In this paper a more improved Feature Selection and Classification technique is implanted on Benchmark Datasets such as Mushroom and Soyabean. The Proposed Methodology implemented is based on the Hybrid Combinatorial method of Applying PSO-SVM for the selection of Features from the Dataset and Then Classification is done using Fuzzy Based Decision Tree. Experimental results when performed on Various Datasets prove that the proposed methodology extracts more features as well as provides more accuracy as compared to existing methodologies.

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).