Powdery　mildew　significantly　impacts　the　yield　of　natural　rubber　by　being　one　of　the　predominant　diseases　that　affect　rubber　trees.　Accurate,　non-destructive　recognition　of　powdery　mildew　in　the　early　stage　is　essential　for　the　cultivation　management　of　rubber　trees.　The　objective　of　this　study　is　to　establish　a　technique　for　the　early　detection　of　powdery　mildew　in　rubber　trees　by　combining　spectral　and　physicochemical　parameter　features.　At　three　field　experiment　sites　and　in　the　laboratory,　a　spectroradiometer　and　a　hand-held　optical　leaf-clip　meter　were　utilized,　respectively,　to　measure　the　hyperspectral　reflectance　data　(350–2500　nm)　and　physicochemical　parameter　data　of　both　healthy　and　early-stage　powdery-mildew-infected　leaves.　Initially,　vegetation　indices　were　extracted　from　hyperspectral　reflectance　data,　and　wavelet　energy　coefficients　were　obtained　through　continuous　wavelet　transform　(CWT).　Subsequently,　significant　vegetation　indices　(VIs)　were　selected　using　the　ReliefF　algorithm,　and　the　optimal　wavelengths　(OWs)　were　chosen　via　competitive　adaptive　reweighted　sampling.　Principal　component　analysis　was　used　for　the　dimensionality　reduction　of　significant　wavelet　energy　coefficients,　resulting　in　wavelet　features　(WFs).　To　evaluate　the　detection　capability　of　the　aforementioned　features,　the　three　spectral　features　extracted　above,　along　with　their　combinations　with　physicochemical　parameter　features　(PFs)　(VIs　+　PFs,　OWs　+　PFs,　WFs　+　PFs),　were　used　to　construct　six　classes　of　features.　In　turn,　these　features　were　input　into　support　vector　machine　(SVM),　random　forest　(RF),　and　logistic　regression　(LR),　respectively,　to　build　early　detection　models　for　powdery　mildew　in　rubber　trees.　The　results　revealed　that　models　based　on　WFs　perform　well,　markedly　outperforming　those　constructed　using　VIs　and　OWs　as　inputs.　Moreover,　models　incorporating　combined　features　surpass　those　relying　on　single　features,　with　an　overall　accuracy　(OA)　improvement　of　over　1.9%　and　an　increase　in　F1-Score　of　over　0.012.　The　model　that　combines　WFs　and　PFs　shows　superior　performance　over　all　the　other　models,　achieving　OAs　of　94.3%,　90.6%,　and　93.4%,　and　F1-Scores　of　0.952,　0.917,　and　0.941　on　SVM,　RF,　and　LR,　respectively.　Compared　to　using　WFs　alone,　the　OAs　improved　by　1.9%,　2.8%,　and　1.9%,　and　the　F1-Scores　increased　by　0.017,　0.017,　and　0.016,　respectively.　This　study　showcases　the　viability　of　early　detection　of　powdery　mildew　in　rubber　trees.　©　2024　by　the　authors.