RNA binding proteins (RBPs) are a large protein family that play important roles at all level of gene regulation through interaction with RNAs. Plant RBPs play diverse roles in growth, development, genome organization, stress response, immunity, mRNA processing, and post-transcriptional regulation. Existing experimental techniques for identifying RBPs in plants are time-consuming and expensive. Thus, prediction of RBPs from sequence data using computational methods can be useful to quickly annotate and guide the experimental process. The RBPLight is a sequence-based computational model for identifying plant specific RBPs. The server has been trained with 2496 RBPs and 2496 non-RBPs encompassing 35 plant species, where position-specific-scoring-matrix (PSSM)-based feature descriptors have been utilized as numerical input to learning algorithm. The Light gradient boosting machine (LightGBM) has been utilized as learning algorithm for prediction.