102
Views
3
CrossRef citations to date
0
Altmetric
Articles

A new method using feature extraction for identifying paddy rice species for quality seed selection

&
Pages 226-238 | Received 13 Feb 2016, Accepted 30 Mar 2017, Published online: 28 Apr 2017

References

  • Chaugule A, Mali SN. Evaluation of shape and color features for classification of four paddy varieties. Int. J. Image Graphics Signal Proces. 2014;12:32–38. doi: 10.5815/ijigsp.2014.12.05.
  • Chaugule AA, Mali SN. Performance comparison of second order statistics texture features for variety identification of paddy seeds. Int J Appl Pattern Recogn. 2015; 2:325–339.
  • Pourreza A, Pourreza H, Abbaspour-Fard M-H, et al. Identification of nine Iranian wheat seed varieties by textural analysis with image processing. Comput Electron Agric. 2012;83:102–108. doi: 10.1016/j.compag.2012.02.005
  • Mebatsion HK, Paliwal J, Jayas DS. Evaluation of variations in the shape of grain types using principal components analysis of the elliptic Fourier descriptors. Comput Electron Agric. 2012;80:63–70. doi: 10.1016/j.compag.2011.10.016
  • Mebatsion HK, Paliwal J, Jayas DS. Automatic classification of non-touching cereal grains in digital images using limited morphological and color features. Comput Electron Agric. 2013;90:99–105. doi: 10.1016/j.compag.2012.09.007
  • Huang K-Y. Detection and classification of areca nuts with machine vision. Comput Math Appl. 2012;64:739–746. doi: 10.1016/j.camwa.2011.11.041
  • Wiwart M, Suchowilska E, Lajszner W, et al. Identification of hybrids of spelt and wheat and their parental forms using shape and color descriptors. Comput Electron Agric. 2012;83:68–76. doi: 10.1016/j.compag.2012.01.015
  • Delwiche SR, Yang I-C, Graybosch RA. Multiple view image analysis of freefalling U.S. wheat grains for damage assessment. Comput Electron Agric. 2013;98:62–73. doi: 10.1016/j.compag.2013.07.002
  • Duda RO, Hart PE, Stork DG. Pattern classification, 2nd ed. Wiley Publishers India; 2000. ISBN: 978-81-265-116-7.
  • Liu H, Yu L. Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng. 2005;17(4):491–502. doi: 10.1109/TKDE.2005.66
  • Zhang C, Hu H. Ant colony optimization combining with mutual information for feature selection in support vector machines. Advances Artif Intell, Lecture Notes in Comput Sci. Springer, Berlin. 2005;3809:918–921. doi: 10.1007/11589990_110
  • Dash M, Choi K, Scheuermann P, et al. Feature selection for clustering – a filter solution. 2nd International Conference on Data Mining, Piscataway, NJ: IEEE; 2002 . p. 115–122.
  • Yu L, Liu H. Feature selection for high-dimensional data: a fast correlation-based filter solution. 20th International Conference on Machine Learning (ICML-2003), The International Machine Learning Society, Princeton; 2003 . p. 856–863.
  • Dy J, Brodley C. Feature subset selection and order identification for unsupervised learning. 17th International Conference Machine Learning, The International Machine Learning Society, Princeton; 2000 . p. 247–254.
  • Kohavi R, John G. Wrappers for feature subset selection. Artif Intell. 1997;97:273–324. doi: 10.1016/S0004-3702(97)00043-X
  • Kim Y, Street W, Menczer F. Feature selection for unsupervised learning via evolutionary search. ACMSIGKDD International Conference in Knowledge Discovery and Data Mining; 2000. p. 365–369.
  • Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: a gravitational search algorithm. Inform Sci. 2009;179:2232–2248. doi: 10.1016/j.ins.2009.03.004
  • Piatrik, T, Izquierdoe E. Subspace clustering of images using ant colony optimization. Proceedings of the International Conference on Image Processing, Nov. 7-10, Cairo: IEEE Xplore Press; 2009. p. 229–232.
  • Ye, Z, Xia B, Wang D, et al. Weight optimization of image retrieval based on particle swarm optimization algorithm. Proceedings of International Symposium on Computer Network and Multimedia Technology, Wuhan; 2009. p. 1–3.
  • Silva SFD, Ribeiro MX, Neto JDESB, et al. Traina, improving the ranking quality of medical image retrieval using a genetic feature selection method. Decis Support Syst. 2011;51:810–820. doi: 10.1016/j.dss.2011.01.015
  • Jiang Y, Ren J. Eigenvector sensitive feature selection for spectral clustering, Machine learning and knowledge discovery in databases. Lecture Notes Comput Sci. Springer, Berlin. 2011;6912:114–129. doi: 10.1007/978-3-642-23783-6_8
  • Rodrigues D, Pereira LAM, Souza AN, et al. Binary cuckoo search: a binary cuckoo search algorithm for feature selection. IEEE International Symposium on Circuits and Systems (ISCAS). 2013: 465–468. doi:10.1109/ISCAS.2013.6571881.
  • Jaganathan Y, Vennila I. An integrated gramework based on texture features, cuckoo search and relevance vector machine for medical image retrieval system. Am J Appl Sci. 2013 Science Publication. 2013;10:1398–1412. doi: 10.3844/ajassp.2013.1398.1412
