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A Survey and Analysis of Various Agricultural Crops Classification Techniques

by Surabhi Chouhan, Divakar Singh, Anju Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 136 - Number 11
Year of Publication: 2016
Authors: Surabhi Chouhan, Divakar Singh, Anju Singh
10.5120/ijca2016908575

Surabhi Chouhan, Divakar Singh, Anju Singh . A Survey and Analysis of Various Agricultural Crops Classification Techniques. International Journal of Computer Applications. 136, 11 ( February 2016), 25-30. DOI=10.5120/ijca2016908575

@article{ 10.5120/ijca2016908575,
author = { Surabhi Chouhan, Divakar Singh, Anju Singh },
title = { A Survey and Analysis of Various Agricultural Crops Classification Techniques },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 136 },
number = { 11 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 25-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume136/number11/24199-2016908575/ },
doi = { 10.5120/ijca2016908575 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:36:50.327580+05:30
%A Surabhi Chouhan
%A Divakar Singh
%A Anju Singh
%T A Survey and Analysis of Various Agricultural Crops Classification Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 136
%N 11
%P 25-30
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Mining is way of providing and extracting some meaning information from the data so that the data can be classified and grouped easily and quickly. These mining algorithms can be applied in various fields including classification of agricultural crops production. In the fields of Data Mining various efficient algorithms are implemented for the classification of agricultural crops production. Here in this paper a survey of all the existing techniques as well as their advantages and issues are discussed. Hence by analyzing their various advantages and issues a new and efficient technique for the classification of agricultural crops production is proposed in future such as classification using Fuzzy Conclusion Tree by the Optimizing the Feature Withdrawal using PSO-SVM (Particle Swarm Optimization with Support Vector Machine).

