Integrated Remote Sensing and Ai Framework for Precision Agriculture In Con f lict-Affected Yemen: Improving Water Efficiency, Climate Resilience, and Food Security
Abstract:
This study proposes a solid academic and linguistic criticism of precision agriculture (PA) as an innovative response for the Yemeni agricultural sector confronted with severe water scarcity, climate change impacts, and conflict toler ance. Leveraging the latest Remote Sensing (RS) and Geographic Information Systems (GIS) technologies, we pres ent a robust Precision Agriculture Framework for Yemen (PAF-Yemen). This framework integrates a mixed-meth ods research strategy, an artificial intelligence-based spatial data infrastructure, and a multi-level implementation framework to ensure optimal utilization of resources, ensure climate resilience, and enhance food security. Our assessment, supplemented with ongoing empirical evidence (2023-2025), demonstrates the capacity of PAF-Yemen to significantly reduce water consumption, lessen crop loss, and enhance the agricultural economy. Moreover, this study contributes scientifically by plugging gaps in data, adoption, and policy in fragile situations, with a replicable model of sustainable agricultural growth in similar arid and conflict-prone zones. Ultimately, the focus of the study is the remote implementation of strategies and policies directed toward self-sustaining food systems for Yemen.
