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Agriculture Yield Analysis using Som Classifier Algorithm along with Enhanced Preprocessing Techniques


  • Department of CSE, VNR VJIET, Hyderabad - 500090, Telangana, India
  • Department of CSE, SNIST, Hyderabad - 501301, Telangana, India


Objectives: Ages back mankind depends on agriculture yield and it is the only source for food, income and wealth. Even today people of countries like India depend majorly on Agriculture and allied sectors for their livelihood. Most of India’s income source is from the agriculture sector. Agriculture yield estimation and analysis are not taking place effectively. Method/Analysis: In this regard an algorithm to train the SVM’s i.e., the Sequential Minimal Optimization (SMO), classifier algorithm was proposed and results showed that classifier accuracies were improved when compared to other existing techniques. The process involves in replacing all missing values globally. This implementation is globally and then changes nominal attributes to binary form. By default all the attributes are normalized. Findings: Classifier coefficients output is purely from normalized data rather than from original data, which is very useful and important. Pair wise coupling is a multi-class classification method. Approach addresses the predicted probabilities that are coupled with the pairwise coupling method of Hastie and Tibshirani’s. The accuracies were very low when 10 fold cross validation is applied. Novelty/Improvement: The pre-processing techniques were enhanced to further improve the performance accuracies of SMO algorithm even when cross fold validation is applied on the data sets. Performance based com-parisons were made with the existing techniques.


Agriculture Yield, Estimation and Analysis, Multi-Class Problems, Preprocessing Techniques, Sequential Minimal Optimisation.

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  • Platt J. SVM by Sequential Minimal Optimization (SMO). Lecture by David Page. Available from: pages.cs.wisc. edu/ ~dpage/ cs760/ MLlectureSMO.ppt
  • Vagh Y. An investigation into the effect of stochastic annual rainfall on crop yields in South Western Australia. International Journal of Information and Education Technol-ogy. 2012 Jun; 2(3).
  • Cao LJ, et al. Parallel sequential minimal optimization for the training of support vector machines. IEEE Transactions on Neural Networks. 2006; 17:1039-49.
  • Mucherino A, Petraq P, Pardalos PM. A survey of data mining techniques applied to ag-riculture. Oper Res Int J. 2009; 9:121–40. DOI:10.1007/s12351-009-0054-6
  • Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov. 1998; 2(2):955–74.
  • Cortes C, Vapnik V. Support vector networks. Mach Learning. 1995; 20:273–97.
  • Vapnik VN. Statistical Learning Theory. New York: Wiley; 1998.
  • Wang JC, Holan SH, Nandram B, Barboza W, Toto C, Anderson E. A Bayesian approach to estimating agricultural yield based on multiple repeated surveys. Journal of Agricul-tural, Biological and Environmental Statistics. 2012; 17(1):84–106. DOI:10.1007/s13253-011-0067-5.
  • Marinkovic B, et al. Data mining approach for predictive modeling of agricultural yield data.
  • Quinlan JR. Learning with continuous classes. Proceedings of 5th Australian Joint Con-ference on Artificial Intelligence; 1992. p. 343-8.
  • Flake GW, Lawrence S. Efficient SVM regression training with SMO∗. Machine Learning. Kluwer Academic Publishers; 2002; 46:271–90.
  • Rao ChM, Rao AA. Crop yield analysis of the irrigated areas of all spatial locations in Guntur District of AP. 2014 Jun; 4(6):1-7.
  • Platt JC. Sequential minimal optimization: A fast algorithm for training support vector machines.
  • Dahikar SS, Rode Sandeep V. Agricultural crop yield prediction using artificial neural network approach. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering. 2014 Jan; 2(1).
  • Yu L, Liu H. Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research. 2004; 5:1205–24.
  • Hall MA, Holmes G. Benchmarking attributes selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering. 2003 May/Jun; 15(3).
  • Liu H, Hussain F, Tan CL, Dash M. Discretization: An enabling technique. Data Mining and Knowledge Discovery. 2002; 6(4):393–423.


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