2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA) · 2021 · 15 citations · 12 references
AgricultureEarth SciencesSoil SciencesMachine LearningSoil FertilitySoil ScienceKernel MethodsSoil Chemical PropertiesSoil Fertility EvaluationSoil PhSupport Vector MachinesPrediction ModelsPredictive Soil ModelingSoil Ph PredictionSoil Property PredictionSupport Vector ClassificationSupport Vector Machine ClassificationSupport Vector Regression
The significance of pH value of soil in assessing the soil fertility has been the motivation behind writing this research paper. As the pH of the soil is strongly dependent on the extent of minerals present in it, its pH can be estimated using the mineral data. The soil data belonging to the Kumaun part of Uttarakhand is used to design the prediction models. A machine learning techniaue SVM (support vector machine) has been implemented in MATLAB version R2021a. Three different prediction models with linear, quadratic and cubic kernels are established, and their prediction accuracy is compared.
12
Akey Sungheetha, Rajesh Sharma R · Journal of Trends in Computer Science and Smart Technology · 2021 · 167 citations · Full text