Skip to main content
SpringerLink
Log in

Identification of Plant Textures in Agricultural Images by Principal Component Analysis

  • Conference paper
  • First Online:
Hybrid Artificial Intelligent Systems (HAIS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9648))

Included in the following conference series:

Abstract

In precision agriculture the extraction of green parts is a very important task. One of the biggest issues, when it comes to computer vision, is image segmentation, which has motivated the research conducted in this work. Our goal is the segmentation of vegetative and soil parts in the images. For this proposal a novel method of segmentation is defined in which different vegetation indices are calculated and through the reduction of components by principal component analysis (PCA) we obtain an enhanced greyscale image. Finally, by Otsu thresholding, we binarize the grayscale image isolating the green parts from the other elements in the image.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Davies, G., Casady, W., Massey, R.: Precision agriculture: an introduction [En línea] (1998). http://extension.missouri.edu/explorepdf/envqual/wq0450.pdf. Último acceso 25 11 2015

  2. Ahmed, F., Al-Mamun, H.A., Bari, A.H., Hossain, E., Kwan, P.: Classification of crops and weeds from digital images: a support vector machine approach. Crop Prot. 40, 98–104 (2012)

    Article  Google Scholar 

  3. Montalvo, M., Guerrero, J.M., Romeo, J., Emmi, L., Guijarro, M., Pajares, M.: Automatic expert system for weeds/crops identification in images from maize fields. Expert Syst. Appl. 40(1), 75–82 (2013)

    Article  Google Scholar 

  4. Onyango, C.M., Marchant, J.A.: Segmentation of row crop plants from weeds using colour and morphology. Comput. Electron. Agric. 39, 141–155 (2003)

    Article  Google Scholar 

  5. Woebbecke, D.M., Meyer, G.E., Von Bargen, K., Mortensen, D.A.: Shape features for identifying young weeds using image analysis. Trans. Am. Soc. Agric. Biol. Eng. 38(1), 271–281 (1995)

    Article  Google Scholar 

  6. Ribeiro, A., Fernández-Quintanilla, C., Barroso, J., García-Alegre, M.C.: Development of an image analysis system for estimation of weed. In: Proceedings of 5th European Conference on Precision Agriculture (5ECPA), pp. 169–174 (2005)

    Google Scholar 

  7. Meyer, G., Metha, T., Kocher, M., Mortensen, D., Samal, A.: Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Trans. ASAE 41(4), 1189–1197 (1998)

    Article  Google Scholar 

  8. Kataoka, T., Kaneko, T., Okamoto, H., Hata, S.: Crop growth estimation system using machine vision. In: The 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, vol. 2, pp. 1079–1083 (2003)

    Google Scholar 

  9. Neto, J.C.: A Combined Statistical–Soft Computing Approach for Classification and Mapping Weed Species in Minimum Tillage Systems. University of Nebraska, Lincoln (2004)

    Google Scholar 

  10. Hague, T., Tillet, N., Wheeler, H.: Automated crop and weed monitoring in widely spaced cereals. Precis. Agric. 1(1), 95–113 (2006)

    Google Scholar 

  11. Meyer, G.E., Camargo-Neto, J., Jones, D.D., Hindman, T.W.: Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images. Comput. Electron. Agric. 42, 161–180 (2004)

    Article  Google Scholar 

  12. Zheng, L., Shi, D., Zhang, J.: Segmentation of green vegetation of crop canopy images based on mean shift and Fisher linear discriminate. Pattern Recogn. Lett. (2010)

    Google Scholar 

  13. Zheng, L., Zhang, J., Wang, Q.: Mean-shift-based color segmentation of images containing green vegetation. Comput. Electron. Agric. 65, 93–98 (2009)

    Article  Google Scholar 

  14. Guijarro, M., Pajares, G., Riomoros, I., Herrera, P.J., Burgos-Artizzu, X.P., Ribeiro, A.: Automatic segmentation of relevant textures in agricultural images. Comput. Electron. Agric. 75(1), 75–83 (2011)

