Vol. 7, Issue 9, Part A (2025)

Dynamic environmental stressors and fast-spreading crop diseases are becoming a greater threat to the agricultural productivity as they can lead to very serious losses that cannot be saved unless they are observed and corrected in time. The study presents a high-tech AI-based early warning system, namely Multi-Modal Stress-Aware Predictive Early Warning Network (MSPEW-Net), capable of addressing agricultural risks in a preventive way, based on predictive inference. MSPEW-Net is a heterogeneous data integration network that combines such data sources as data on environment sensors, satellite indices, UAV imagery, and past disease patterns to provide real-time prediction of stress-related abnormalities and disease outbreaks. Architecture is a combination of the temporal environmental stress encoder with a spatiotemporal crop health monitor that implements deep neural networks and a disease propagation inference model in graph neural networks. A multi-modal feature is aggregated through an attention-guided fusion layer, which makes the model interpreting and predicting better. Besides, the application of probabilistic forecasting and risk scoring will be provided to equip timely warnings and actionable information to farmers, agronomists, and policymakers. The proposed system based on explainable AI and predictive analytics helps to discover the early symptoms of ecological imbalance and the presence of pathogens much better. This is how it is possible to organize proactive intervention approaches, reduce crop losses, and sustain agricultural judgment in climate-susceptible areas. The suggested MSPEW-Net framework attained an overall accuracy of 94.2% in early risk in agriculture forecasting.