Spectral　imaging　is　a　promising　technique　for　detecting　the　quality　of　rice　seeds.　However,　the　high　cost　of　the　system　has　limited　it　to　more　practical　applications.　The　study　was　aimed　to　develop　a　low-cost　narrow　band　multispectral　imaging　system　for　detecting　rice　false　smut　(RFS)　in　rice　seeds.　Two　different　cultivars　of　rice　seeds　were　artificially　inoculated　with　RFS.　Results　have　demonstrated　that　spectral　features　at　460,　520,　660,　740,　850,　and　940　nm　were　well　linked　to　the　RFS.　It　achieved　an　overall　accuracy　of　98.7%　with　a　false　negative　rate　of　3.2%　for　Zheliang,　and　91.4%　with　6.7%　for　Xiushui,　respectively,　using　the　least　squares-support　vector　machine.　Moreover,　the　robustness　of　the　model　was　validated　through　transferring　the　model　of　Zheliang　to　Xiushui　with　the　overall　accuracy　of　90.3%　and　false　negative　rate　of　7.8%.　These　results　demonstrate　the　feasibility　of　the　developed　system　for　RFS　identification　with　a　low　detecting　cost.