Next-Generation Intelligent Prediction Model for Real-Time Soil Nutrients’ Monitoring in Sustainable Farming

Soil nutrient content (SNC) is a key factor in plant development, and low soil fertility leads to an uneven distribution of essential nutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K). Hence, developing a nutrient predictive model has become an important area of research. Proper nutrient supplementation is essential to enhance crop yield and improve product quality. This paper proposes a model to estimate Soil Nutrient Content (SNC) using Gradient Descent Algorithm-based Multiple Linear Regression (GDA-MLR) algorithm. The model’s performance is evaluated for predicting SNC in agricultural fields. Additionally, Pearson’s correlation analysis is used to examine the relationship between SNC and factors such as moisture percentage (Mp), pH, and other soil nutrients, aiding in feature selection for the model. Model performance is evaluated using Root Mean Square Error (RMSE), Coefficient of Determination (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>), Mean Absolute Error (MAE), and Ratio of Prediction to Deviation (RPD). The proposed GDA-MLR model achieved an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 99.97% with urea fertilizer (Nitrogen) application, indicating excellent predictive accuracy. Its low RMSE and high R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> demonstrate superior performance compared to existing models. This makes the model well-suited for both precision and sustainable farming, promoting efficient and eco-friendly agricultural practices.