Machine Learning Approach for Soil Nutrient Prediction

Soil nutrient prediction is of utmost importance in agricultural practices as it directly affects crop productivity and sustainable farming. In order to forecast soil nutrient levels, this study compares the effectiveness of two machine learning algorithms: K-Nearest Neighbors (KNN) regression and Multiple Linear Regression (MLR). Atomic emission spectroscopy data and conventional laboratory data pertaining to macro and micronutrients present in soil taken from seven different zones of Tamil Nadu are involved in the construction of the customized dataset that is used for this investigation. Initially, the data in the dataset is pre-processed to filter outliers. Both KNN regression and MLR were trained and assessed using the gathered dataset in the comparison study. Using the r2_score as a performance metric, the percentage of variation achieved by the models is evaluated. The performance of algorithms was evaluated. According to the findings, KNN regression works better than MLR at predicting soil nutrient levels. Due to its capacity to capture complex interactions between soil nutrient concentrations and predictor variables, KNN regression shows improved accuracy and generalization capabilities. The findings have important implications for farmers and decision-makers, offering insightful information to support wise choices for land management and environmentally friendly farming methods.