References
  1. Geraldin B. Dela Cruz, Bobby D. Gerardo, Bartolome T. Tanguilig III, “Agricultural Crops Classification Models Based on PCA_GA Implementation in Data Minig”, International Journal of Modeling & Optimization, Vol. 4, No. 5, 2014.
  2. Srivastava, Jaideep, Robert Cooley, Mukund Deshpande, and Pang-Ning Tan. "Web usage mining: Discovery and applications of usage patterns from web data." ACM SIGKDD Explorations Newsletter, vol. 1, no. 2, pp. 12-23, 2000.
  3. U.M. Fayyad, et al.: “From Data Mining to Knowledge Discovery: An Overview“, Advances in Knowledge Discovery and Data Mining: 1-34, AAAI Press/ MIT Press, 1996, ISBN 0-262-56097-6.
  4. Agrawal, Rakesh, and Ramakrishnan Srikant. "Fast algorithms for mining association rules." In Proceedings of 20th international conference on very large data bases, VLDB, vol. 1215, pp. 487-499, 1994.
  5. Han, Jiawei, Jian Pei, and Yiwen Yin. "Mining frequent patterns without candidate generation." In ACM SIGMOD Record, vol. 29, no. 2, pp. 1-12. ACM, 2000.
  6. Toivonen, Hannu "Sampling large databases for association rules." In Proceedings of International Conference on very Large Databases (VLDB), vol. 96, pp. 134-145, 1996.
  7. Han J, Pei J, Yin Y, Mao R, Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach, Data Mining and Knowledge Discovery, Vol 8, Issue 1, Jan 2004, pp 53 – 87, 2004.
  8. J. Han, M. Kamber, and J. Pei, “Data Mining: Concepts and Techniques”, 3rd edition, The Morgan Kaufmann Series in Data Management Systems, USA, 2006.
  9. Xin Wang and Howard J. Hamilton “A Comparative Study of Two Density-Based Spatial Clustering Algorithms for Very Large Datasets”, Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence, pp. 120-132, 2005.
  10. Santhosh Kumar, B., and K. V. Rukmani. "Implementation of Web Usage Mining Using APRIORI and FP Growth Algorithms." International Journal of Advanced Networking & Applications 1, no. 6 (2010).
  11. Yethiraj N G, “Applying Data Mining Techniques in the Field Of Agriculture and Allied Sciences”, International Journal of Business Intelligents ISSN: 2278-2400, Vol 01, Issue 02, and December 2012.
  12. Sanjay D. Sawaitul, Prof. K.P. Wagh, Dr. P.N. Chatur, “Classification and Prediction of Future Weather by using Back Propagation Algorithm-An Approach”, International Journal of Emerging Technology and Advanced Engineering, Vol. 2, Issue 1, January 2012, pp. 110-113.
  13. K. SOMVANSHI, ET AL., “MODELING AND PREDICTION OF RAINFALL USING ARTIFICIAL NEURAL NETWORK AND ARIMA TECHNIQUES”, J. IND. GEOPHYS. UNION, VOL. 10, NO. 2, PP. 141-151, 2006.
  14. K. Verheyen, D. Adriaens, M. Hermy, and S. Deckers, “High resolution continuous soil classification using morphological soil profile descriptions”, Geoderma, vol. 101, pp. 31-48, 2001.
  15. Urtubia, A., Pérez-Correa, J. R., Soto, A., & Pszczolkowski, P. (2007, “Using data mining techniques to predict industrial wine problem fermentations”, Food Control, 18(12), 1512-1517.
  16. I. Jagielska, C. Mattehews, T. Whitfort, “An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems”, Neurocomputing, Vol. 24, pp. 37-54, 1999.
  17. Tellaeche, A., BurgosArtizzu, X. P., Pajares, G., & Ribeiro, A. (2007), “A vision-based hybrid classifier for weeds detection in precision agriculture through the Bayesian and Fuzzy k-Means paradigms”, In Innovations in Hybrid Intelligent Systems (pp. 72-79). Springer Berlin Heidelberg.
  18. Veenadhari, S. 2007, “Crop productivity mapping based on decision tree and Bayesian classification”. Unpublished M.Tech Thesis submitted to Makhanlal Chaturvedi National University of Journalism and Communication, Bhopal.
  19. Shalvi D and De Claris N., “Unsupervised neural network approach to medical data mining techniques”, in Proceedings of IEEE International Joint Conference on Neural Networks, (Alaska), pp. 171-176, May 1998.
  20. B. Rajagopalan and U. Lal, “A K-nearest neighbor simulator for daily precipitation and other weather variable”, Water Resources, vol. 35, pp. 3089-3101, 1999.
  21. S.Veenadhari, Dr. Bharat Misra, Dr. CD Singh, “Data mining Techniques for Predicting Crop Productivity – A review article”, International Journal of Computer Science and Technology IJCST Vol. 2, Issue 1, March 2011.
  22. Sneath, P. H. A., & Sokal, R. R. (1973). Numerical Taxonomy. Freeman: San Francisco.
  23. Detecting Leaf Spots in Cucumber CropUsing Fuzzy Clustering Algorithm: Mohammed El Helly, Hoda Onsi, Ahmed Rafea, Salwa El-Gamma.
  24. Fisher, R. A. (1936). The Use of Multiple Measurements in Taxonomic Problems, Annals of Eugenics, 7: 179–188.
  25. Huang, J., Y. Yuan, W. Cui and Y. Zhan, 2008. IFIP international federation for information processing. http://en.wikipedia.org/wiki/International_Federation_for_Information_Processing.
  26. J.R. Quinlan. C4.5 programs for machine learning. Morgan Kaufmann, 1993.
  27. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Springer, 2001.
  28. Chosa, T., Y. Shibata, M. Omine, K. Kobayashi, K. Toriyama and R. Sasaki, 2003. Map based variable control system for granule applicator. J. Jap. Soc. Agric. Machinery, 65: 128-135.
  29. Baugher, T.; Schupp, J.; Travis, J.; Hull, L.; Ngugi, H.; Krawczyk, G.; Harsh, M.; Reichard, K.; Ellis, K.; Remcheck, J.; Crassweller, R.; Marini, R.; Harper, J.; Kime, L.; Heinemann, P.; Liu, J.; Lewis, K.; Hoheisel, G.; Jones, V.; Glenn, M.; Miller, S.; Tabb, A.; Park, J.; Slaughter, D.; Johnson, S.; Landers, A.; Reichard, G.; Singh, S.; Bergerman,M.; Kantor, G.; Messner, W. Speciality Crop Innovations: Progress and Future Directions; Specialty Crop Innovations Progress Report; College of Agri
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