UAV-Based Remote Sensing and Artificial Intelligence for Climate-Smart Agriculture: A Systematic Review of Technologies, Analytics, and Applications in Smallholder Systems

Unmanned aerial vehicle (UAV)-based remote sensing combined with artificial intelligence (AI) has emerged as a key enabler of climate-smart agriculture (CSA). However, the extent to which these technologies operationalize CSA’s three pillars, productivity, adaptation, and mitigation, remains unevenly assessed. This study presents a PRISMA-guided systematic review of 59 peer-reviewed studies examining UAV–AI applications in agricultural systems. The synthesis categorizes platform configurations, sensor modalities, analytical architectures, geographic distribution, and data integration strategies, and evaluates their alignment with CSA objectives. Results indicate that productivity-oriented applications, including yield estimation, biomass mapping, and nutrient assessment, are the most mature, while adaptation-focused stress detection is also well established. In contrast, mitigation-oriented applications, such as carbon quantification and greenhouse gas monitoring, remain comparatively underrepresented. The analysis further reveals a growing convergence toward multimodal sensing and cross-scale data integration linking UAV observations with satellite and environmental datasets. However, substantial variability in validation approaches and dataset representativeness limits generalizability and scalability. Advancing UAV–AI contributions to CSA therefore requires methodological standardization, interoperable data governance, and strengthened institutional capacity. Collectively, the findings position UAV–AI systems as emerging components of climate-smart agricultural intelligence infrastructure rather than isolated monitoring tools.