Aiming　at　the　characteristics　of　small　targets,　many　interferents　and　inconspicuous　features　of　spore　images　of　wheat　powdery　mildew,　a　weight　adaptive　feature　fusion　model　is　proposed　based　on　SSD　network　structure　to　improve　the　accuracy　of　spore　detection.　Firstly,　a　feature　fusion　path　is　constructed　to　recursively　fuse　features　of　various　scales　from　deep　to　shallow,　and　at　the　same　time,　a　layer　of　feature　matrix　is　added　to　enhance　the　utilization　of　deep　and　shallow　features　by　the　network;　Secondly,　a　hybrid　attention　module　is　proposed,　which　redistributes　the　weights　of　features　adaptively　to　enhance　the　ability　of　extracting　network　context　information.　Finally,　the　k-means　algorithm　is　used　to　set　the　shape　of　the　prior　box,　which　effectively　improves　the　problem　that　it　is　difficult　to　manually　adjust　the　hyperparameter　of　the　neural　network.　The　AP　of　powdery　mildew　spores　was　91.17%,　Compared　with　the　classical　SSD　detection　method,　it　has　been　greatly　improved.　©　2022　SPIE.