In this study, data mining and statistical approaches were established to estimate the agriculture growth using soil micro and macro nutrient level. Agricultural development covers different parameters like weather conditions and soil nutrient level. In this paper, consider four essential plant nutrient elements defined as micronutrients namely, zinc (Zn), iron (Fe), copper (Cu) and manganese (Mn). To archive this objective, analysis of paddy yield and level of soil micronutrients using data mining and machine learning approaches with stochastic gaussian process, linear regression model, and sequential minimal optimization algorithm. Numerical illustrations also provide to prove the results and discussions using different nutrients parameters and various machine learning classifiers with its accuracy parameters namely R2 score, Mean Absolute Error (MAE), Root Mean Squar Error (RMSE), Relative Absolute Error (RAE) and Root Relative Squared Error (RRSE).

Data Mining, Machine Learning, Gaussian Process and Performance Metrics.