<![CDATA[The rapid development of remote sensing technologies has opened new opportunities for precision agriculture, particularly in monitoring crop growth and managing pest outbreaks. This study explores the integration of unmanned aerial vehicles (drones) and satellite imagery to analyze vegetation health and detect pest infestations in paddy fields. Multispectral drone data and Sentinel-2 imagery were processed to generate vegetation indices such as NDVI and EVI, which serve as indicators of plant physiological status. Field validation confirmed that anomalies in vegetation indices corresponded with pest attacks, particularly brown planthoppers and stem borers, as well as environmental stress factors such as drought and nutrient deficiencies. The results indicate that drones provide high-resolution data capable of capturing micro-variations within small-scale fields, while satellites offer broader coverage and temporal consistency. The integration of both data sources, combined with Geographic Information System (GIS) analysis, enabled the creation of spatial distribution maps for crop growth and pest occurrence. These maps facilitate site-specific management strategies, improving the efficiency of fertilizer and pesticide application, and supporting sustainable agricultural practices. Overall, this research highlights the strategic role of drones and satellite imagery in advancing precision farming in Indonesia. Despite challenges such as operational costs and technical capacity, the findings demonstrate the potential of remote sensing technologies to enhance productivity, reduce risks, and strengthen national food security. Future directions include the application of artificial intelligence and machine learning to further optimize remote sensing-based agricultural monitoring systems. ]]>
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