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The Scientific World Journal
Volume 2014, Article ID 509429, 10 pages
Review Article

Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction

1IMTA, Boulevard Cuauhnáhuac 8532, Colonia Progreso, 62550 Jiutepec, MOR, Mexico
2UPEMOR, Boulevard Cuauhnáhuac 566, Colonia Lomas del Texcal, 62550 Jiutepec, MOR, Mexico

Received 6 December 2013; Accepted 10 February 2014; Published 26 May 2014

Academic Editors: S. Balochian and Y. Zhang

Copyright © 2014 Alberto Gonzalez-Sanchez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. A. Raorane and R. V. Kulkarni, “Data mining: an effective tool for yield estimation in the agricultural sector,” International Journal of Emerging Trends of Technology in Computer Science, vol. 1, no. 2, pp. 75–79, 2012. View at Google Scholar
  2. B. Marinkovic, J. Crnobarac, S. Brdar, B. Antic, G. Jacimovic, and V. Crnojevic, “Data mining approach for predictive modeling of agricultural yield data,” in Proceedings of the 1st International Workshop on Sensing Technologies in Agriculture, Forestry and Environment (BioSense '09), pp. 1–5, Novi Sad, Serbia, October 2009.
  3. S. T. Drummond, K. A. Sudduth, A. Joshi, S. J. Birrell, and N. R. Kitchen, “Statistical and neural methods for site-specific yield prediction,” Transactions of the American Society of Agricultural Engineers, vol. 46, no. 1, pp. 5–14, 2003. View at Google Scholar · View at Scopus
  4. A. Irmak, J. W. Jones, W. D. Batchelor, S. Irmak, K. J. Boote, and J. O. Paz, “Artificial neural network model as a data analysis tool in precision farming,” Transactions of the ASABE, vol. 49, no. 6, pp. 2027–2037, 2006. View at Google Scholar · View at Scopus
  5. J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2nd edition, 2006.
  6. A. Roel and R. E. Plant, “Factors underlying yield variability in two California rice fields,” Agronomy Journal, vol. 96, no. 5, pp. 1481–1494, 2004. View at Google Scholar · View at Scopus
  7. J. G. Fortin, F. Anctil, L.-É. Parent, and M. A. Bolinder, “Site-specific early season potato yield forecast by neural network in Eastern Canada,” Precision Agriculture, vol. 12, no. 6, pp. 905–923, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Ruß and R. Kruse, “Feature selection for wheat yield prediction,” in Research and Development in Intelligent Systems XXVI, M. Bramer, R. Ellis, and M. Petridis, Eds., pp. 465–478, Springer, London, UK, 2010. View at Publisher · View at Google Scholar
  9. M. Karagiannopoulos, D. Anyfantis, S. Kotsiantis, and P. Pintelas, “A feature selection for regression problems,” in Proceedings of the 8th Hellenic European Conference on Computer Mathematics and Its Applications (HERCMA '07), Athens, Greece, September 2007.
  10. A. Arauzo-Azofra, J. M. Benitez, and J. L. Castro, “Consistency measures for feature selection,” Journal of Intelligent Information Systems, vol. 30, no. 3, pp. 273–292, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. W. Ojeda-Bustamante, J. M. González-Camacho, E. Sifuentes-Ibarra, E. Isidro, and L. Rendón-Pimentel, “Using spatial information systems to improve water management in Mexico,” Agricultural Water Management, vol. 89, no. 1-2, pp. 81–88, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. B. Safa, A. Khalili, M. Teshnehlab, and A. Liaghat, “Artificial neural networks application to predict wheat yield using climatic data,” in Proceedings of 20th International Conference on IIPS, pp. 1–39, Iranian Meteorological Organization, January 2004.
  13. J. Frausto-Solis, A. Gonzalez-Sanchez, and M. Larre, “A new method for optimal cropping pattern,” in Proceedings of the 8th Mexican International Conference on Artificial Intelligence (MICAI '09), vol. 5845 of Lecture Notes in Computer Science, pp. 566–577, Springer, Guanajuato, México, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. L. Wasserman, All of Statistics. A Concise Course in Statistical Inference, Springer, New York, NY, USA, 2004.
  15. IBM Corp., IBM SPSS Statistics for Windows, Version 20.0, IBM Corp., Armonk, NY, 2011.
  16. R. Mundry and C. L. Nunn, “Stepwise model fitting and statistical inference: turning noise into signal pollution,” The American Naturalist, vol. 173, no. 1, pp. 119–123, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. I. H. Witten and E. Frank, Data Mining, Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2nd edition, 2005.