An Integrated UAV Micro-Meteorological Decision-Support Framework for Climate-Smart Agriculture

The emergence of unmanned aerial vehicles (UAVs) as airborne sensing platforms has expanded opportunities for high-resolution atmospheric observations in agricultural environments. This paper presents an integrated framework that combines UAV-based micro-meteorological profiling with artificial intelligence (AI)-assisted spraying optimization and life cycle assessment (LCA) to support climate-smart agricultural decision-making. Conventional ground stations and satellite observations often lack the spatial and temporal resolution required to characterize near-surface variations in temperature, humidity, wind conditions, and particulate concentrations that directly influence agrochemical dispersion and operational efficiency. UAV-enabled low-altitude sensing addresses these limitations by providing real-time measurements of microclimatic conditions during agricultural operations. Using field observations from semi-arid agricultural systems in Northern India, the study demonstrates how integrating UAV-derived atmospheric measurements with AI-assisted operational decision support improves the characterization of spray conditions while enabling environmental performance assessment through LCA. Rather than treating atmospheric monitoring, optimization, and sustainability assessment as independent processes, the proposed framework links these components into a unified decision-support workflow for precision agriculture. The analysis illustrates how localized atmospheric observations can improve operational decision-making, reduce unnecessary agrochemical losses, and strengthen evidence-based environmental management. Furthermore, the framework provides a practical pathway for incorporating field-scale atmospheric information into climate-smart agricultural planning and emission monitoring. By integrating atmospheric sensing with environmental assessment, this research contributes toward scalable digital agriculture systems that support sustainable resource management, improved operational efficiency, and climate-resilient farming practices.