Artificial Intelligence-Driven Hybrid System for Real-Time Soil Monitoring and Optimal Crop Prediction Using SARIMAX for Forecasting and MSVM-DAG with Grey Wolf Optimizer

Climate change, resource scarcity, and the ever-increasing need for food are three of modern agriculture's most pressing problems. Using historical data, the suggested approach accurately predicts soil moisture using a Seasonal Autoregressive Integrated Moving Average with exogenous variables (SARIMAX) model. This model accounts for both seasonal trends and external factors, making the forecasts more credible. At the same time, a model called MSVM-DAG is used to come up with crop suggestions using all the weather and soil data possible. To further optimize the system and ensure robust model performance, the Grey Wolf Optimizer (GWO) method is used for hyperparameter tweaking. A variety of measures are used to assess the system's performance. For the soil moisture model, these include MSE, MAE, RMSE, R-squared, and MAPE. For the crop recommendation model, they include accuracy, precision, recall, F1-score, confusion matrix, ROC curve, and AUC. Cross-validation is also integrated. By leveraging real-time and historical data, this hybrid system aims to improve irrigation scheduling, water management, and crop selection, thereby optimizing resource allocation and maximizing yields.