The　detection　of　insect　pests　of　Phyllostachys　edulis　plays　a　vital　role　in　the　growth　of　bamboo　and　the　development　of　the　bamboo　industry.　Based　on　the　relationship　between　the　hyperspectral　canopy　spectrum　information　and　the　pest　degree　of　Phyllostachys　edulis,　the　characteristic　wavelengths,　indices,　and　spectral　parameters　closely　related　to　the　pests　in　the　canopy　spectrum　were　extracted,　and　Fisher＇s　discriminant　analysis　method　was　used　to　establish　Phyllostachys　edulis　Pest　degree　detection　model.　Here　are　the　wavelengths　at　400~508,　586~693,　724~900　nm　of　the　original　spectrum,　and　the　envelope　curve　to　remove　the　characteristic　wavelengths　between　400~756　nm　of　the　spectrum,　9　of　canopy　spectrum　vegetation　indices　and　7　characteristic　spectral　parameters　of　the　canopy　are　used　as　independent　variables　of　the　Fisher　discriminant　function　to　construct　the　discriminant　function.　Collected　300　groups　of　Phyllostachys　pubescens　leaf　pest　sample　data,　and　randomly　divided　them　into　210　modeling　sets　and　90　verification　sets.　According　to　the　detection　accuracy,　Kappa　coefficient　and　determination　coefficient　R2　as　the　test　standards,　the　effect　of　the　established　discriminant　function　is　evaluated　and　compared.　The　results　show　that　the　inspection　accuracy　of　the　Fisher　discriminant　function　established　by　the　original　spectrum,　de-envelope　spectrum,　canopy　index,　and　spectral　parameters　as　independent　variables　are　84.4%,　81.1%,　79.7%,　78.7%,　respectively.　The　inspection　accuracy　of　Kappa　coefficient　is　0.79,　0.74,　0.74,　0.76,　and　R2　is:　0.89,　0.88,　0.88,　0.85,　respectively.　It　can　be　seen　that　the　function　established　by　the　Fisher　discriminant　analysis　model　has　a　good　ability　to　detect　the　degree　of　pests　of　the　Phyllostachys　edulis,　and　the　discriminant　function　established　based　on　the　original　spectrum　of　the　canopy　has　the　best　detection　effect.　The　discriminant　function　established　based　on　the　original　spectrum　of　the　canopy　of　the　hyperspectral　data　was　used　to　detect　the　pest　degree　of　Phyllostachys　edulis　in　Yangmen　and　Tulong　Village　in　Wufang　Village,　Dagan　Town,　Shunchang　County,　Fujian　Province.　The　test　result　is　that　the　bamboo　forests　in　the　two　sample　areas　of　Shanghu　are　mainly　healthy,　and　the　pest　degree　of　the　two　sample　areas　of　Yangmen　is　mainly　moderate　and　severe.　Therefore,　based　on　UAV　hyperspectral　remote　sensing,　it　is　feasible　for　large-area　detection　of　Phyllostachys　edulis　pests.　The　method　and　results　can　provide　a　reference　for　the　exploration　of　pest　detection　and　contribute　theoretical　support　for　pest　detection　based　on　canopy　remote　sensing.　©　2021,　Peking　University　Press.　All　right　reserved.