Studies investigating the action of small RNAs on computationally predicted target genes require some form of experimental validation. Classical molecular methods of validating microRNA action on target genes are laborious， while approaches that tag predicted target sequences to qualitative reporter genes encounter technical limitations. The aim of this study was to address the challenge of experimentally validating large numbers of computationally predicted microRNA-target transcript interactions using an optimized， quantitative， cost-effective， and scalable approach. The presented method combines transient expression via agroinfiltration of leaves with a quantitative dual luciferase reporter system， where firefly luciferase is used to report the microRNA-target sequence interaction and Renilla luciferase is used as an internal standard to normalize expression between replicates. We report the appropriate concentration of leaf extracts and dilution factor to apply in order to avoid inhibition of firefly LUC activity. Furthermore， the optimal ratio of microRNA precursor expression construct to reporter construct and duration of the incubation period post-agroinfiltration were determined. The optimized dual luciferase assay provides an efficient， repeatable and scalable method to validate and quantify microRNA action on predicted target sequences. The optimized assay was used to validate five predicted targets of rice microRNA miR529b， with as few as six technical replicates. The assay can be extended to assess other small RNA-target sequence interactions， including assessing the functionality of an artificial miRNA or an RNAi construct on a targeted sequence.