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 >

Hilly and mountainous areas account for 69% of China’s land area and undertake critical agricultural production tasks, but the poor adaptability of mainstream intelligent agricultural machinery and inefficient promotion models have become key bottlenecks restricting agricultural modernization. This study’s core innovation lies in constructing a ``four-dimensional integrated solution'' (equipment lightweight improvement - dynamic control optimization - hybrid sharing promotion - farmland mechanization-friendly transformation) and quantifying the coupling mechanism between topographic constraints and agricultural machinery performance. By introducing plot shape coefficient and slope volati... More >