2,634
Views
22
CrossRef citations to date
0
Altmetric
Original Articles

Identifying Potato Varieties Using Machine Vision and Artificial Neural Networks

, , &
Pages 618-635 | Received 15 Nov 2014, Accepted 05 Apr 2015, Published online: 02 Dec 2015

References

  • Cooke, J.R. The Reasons for Variety Identification. In Identification of Food Grain Varieties Potato Research; Wrigley, C.W.; Ed.; AACC: Saint Paul, MN, USA, 1995a, 1–18.
  • Zapotoczny, P. Discrimination of Wheat Grain Varieties Using Image Analysis and Multidimensional Analysis Texture of Grain Mass. International Journal of Food Properties 2014, 17, 139–151.
  • Gebhardt, C.; Ballvora, A.; Walkenmeier, B.; Oberhagemann, P.; Schiller, K. Assessing Genetic Potential in Germplasm Collections of Crop Plants by Marker-Trait Association: A Case Study for Potatoes with Quantitative Variation of Resistance to Late Blight and Maturity Type. Molecular Breeding 1994, 13, 93–102.
  • Sosinski, B.; Douches, D.S. Using Polymerase Chain Reaction-Based DNA Amplification to Fingerprint North American Potato Cultivars. HortScience 1996, 31, 130–133.
  • McGregor, C.E.; Lambert, C.A.; Greyling, M.M.; Louw, J.H.; Warnich, L. A Comparative Assessment of DNA Fingerprinting Techniques (RAPD, ISSR, AFLP, and SSR) in Tetraploid Potato (Solanumtuberosum L.) Germplasm. Euphytica 2000, 113, 135–144.
  • Chakrabarti, S.K.; Pattanayak, D.; Sarmat, D.; Chimote, V.P.; Naik, P.S. Stability of RAPD Fingerprints in Potato: Effect of Source Tissue and Primers. Biologia Plantarum 2006, 50, 531–536.
  • Kumar, A.; Hirochika, H. Applications of Retrotransposons As Genetic Tools in Plant Biology. Trends in Plant Sciences 2001, 6, 127–134.
  • Smykal, P. Development of An Efficient Retrotransposon-Based Fingerprinting Method for Rapid Pea Variety Identification. Journal of Applied Genetics 2006, 47, 221–230.
  • Novakova, A.; Simacova, K.; Barta, J.; Curn, V. Potato Variety Identification by Molecular Markers Based on Retrotransposon Analyses. Plant Breed 2009, 45(1), 1–10.
  • Vivek Venkatesh, G.; Iqbal, S. Md.; Ganesan, D. Estimation of Volume and Mass of Axi-Symmetric Fruits Using Image Processing Technique 2015, 18, 608–626.
  • Sylla, C. Experimental Investigation of Human and Machine-Vision Arrangements in Inspection Tasks. Control Engineering Practice 2002, 10(3), 347–361.
  • Cruvinel, P.E.; Minatel, E.R. Image Processing in Automated Pattern Classification of Oranges; World Congress of Computers in Agriculture and Natural Resources: Iguacu Falls, Brazil, 2002.
  • Zapotoczny, P. Discrimination of Wheat Grain Varieties Using Image Analysis and Neural Networks. Part I. Single Kernel Texture. Journal of Cereal Science 2011, 54, 60–68.
  • Li, X.; Nie, P.; Qiu, Z.J.; He, Y. Using Wavelet Transform and Multi-Class Least Square Support Vector Machine in Multi-Spectral Imaging Classification of Chinese Famous Tea. Expert Systems with Application 2011, 38, 11149–11159.
  • Golpour, I.; Amiri Parian, J.; Amiri Ghayjan, R. Identification and Classification of Bulk Paddy, Brown, and White Rice Cultivars with Colour Features Extraction Using Image Analysis and Neural Network. Journal of Food Science 2014, 32(3), 280–287.
  • Castleman, K. Digital Image Processing; Prentice-Hall: Englewood Cliffs, NJ, 1996; 667 p.
  • Chen, X.; Xun, Y.; Li, W.; Zhang, J. Combining Discriminant Analysis and Neural Networks for Corn Variety Identification. Computer and Electronics in Agriculture 2010, 71, 48–53.
  • Li, J.; Tan, J.; Martz, F.A.; Haymann, H. Image Texture Features As Indicators of Beef Tenderness. Meat Science 1999, 53, 17–22.
  • Park, B.; Chen, Y.R. Co-Occurrence Matrix Texture Features of Multi-Spectral Images on Poultry Carcasses. Journal of Agricultural Engineering Research 2001, 78(2), 127–139.
  • Gonzalez, R.C.; Woods, R.E. Digital Image Processing; Pearson Education, Inc.: Upper Saddle River, NJ, 2008; 808–819.
  • Ying, Y.; Jing, H.; Tao, Y.; Zhang, N. Detecting Stem and Shape of Pears Using Fourier Transformation and An Artificial Neural Network. Information and Electrical Technologies Division of the ASAE 2002, 46(1), 157–162.
  • Kara, S.; Dirgenali, F. A System to Diagnose Atherosclerosis Via Wavelet Transforms, Principal Component Analysis and Artificial Neural Networks. Expert Systems with Applications 2007, 32, 632–640.
  • Duda, R.O.; Hart, E.P.; Stork, G.D. Pattern Classification; John Wiley & Sons, Inc.: New York, NY, 2001.

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.