Paper Title
Dynamic Crop Yield Prediction through Real-Time Fusion of Multi-Source Agricultural Data

Abstract
Crop yield prediction in precision agriculture proves to Be a very critical issue that involves integration of a number of heterogeneous data streams at different temporal and spatial resolutions. This paper presents a novel three- stage fusion architecture which seamlessly fuses satellite, IoT sensor network, weather data, and historical yield records through attention-based neural mechanisms. The developed framework will focus on three main issues: (1) aligning high- frequency IoT data with 5-minute intervals to low-frequency satellite observations (5 days revisits); (2) dynamic importance of features through the different stages of phenology in crops; (3) spatial resolution differences in the two data types, satellite pixel of10m, and point-source sensor measurements. The proposed Farm Cast AI system implements wave let-based temporal synchronization, growth- stage-dependent attention mechanisms, and a hybrid early- late fusion strategy to overcome those limitations. Experimental validation on six major crops has resulted in an improvement of 41.2% over single modality baselines, resulting in a Mean Absolute Error (MAE) of 148 kg/Ha for wheat and R²=0.93 for soybeans. Large-scale deployment of Farm Cast AI (farmcastai. lovable.app) is, in fact, capable of processing 1.2 million data points in one hour with 98.7 percent uptime and providing action-oriented insights to stakeholders through quantile regression and anomaly detection modules. Ablation studies show that our temporal alignment module leads to a reduction of prediction from error by 23.7 percent, while the dynamic attention gates add another17.5percentimprovementinaccuracyduringcritical growth stages of the crops. This system also proves to work extremely robustly under drought conditions with prediction errors maintained below 12.1 percent where traditional models proved to have errors above 41.5 percent]. Keywords - Multimodal Fusion, Temporal Alignment, Attention Mechanisms, Precision Agriculture, Yield Prediction, Deep Learning, IoT Sensors, Satellite Imagery, Phenology- Aware Modeling, Quantile Regression.