    Article  Google Scholar 

  15. Guerrero, J.M., Pajares, G., Montalvo, M., Romeo, J., Guijarro, M.: Support vector machines for crop/weeds identification in maize fields. Expert Syst. Appl. 39(12), 1149–1155 (2012)

    Article  Google Scholar 

  16. Sewell, M.: http://www.stats.org.uk [En línea]. http://www.stats.org.uk/pca. Último acceso 28 05 2015

  17. Panda, S.S., Hoogenboom, G., Paz, J.: Distinguishing blueberry bushes from mixed vegetation land use using high resolution satellite imagery and geospatial techniques. Comput. Electron. Agric. 67, 51–58 (2009)

    Article  Google Scholar 

  18. Hanh, N.T.T., Dien, T.N.: Application of Principal Component Analysis Technique to Multispectral Image LANDSAT for Monitoring Change of Agricultural Land in the North Delta, Vietnam. Asian Association on Remote Sensing (2013)

    Google Scholar 

  19. Wang, J., Han, Y., Fu, Z., Li, D., Chen, J., Wang, S.: Edge geometric measurement based principal component analysis in strawberry leaf images. In: Li, D., Chen, Y. (eds.) Computer and Computing Technologies in Agriculture VI, Part I. IFIP AICT, vol. 392, pp. 58–68. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  20. Otsu, N.: A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979)

    Article  Google Scholar 

  21. Anderson, T.W.: An Introduction to Multivariate Statistical Analysis. Wiley-Interscience, Hoboboken (2003)

    MATH  Google Scholar 

  22. Gée, C., Bossu, J., Jones, G., Truchetet, F.: Crop/weed discrimination in perspective agronomic images. Comput. Electron. Agric. 60, 49–59 (2008)

    Article  Google Scholar 

  23. Montalvo, M., Pajares, G., Guerrero, J.M., Romeo, J., Guijarro, M., Ribeiro, A., Ruz, J.J., Cruz, J.J.: Automatic detection of crops rows in maize fields with high weeds pressure. Expert Syst. Appl. 39, 11889–11897 (2012)

    Article  Google Scholar 

  24. Pérez, A.J., López, F., Benllock, J.V., Christensen, S.: Colour and shape analysis techniques for weed detection in cereal fields. Comput. Electron. Agric. 25, 197–212 (2000)

    Article  Google Scholar 

  25. MathWorks. Accelerating the pace of engineering and science [En línea]. http://www.mathworks.com. Último acceso 1 10 2015

Download references

Acknowledgments

The authors wish to acknowledge to the project AGL2014-52465-C4-3-R, supported by the Ministerio de Educación y Ciencia of Spain within the Plan Nacional de I + D + i, for its support and coordination work.

Author information

Authors and Affiliations

  1. Department of Software Engineering and Artificial Intelligence, School of Computer Science, University Complutense of Madrid, Madrid, Spain

    Martín Montalvo & José Miguel Guerrero

  2. Department of Computer Architecture and Automatic, School of Computer Science, University Complutense of Madrid, Madrid, Spain

    María Guijarro

  3. Centre for Automation and Robotics, (CSIC-UPM), Arganda del Rey, 28500, Madrid, Spain

    Ángela Ribeiro

Authors
  1. Martín Montalvo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. María Guijarro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José Miguel Guerrero
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ángela Ribeiro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Martín Montalvo .

Editor information

Editors and Affiliations

  1. Universidad Pablo de Olavide, Sevilla, Spain

    Francisco Martínez-Álvarez

  2. Universidad Pablo de Olavide, Sevilla, Spain

    Alicia Troncoso

  3. University of Salamanca, Salamanca, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Montalvo, M., Guijarro, M., Guerrero, J.M., Ribeiro, Á. (2016). Identification of Plant Textures in Agricultural Images by Principal Component Analysis. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-32034-2_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32033-5

  • Online ISBN: 978-3-319-32034-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation