WEIGHT ESTIMATION AND CLASSIFICATION OF MILLED RICE USING SUPPORT VECTOR MACHINES

Oliver C. Agustin, Byung-Joo Oh

Abstract

This paper presents a method for weight estimation and classification of milled rice kernels using support vector machines. Shape descriptors are used as input features for determining the grade factors based on physical shapes such as headrice, broken kernel, and brewer. Colour histogram is extracted from milled rice image to obtain 24 colour features in RGB and Cielab colour spaces. We built a support vector regression (SVR) model for estimating rice kernel weight and support vector classifier (SVC) for rice defectives. Results showed that in real data, the performance of SVR is better than linear regression (LR) with a mean square error (MSE), mean absolute error (MAE) and correlation coefficient of 78.35x10-3, 0.206 and 0.9943, respectively. In determining grade factors based on colour appearance (rice defectives), SVC outperforms the generalized regression neural network (GRNN) with an accuracy of 98.86%.

References

  1. Agustin, O. C. and B. J. Oh (2007). "Applications of Ward Network and GRNN for Corn Quality Classification." Journal of Korean Institute of Information Technology 5(4): 218-225.
  2. Agustin, O. C. and B. J. Oh (2008). Automatic Milled Rice Quality Analysis. The 2nd International Workshop on Network Assurance (NA 2008), Hainan Island, China, IEEE CS Proceedings.
  3. Chang, C. C. and C. J. Lin (2001). "LIBSVM: A library for support vector machines."
  4. Hsu, C.-W. and C.-J. Lin (2002). "A comparison of methods for multiclass support vector machines." IEEE Transactions on Neural Networks 13(2): 415- 425.
  5. NFA (2002). Philippine Grains Standardization Program, National Food Authority.
  6. Ni, B., M. R. Paulsen, et al. (1997). "Design of an automated corn kernel inspection system for machine vision." Transactions of the ASAE 40(2): 491-497.
  7. Scholkopf, B., A. J. Smola, et al. (2000). New Support Vector Algorithms, MIT Press. 12: 1207-1245.
  8. Smola, A. J. and B. Schölkopf (2004). "A tutorial on support vector regression." Statistics and Computing 14(3): 199-222.
  9. Timmermans, A. J. M. (1998). "Computer vision system for online sorting of pot plants based on learning techniques." Acta Horticulturae 421: 91-98.
  10. Vapnik, V. N. (2000). The Nature of Statistical Learning Theory, Springer.
  11. Visen, N. S., J. Paliwal, et al. (2004). "Image analysis of bulk grain samples using neural networks." Canadian Biosystems Engineering 46: 7.11-7.15.
  12. Yadav, B. K. and V. K. Jindal (2001). "Monitoring Milling Quality of Rice by Image Analysis." Computers and Electronics in Agriculture 33(1): 19- 33.
  13. Zapotocznya, P., M. Zielinskaa, et al. (2008). "Application of image analysis for the Varietal Classification of Barley: Morphological features." Journal of Cereal Science 48(1): 4-9.
  14. Zayas, I. Y., C. R. Martin, et al. (1996). "Wheat classification using image analysis and crush force parameters." Transactions of the ASAE 6(39 ): 2199- 2004.
Download


Paper Citation


in Harvard Style

Agustin O. and Oh B. (2009). WEIGHT ESTIMATION AND CLASSIFICATION OF MILLED RICE USING SUPPORT VECTOR MACHINES . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 377-380. DOI: 10.5220/0001755803770380


in Bibtex Style

@conference{visapp09,
author={Oliver C. Agustin and Byung-Joo Oh},
title={WEIGHT ESTIMATION AND CLASSIFICATION OF MILLED RICE USING SUPPORT VECTOR MACHINES},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={377-380},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001755803770380},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - WEIGHT ESTIMATION AND CLASSIFICATION OF MILLED RICE USING SUPPORT VECTOR MACHINES
SN - 978-989-8111-69-2
AU - Agustin O.
AU - Oh B.
PY - 2009
SP - 377
EP - 380
DO - 10.5220/0001755803770380