In　recent　years,　there　have　been　significant　development　in　deep　learning　technology,　which　has　also　promoted　the　development　of　image　segmentation.　Wheat　powdery　mildew　is　a　common　crop　disease,　which　is　very　harmful　to　crops.　The　segmentation　task　of　wheat　powdery　mildew　spore　images　is　important　for　the　target　spores　identification　and　spores　counting.　In　this　paper,　an　improved　framework　based　on　U-Net　is　proposed,　comparing　with　original　U-Net,　we　add　the　pyramid　pooling　module　after　the　1024-channel　feature　map　in　the　down-sampling　part　to　extract　different　sizes　pooling　feature　and　fuse　into　a　global　feature　map,　and　adjust　some　skip　connections　in　original　U-Net　to　obtain　more　features　that　are　effective　for　spore　images　segmentation,　experiment　shows　that　the　improved　U-Net　structure　has　better　segmentation　performance　than　U-Net,　and　the　segmentation　miou　(mean　intersection　over　union)　has　reached　91.4%　in　wheat　powdery　mildew　spore　image　dataset,　which　proves　that　our　proposed　architecture　is　effective　and　competitive　in　the　wheat　powdery　mildew　spore　segmentation　task.　©　Published　under　licence　by　IOP　Publishing　Ltd.