Customized decision tree-based approach for classification of soil on cloud environment

Abstract

Agriculture is the economic backbone and the main means of livelihood in numerous developing countries. Numerous challenges related to farming and agriculturists exist. Cultivators face crop loss due to inappropriate selection of crops, inappropriate use of fertilizers, alterations in soil, ambiguous conditions in climate, and so on. The type of soil forms a crucial element in agriculture.The class of soil plays an important role in identifying what kind of crop should be planted along with the manure type to be applied. Classification of soil is essential to make effective use of the resources of soil. The texture of the soil has a major impact on crop growth. The role played by soil texture in determining the type of crop to be grown is significant. It is also employed in soil labs for determining the categories of soil. Soil texture plays a major role in determining the suitability of crops and handling famines. Soil chemical properties include “Electrical Conductivity” ($E_C$), “Organic Carbon” ($O_C$), “Phosphorous” (P), “Potassium” (K), “Power of Hydrogen” ($P_H$), “Zinc” ($Z_n$), “Boron” (B), and “Sulphur” (S). The crop growth is heavily influenced by the soil’s chemical composition. Keeping these considerations in mind, this work develops a customised decision tree ($C_{DT}$) that serves as a soil classifier (SC). A predictive framework is then devised that utilises the $C_{DT}$ to perform the soil classification based on the texture of the soil and its chemical properties. Extensive experiments on several real-world soil datasets from Karnataka, India; and benchmark agricultural datasets such as seeds, Urban Land Cover (ULC), Satellite Image of Land Data (LS), and Forest Cover Type (FCT) were conducted. The results demonstrated that the designed $C_{DT}$ classifier outperformed existing classifiers such as k-Nearest Neighbor ($K_{NN}$), Logistic Regression ($L_R$), Artificial Neural Network ($A_{NN}$), Classification and Regression Trees ($C_{ART}$), $C_{4.5}$), traditional SVM ($S_{VM}$), and Random Forest ($R_F$) in terms of Accuracy ($A_{cc}$), Sensitivity ($S_{ens}$), Specificity ($S_{pec}$), Precision ($P_{rec}$), and F-Score ($F_S$) on these datasets. The devised SC was deployed on the Heroku (Hk) cloud for effective access. Effective access in terms of end-user availability at all times was provided. An expert system for soil classification was built to provide information about soil classification round the clock using an internet-enabled device to the stakeholders of agriculture, such as cultivators and agricultural organizations. The agricultural raw data was stored in the form of blob objects on Amazon S3 (AS3).