The intelligent transformation of agriculture places plant growth prediction as a critical component for ensuring food security, optimizing resource allocation, and enhancing sustainable productivity. Traditional methods reliant on empirical or simplified mechanistic models struggle with the nonlinearity, high dimensionality, and spatiotemporal heterogeneity inherent in agro-ecological systems. This study investigates the paradigm shift enabled by agricultural big data integrating multi-source, real-time streams from IoT sensors, satellites, UAVs, and farm management systems. We propose a ``Multi-source Data Assimilation and Hybrid Intelligence'' (MDA-HI) framework that synergistically coupl... More >

The integration of information technology into agriculture is fundamental to modern agricultural development. However, traditional agricultural information models, which rely on analytical AI for prediction and monitoring, face significant limitations in handling unstructured data, generating actionable knowledge, and supporting complex decision-making in dynamic farm environments. This study's core innovation lies in constructing a “Generative AI-Driven Agricultural Informatization Framework” (GAAIF), which quantifies the synergistic mechanisms between generative models and specific agricultural scenarios. By introducing a multi-modal data fusion engine and a task-specific fine-tuning p... More >