Wheat　powdery　mildew　spore　detection　is　one　of　the　most　common　scenarios　in　the　field　of　agricultural　pest　and　disease　detection.　Since　wheat　powdery　mildew　spore　images　possess　the　characteristics　of　small　spore　targets,　easy　adhesion,　shallow　feature　dimension,　and　many　background　clutter,　it　leads　to　the　increased　difficulty　of　wheat　powdery　mildew　spore　detection.　By　analyzing　the　above　detection　difficulties,　a　YOLOv5　wheat　powdery　mildew　spore　detection　algorithm　based　on　attentional　feature　fusion　is　proposed　in　this　paper.　Improvements　are　made　on　the　basis　of　YOLOv5s.　Firstly,　an　attentional　mechanism-based　feature　fusion　module　AM-FF　(Attentional　Mechanisms-Feature　Fusion)　is　proposed　to　strengthen　the　fusion　of　features　at　different　scales,　which　enhances　the　learning　ability　of　network　contextual　association　by　autonomously　learning　and　selecting　the　optimal　features　through　the　attentional　mechanism.　Secondly,　in　order　to　enhance　the　shallow　features　and　small　target　detection　accuracy,　we　modify　the　original　network　structure　to　enhance　the　acquisition　of　shallow　features　and　add　fused　reinforced　shallow　features　in　the　neck　network　FPN　(Feature　Pyramid　Networks).　Finally,　the　SIoU　(SCYLLA-IoU)　loss　function　is　introduced　in　order　to　increase　the　consideration　of　vector　angle　in　the　boundary　box　regression　calculation,　which　effectively　improves　the　detection　accuracy.　Experiments　were　conducted　on　the　expert-labeled　wheat　powdery　mildew　spore　dataset,　and　the　final　result　of　the　algorithm　experiment　AP　(Average　Precision)　was　94.23%,　which　was　a　large　improvement　compared　with　the　original　YOLOv5　detection　algorithm　vertically　and　also　compared　with　the　algorithm　made　by　previous　authors　horizontally,　and　the　experimental　results　proved　the　superiority　of　the　proposed　method　in　this　paper.　©　2023　SPIE.