Design of an Efficient Soil Parameter Monitoring & Health Prediction Model via Regressive Optimizations

Understanding and maintaining soil health are of utmost importance at a time of heightened environmental concerns and problems with food security. The intricacy of soil health indices, their dynamic nature, and the complexity of their interconnections have frequently limited the capabilities of existing models despite recent improvements in predictive models. They frequently lack the ability to capture temporal dependencies and fail to foresee potential problems with soil health in the future. The creation of an innovative soil parameter monitoring and health prediction model using regressive optimisations is presented in this research as a solution to these restrictions. Temporal values of important soil parameters, including pH, Temperature, Humidity, Nitrogen, Potassium, and Phosphorous Levels, are systematically processed and analysed using a Vector Autoregressive Moving Average with Exogenous Variables (VARMAx) model. The VARMAx model offers a reliable method for not only determining the soil's present state of health but also foreseeing potential problems in the future. The effectiveness of soil management systems is increased because to this dual functionality, which permits prompt interventions. This model's outstanding precision of 98.9%, accuracy of 98.5%, and recall rate of 98.4%, with a latency less than 1.25 ms, are demonstrated empirically across many soil samples. As a result, the created model not only addresses the shortcomings of the previous models but also provides a highly precise, effective, and futuristic instrument for tracking and forecasting soil health. It has the potential to greatly enhance soil management techniques, which would support sustainable agriculture and food security