Design of an Improved Model for Soil Parameter Monitoring and Health Prediction Using Machine Learning and Data Fusion Techniques

The need for high agricultural productivity and sustainability has driven precision agriculture to the forefront at present, especially in terms of monitoring soil parameters for health prediction. In the proposed model, readings from sensors are integrated through the Data Fusion Model using the Kalman filter and Bayesian inference, which drastically reduces noise and enhances the reliability of the estimated soil parameters. It standardizes the parameters of the soil and aggregates them into a single-index metric, weighted-scoring, to express an easily interpretable degree of soil health to farmers. This ties into the Machine Learning-Based Health Prediction Model, learning from historical and real-time data by empowering algorithms in the likes of Random Forest, Support Vector Machines, and Neural Networks to predict health trends of the soil with high accuracy. Our Yield Prediction Model implements state-of-the-art machine learning techniques, like Gradient Boosting Machines and Long Short-Term Memory networks, in predicting crop yields with inputs based on soil health indicators, weather conditions, and historical data. The Crop Growth Simulation Model can simulate crop growth dynamically through differential equations and, therefore, offer real-time adjustment and predictive insights at different stages of growth. The outputs from all the models get integrated with the support of a decision support system, which provides real-time, rule-based suggestions based on certain conditions in farms.