Sujatha, M.Jaidhar, C.D.2026-02-062021ICSCCC 2021 - International Conference on Secure Cyber Computing and Communications, 2021, Vol., , p. 138-143https://doi.org/10.1109/ICSCCC51823.2021.9478169https://idr.nitk.ac.in/handle/123456789/30132Indian economy depends on agriculture production. However, the quantity of agricultural production depends on fertility of the soil. In this study chemical soil measurements are used to classify fertility of the soil. The 11 soil parameters namely, pH, EC, OC, P, K, S, Zn, B, Fe, Cu, Mn were used to classify soil as LOW, MEDIUM and HIGH fertile. The machine learning-based classifiers such as naive bayes, logistic regression, Support Vector Machine (SVM), decision tree bagging, Boosted Regression Tree (BRT), Random Forests (RF) were used to classify the soil as LOW, MEDIUM and HIGH fertile soil. The RF classifier showed best performance among other classifiers. © 2021 IEEE.ClassificationMachine Learning based TechniqueSoil Fertility