Advances in soil fertility classification: Data mining approach

Agriculture is as old as civilization. Indian agriculture heritage witnessed nomadic shifting cultivation to present precision agriculture feeding its ever-burgeoning population. Indeed a magical feat that no other country in the world can partake in India in terms of its agriculture production. Soil fertility, an important dimension of soil productivity braced up India from starvation to self-sufficiency. This paper focuses on soil fertility classification of soil dataset of Ranga Reddy district of Telangana using data mining techniques viz., Tree based – Random Forest, Random Tree and; Lazy based -IBK and K Star; and Bayesian-based Naïve Bayes. The soil fertility dataset extended to 2,408 instances comprising 12 attributes of soil parameters which included soil physicochemical properties, and macro, secondary and micronutrients of soil collected from selected model villages of the Ranga Reddy district. Soil the performance of each model was examined in terms of correctly classified instances, incorrectly classified instances, Receiver Operating Characteristic (ROC) Area, Kappa statistic., mean absolute error, Root mean squared error, Relative absolute error and Root relative squared error. The classification algorithm, the Random Forest model had achieved the highest prediction accuracy of 93.69%, sensitivity of 0.937 and precision of 0.902 and F1 score of as compared with the rest of the models.