Icing　on　wind　turbine　blades　in　high-altitude,　mountainous,　and　maritime　regions　poses　significant　challenges,　leading　to　power　degradation　and　revenue　loss.　Existing　blade　icing　detection　methods　mainly　rely　on　deep　learning　techniques,　such　as　Graph　Neural　Networks　(GNNs)　and　Transformers,　to　analyze　Supervisory　Control　and　Data　Acquisition　(SCADA)　data　from　wind　turbine　sensors.　However,　these　approaches　are　computationally　intensive,　suffer　from　high　latency,　and　require　significant　computational　resources.　Moreover,　they　often　transmit　SCADA　data　to　centralized　data　centers　for　processing,　which　introduces　delays　and　compromises　data　quality　due　to　network　instability.　Additionally,　these　methods　struggle　to　capture　persistent　and　transient　patterns　in　SCADA　data,　resulting　in　low　detection　accuracy.　To　overcome　these　challenges,　we　propose　FreqICE,　an　efficient　blade　icing　detection　model　via　frequency-domain　learning.　FreqICE　employs　a　lightweight　frequency-domain　multilayer　perceptron　(Freq　MLP)　combined　with　a　multiview　pretrained　codebook,　ensuring　efficient　detection　inference.　The　codebook　learns　persistent　patterns　from　multiple　views　and　can　be　retrieved　via　simple　table　lookups,　while　the　Freq　MLP　captures　transient　temporal　features　effectively.　A　feature　attention　block　is　used　to　emphasize　the　most　important　feature　patterns.　Together,　these　modules　effectively　capture　both　persistent　and　transient　patterns,　enabling　effective　and　efficient　blade　icing　detection　on　wind　turbines.　Experimental　results　demonstrate　that　FreqICE　achieves　state-of-the-art　(SOTA)　detection　accuracy　across　all　baseline　models.　Compared　with　the　existing　SOTA　method,　FreqICE　significantly　reduces　model　complexity　while　maintaining　high　accuracy.　Specifically,　it　decreases　the　total　number　of　parameters　from　301　510　to　50　694,　achieving　a　$5.9\times　$　reduction.　It　also　lowers　the　multiply-accumulate　(MACC)　counts　from　8.01　to　0.04　million,　leading　to　a　$200.3\times　$　acceleration.　Additionally,　peak　memory　usage　is　reduced　from　13.46　to　0.30　MB,　marking　a　$44.9\times　$　decrease.　This　substantial　reduction　in　memory　footprint　facilitates　deployment　on　resource-constrained　devices　without　compromising　performance.