ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Big Data, AI, and the Environment
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1546643
This article is part of the Research TopicAdvanced Applications of Artificial Intelligence and Big Data Analytics for Integrated Water and Agricultural Resource Management: Emerging Paradigms and MethodologiesView all 3 articles
Analysis of Agricultural Production Efficiency Improvement and Economic Sustainability Based on Multi-Source Remote Sensing Data
Provisionally accepted- Xi'an Peihua University, Xi'an, China
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Improving agricultural efficiency while ensuring environmental and economic sustainability remains a global challenge. 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.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. 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.
Keywords: agricultural sustainability, multi-source remote sensing, Economic efficiency, Resource optimization, dynamic modeling
Received: 17 Dec 2024; Accepted: 14 Jul 2025.
Copyright: © 2025 Chai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Cuijing Chai, Xi'an Peihua University, Xi'an, China
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