  • Gupta SC, Kapoor VK. Fundamental of mathematical statistics. India: Sultan Chand and Sons, A.S. Printing Press; 1994.
  • Avcıbas I. Audio steganalysis with content-independent distortion measures. IEEE Signal Process Lett. 2006;13:92–95. doi: 10.1109/LSP.2005.862152
  • Stephens M, Smith NJ, Donnelly P. A new statistical method for haplotype reconstruction from population data. The Am J Human Genet. 2001;68:978–989. doi: 10.1086/319501
  • Surendiran B, Vadivel A. Feature selection using stepwise ANOVA discriminant analysis for mammogram mass classification. ACEEE Int J Sign Image Process. 2011;2:17–19.
  • Das AK, Sil J. An efficient classifier design integrating rough set and set oriented database operations. Appl Soft Comput. 2011;11:2279–2285. doi: 10.1016/j.asoc.2010.08.008
  • Dey P, Dey S, Datta S, et al. Dynamic disreduction using rough sets. Appl Soft Comput. 2011;11:3387–3897. doi: 10.1016/j.asoc.2011.01.015
  • Tiwari R, Singh MP. Correlation based attribute selection using genetic algorithm. Int J Comput Appl. 2010;4:28–34.
  • Agarwal PK, Dadlani, M. Techniques in seed science and technology. South Asian Publishers Pvt.Ltd; 1986. ISBN 81-7003-138-9, pp.24–34.
  • Manickavasagan A, Sathya G, Jayas DS. Comparison of illuminations to identify wheat classes using monochrome images. Comput Electron Agric. 2008;63:237–244. doi: 10.1016/j.compag.2008.03.002
  • Gonzalez RC, Woods RE, Eddins SL. Digital image processing using MATLAB. 2nd ed. New Delhi, India: Tata McGraw Hill Pvt. Ltd.; 2010.
  • Albregtsen F. Statistical texture measures computed from gray level coocurrence matrices. Norway: Image Processing Laboratory, Department of Informatics, University of Oslo; 2008. p. 1–14.
  • Tang X. Texture information in run-length matrices. IEEE Trans Image Process. 1998;7(11):1602–1609. doi: 10.1109/83.725367
  • Gupta SP. Statistical methods. India: Sultan Chand; 2009. p. 177–221.
  • Shenoy GV, Madan P. Statistical methods in business and social sciences. India: Macmillan; 2007. p. 161–185.
  • Chen X, Xun Y, Li W, et al. Combining discriminant analysis and neural networks for corn variety identification. Comput Electron Agric. 2010;71S:S48–S53. doi: 10.1016/j.compag.2009.09.003
  • Paliwal J, Visen NS, Jayas DS, et al. Comparison of a neural network and a non-parametric classifier for grain kernel identification. AE-Automat Emerg Technol, Biosyst Eng. 2003b;85(4):405–413. doi: 10.1016/S1537-5110(03)00083-7
  • Visen NS, Paliwal J, Jayas DS, et al. Specialist neural networks for cereal grain classification. Biosyst Eng, AE-Automat Emerg Technol. 2001;82(2):151–159. doi:10.1006/bioe.2002.0064.
  • Dubey BP, Bhagwat SG, Shouche SP, et al. Potential of artificial neural networks in varietal identification using morphometry of wheat grains. Biosyst Eng, PH – Postharvest Technol. 2006;95(1):61–67.
  • Paliwal J, Visen NS, Jayas DS. Evaluation of neural network architectures for cereal grain classification using morphological features. J Agric Eng Res. 2001;79(4):361–370. doi: 10.1006/jaer.2001.0724
  • Weishi C, Chunqing Z, Jinxing W, et al. Purity identification of maize seed based on discrete wavelet transform and BP neural network. Trans Chin Soc Agric Eng. 2012;28:253–258.
  • Jingtao J, Yanyao W, Ranbing R, et al. Variety identification of corn seed based on Bregman split method. Trans Chin Soc Agric Eng. 2012;28:248–252.
  • Jingbin L, Bingqi C, Luhao S, et al. Variety identification of delinted cottonseeds based on BP neural network. Trans Chin Soc Agric Eng. 2012;28:265–269.
  • Szczypiński PM, Zapotoczny P. Computer vision algorithm for barley kernel identification, orientation estimation and surface structure assessment. Comput Electron Agric. 2012;87:32–38. doi: 10.1016/j.compag.2012.05.014
  • Gunes EO, Aygun S, Kirchi M, et al. Determination of the varieties and characteristics of wheat seeds grown in turkey using image processing techniques; 2014 August 11–14. IEEE. doi:10.1109/Agro-Geoinformatics.2014.6910610.
  • Khunkhett S, Remsungnen T. Non-destructive identification of pure breeding rice seed using digital image analysis; 2014 March 5–8. IEEE. doi:10.1109/JICTEE.2014.6804096.
  • Auttawaitkul Y, Buochareon S, Maneechukate T, et al. Non-destructive identification of breeder rice seed using transparent image analysis; 2014 March 5–8. IEEE. doi:10.1109/JICTEE.2014.6804100.
  • Liu D, Ning X, Li Z, et al. Discriminating and elimination of damaged soybean seeds based on image characteristics. J Stored Prod Res. 2015;60:67–74. doi: 10.1016/j.jspr.2014.10.001
  • Jia S, An D, Liu Z, et al. Variety identification method of coated maize seeds based on near-infrared spectroscopy and chemometrics. J Cereal Sci. 2015;63:21–26. doi: 10.1016/j.jcs.2014.07.003

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.