape based leaf image retrieval”, Vision, Image and, Signal Processing, 150(1), pp. 34–43, feb 2003.

    Google Scholar 

  • Y. Shen, C. Zhou, K. Lin, “Leaf image retrieval using a shape based method”, in Artificial Intelligence Applications and Innovations, ser. IFIP International Federation for Information Processing, D. Li and B. Wang, Eds. Springer, Boston, 2005, vol. 187, pp. 711–719.

    Google Scholar 

  • C.-L. Lee and S.-Y. Chen, “Classification of leaf images”, International Journal of Imaging Systems and Technology, vol. 16, no. 1, pp. 15–23, 2006.

    Google Scholar 

  • C. Caballero and M. C. Aranda, “Plant species identification using leaf image retrieval”, in Proc. of the ACM International Conference on Image and Video Retrieval. New York, NY, USA: ACM, 2010.

    Google Scholar 

  • N. Sakai, S. Yonekawa, A. Matsuzaki, and H. Morishima, “Two-dimensional image analysis of the shape of rice and its application to separating varieties”, Journal of Food Engineering, vol. 27, no. 4, pp. 397–407, 1996.

    Google Scholar 

  • A. R. Backes and O. M. Bruno, “Plant leaf identification using multi-scale fractal dimension”, in Proc. of the 15th International Conference on Image Analysis and Processing. Berlin, Heidelberg: Springer-Verlag, 2009, pp. 143–150.

    Google Scholar 

  • B. S. Bama, S. M. Valli, S. Raju, and V. A. Kumar, “Content based leaf image retrieval (cblir) using shape, color and texture features”, Indian Journal of Computer Science and Engineering, vol. 2, no. 2, pp. 202–211, 2011.

    Google Scholar 

  • N. Otsu, “A threshold selection method from gray-level histograms”, IEEE Transactions on Systems, Man and Cybernetics, vol. 9, no. 1, pp. 62–66, jan. 1979.

    Google Scholar 

  • T. Jolliffe, Principal Component Analysis, ser. Springer Series in Statistics. Springer New York, 2006.

    Google Scholar 

  • C. Cortes and V. Vapnik, “Support-vector networks”, in, Machine Learning, 1995, pp. 273–297.

    Google Scholar 

  • M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces”, in Proc. of IEEE Computer Society Conference on Computer Vision and, Pattern Recognition, jun 1991, pp. 586–591.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. BSA College of Engineering & Technology, Mathura, India

    Meenakshi & Durga Puja

  2. ABV- Indian Institute of Information Technology & Management, Gwalior, India

    Mukesh Saraswat & K. V. Arya

Authors
  1. Meenakshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Durga Puja
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mukesh Saraswat
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. K. V. Arya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meenakshi .

Editor information

Editors and Affiliations

  1. South Asian University, Chankya Puri, New Delhi, 110021, India

    Jagdish Chand Bansal

  2. ABV - IIITM, Gwalior, Gwalior, 474015, Madhya Pradesh, India

    Pramod Kumar Singh

  3. , Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Kusum Deep

  4. , Department of Paper Technology, Indian Institute of Technology Roorkee, Saharanpur Campus, Roorkee, India

    Millie Pant

  5. , Department of Computer Science, Liverpool Hope University, Office: FML 412, Liverpool, Liverpool, L16 9JD, United Kingdom

    Atulya K. Nagar

Rights and permissions

Reprints and Permissions

Copyright information

© 2013 Springer India

About this paper

Cite this paper

Meenakshi, Puja, D., Saraswat, M., Arya, K. (2013). Automatic Agricultural Leaves Recognition System. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 201. Springer, India. https://doi.org/10.1007/978-81-322-1038-2_11

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-1038-2_11

  • Published:

  • Publisher Name: Springer, India

  • Print ISBN: 978-81-322-1037-5

  • Online ISBN: 978-81-322-1038-2

  • eBook Packages: EngineeringEngineering (R0)