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