A Comparative Study on Various Data Mining Algorithms with Special Reference to Crop Yield Prediction

Objectives: To compare different data mining algorithms with the same parameters on the 10fold cross validation test to predict the crop yield. Methods/Analysis: Different data mining classification algorithms like K-nearest Neighbor, K-means, Neural Network, Support Vector Machine, Case-based Reasoning, Decision Tree algorithm, etc. are applied for various application of agriculture domain. A comparative study is done by using J48, Naïve Bayes and Simple Cart algorithms to determine which classification algorithm is best fitted for crop prediction. Findings: In this study, this work reveals the superior performance of J48 classification algorithm with accuracy 89.33% for crop prediction than the other two classification algorithms Simple Cart and Naïve Bayes. Novelty /Improvement: This study first time demonstrates the application of different data mining classification techniques (as discussed above) in the domain of agriculture for yield prediction.