Harnessing Deep Learning for Predictive Modeling of Soil Nutrient Dynamics in Precision Agriculture

Abstract

Precision agriculture relies on accurate soil nutrient detection to optimize fertilizer usage, enhance crop yield, and minimize environmental impact. This study investigates the application of a Deep Neural Network (DNN) model for predictive modeling of soil nutrient dynamics, utilizing a public soil dataset. Key performance metrics, including accuracy, precision, recall, and F1-score were analyzed across major nutrients: nitrogen, phosphorus, and potassium. The DNN model achieved superior performance, especially in distinguishing nutrient levels, surpassing ensemble models like Random Forest and Gradient Boosting. Visualization methods, including line graphs, a confusion matrix, and ROC curves, highlighted the model’s robustness and adaptability to varied soil conditions. While the model effectively addresses complex soil nutrient relationships, challenges remain in improving interpretability and managing closely aligned nutrient levels. This research underscores the potential of DNN models to support sustainable precision agriculture by enabling more precise, data-driven nutrient management decisions.