The　on-year　and　off-year　phenomenon　is　a　distinctive　phenological　characteristic　of　Moso　bamboo,　reflecting　variations　in　nutrient　dynamics　and　endogenous　hormonal　rhythms　during　the　transition　from　bamboo　shoot　to　the　culm.　This　phenomenon　also　influences　pest　resistance　between　the　on-year　and　off-year　cycles　of　Moso　bamboo.　Pantana　phyllostachysae　Chao　is　a　leaf-feeding　pest　that　affects　Moso　bamboo.　However,　monitoring　P.　phyllostachysae　damage　using　remote　sensing　data　is　challenging　because　the　off-year　Moso　bamboo　has　physiological　characteristics　similar　to　on-year　Moso　bamboo　infested　with　P.　phyllostachysae.　This　study　utilizes　the　Recursive　Feature　Elimination　(RFE)　algorithm　to　investigate　hyperspectral　remote　sensing　characteristics　of　P.　phyllostachysae　in　Moso　bamboo　forests.　We　analyzed　the　impact　of　on-year　and　off-year　phenological　characteristics　on　the　accuracy　of　hazard　extraction　and　developed　detection　models　for　P.　phyllostachysae　hazard　levels　in　on-year　and　off-year　Moso　bamboo　using　Support　Vector　Machine　(SVM),　Random　Forest　(RF),　eXtreme　Gradient　Boosting　(XGBoost),　and　one-dimensional　Convolutional　Neural　Network　(1D-CNN).　The　results　demonstrate　that　classical　machine　learning　and　deep　learning　models　can　effectively　detect　P.　phyllostachysae　damage,　with　the　1D-CNN　algorithm　achieving　the　best　performance.　Analyzing　the　impact　of　the　phenological　differences　between　on-year　and　off-year　Moso　bamboo　on　pest　identification　accuracy　revealed　that　when　four　machine　learning　models　accounted　for　these　phenological　characteristics,　their　accuracy　in　identifying　pests　was　significantly　higher　than　that　of　a　model　which　did　not　take　into　account　the　bamboo　phenology.　This　finding　highlights　that　considering　the　phenological　characteristics　of　on-year　and　off-year　Moso　bamboo　can　substantially　improve　the　detection　accuracy　of　UAV　hyperspectral　remote　sensing　in　monitoring　P.　phyllostachysae　damage.　This　provides　more　accurate　technical　support　for　the　health　management　and　resource　protection　of　bamboo　forests　and　offers　a　scientific　basis　for　maximizing　the　ecological　and　economic　benefits　of　bamboo　forests.　©　2025　Wuhan　University.　Published　by　Informa　UK　Limited,　trading　as　Taylor　&　Francis　Group.