In　recent　years,　the　rapid　development　of　unmanned　aerial　vehicle　(UAV)　remote　sensing　technology　has　provided　a　new　means　to　efficiently　monitor　forest　resources　and　effectively　prevent　and　control　pests　and　diseases.　This　study　aims　to　develop　a　detection　model　to　study　the　damage　caused　to　Moso　bamboo　forests　by　Pantana　phyllostachysae　Chao　(PPC),　a　major　leaf-eating　pest,　at　5　cm　resolution.　Damage　sensitive　features　were　extracted　from　multispectral　images　acquired　by　UAVs　and　used　to　train　detection　models　based　on　support　vector　machines　(SVM),　random　forests　(RF),　and　extreme　gradient　boosting　tree　(XGBoost)　machine　learning　algorithms.　The　overall　detection　accuracy　(OA)　and　Kappa　coefficient　of　SVM,　RF,　and　XGBoost　were　81.95%,　0.733,　85.71%,　0.805,　and　86.47%,　0.811,　respectively.　Meanwhile,　the　detection　accuracies　of　SVM,　RF,　and　XGBoost　were　78.26%,　76.19%,　and　80.95%　for　healthy,　75.00%,　83.87%,　and　79.17%　for　mild　damage,　83.33%,　86.49%,　and　85.00%　for　moderate　damage,　and　82.5%,　90.91%,　and　93.75%　for　severe　damage　Moso　bamboo,　respectively.　Overall,　XGBoost　exhibited　the　best　detection　performance,　followed　by　RF　and　SVM.　Thus,　the　study　findings　provide　a　technical　reference　for　the　regional　monitoring　and　control　of　PPC　in　Moso　bamboo.　©　2022　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.