Analysis of agricultural production efficiency improvement and economic sustainability based on multi-source remote sensing data

Introduction Improving agricultural efficiency while ensuring environmental and economic sustainability remains a global challenge. Methods This study introduces the Integrated Agro-Economic Sustainability Framework (IAESF), a novel architecture that fuses multi-source remote sensing data—including satellite, UAV, and ground sensors—with multi-objective optimization and real-time feedback mechanisms. IAESF leverages predictive analytics and adaptive resource allocation to balance profitability with sustainability metrics such as carbon emissions, water usage, and biodiversity preservation. The framework is evaluated across four benchmark datasets (GF-FloodNet, SSL4EO-L, OpenSARShip, TimeSen2Crop) covering spatial, temporal, and spectral variability. Results Experimental results show significant improvements in segmentation accuracy (IoU up to 91.34%) and yield forecasting precision (RMSE reduced by 29.5%) over state-of-the-art models. Scalability is demonstrated through deployment across both smallholder and industrial-scale simulations, supported by dynamic optimization and lightweight model design. Discussion IAESF aligns with global sustainability goals (e.g., SDG 2, SDG 13) and offers actionable insights for precision agriculture policy and planning. This work advances a transparent, interpretable, and resilient decision-making paradigm for sustainable agricultural systems.