ABSTRACT
Abstract Agriculture, the backbone of every country, has been an emerging field of research, particularly in the recent past. The soil type and environment are critical factors that drive agriculture, especially in terms of crop prediction. To determine which crops grow best in certain types of soil and environment, the characteristics of the latter are to be ascertained. In the past, farmers picked suitable crops for cultivation, based on first-hand experience. Today, however, identifying appropriate crops for particular areas has become a difficult proposition. The application of machine learning techniques to agriculture is an emerging field of research that helps predicts crops for easy cultivation and improved productivity. In this work, a comparative analysis is undertaken using several classifiers like the k-Nearest Neighbor (kNN), Naïve Bayes (NB), Decision Tree (DT), Support Vector Machines (SVM), Random Forests (RF) and Bagging to help suggest the most suitable cultivable crop(s), based on soil and environmental characteristics, for a specific piece of land. The algorithms are trained with the training data and subsequently tested with the soil and climate-based test dataset. The results of all the approaches are evaluated to identify the best classification techniques. Experimental results show that the bagging method outclasses others with respect to all performance metrics.