Abstract
Soil performs a significant role in the agricultural ecosystem by supplying essential nutrients and a conducive environment for plants’ growth and crop yield. Inside the agribusiness space, the soil classification is a crucial work that gives good classification results for different soil types. The taxonomy provides an excellent rating for inherent soil elements. This work investigates the accuracy of three well-known classification models like K-Nearest Neighbor (k-NN), Naive Bayes (NB) and, Decision Tree (DT) using a publically available agricultural soil dataset. Post investigation, an Ensemble Classifier (EC) is proposed by fusing the above mentioned three classifiers. The experimental results indicate that EC has the highest accuracy of 84% in comparison to the NB (72.90%), k-NN (73.56%), and DT (80.84%). So it performs better than the other classifiers. The results infer that EC would be useful for accurate classification of soil types in the agricultural domain.
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