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To achieve rapid and accurate identification of fusarium head blight on wheat spikelets in complex field background, a wheat fusarium head blight image dataset comprising a total of 640 images across three growth stages: flowering, grain filling, and ripening of winter wheat was constructed. Additionally, a wheat spikelet fusarium head blight recognition method based on an improved YOLO v8s model was proposed. Firstly, using the omni-dimensional dynamic convolution (ODConv) to replace the standard convolution in the backbone network enhanced the network’s extraction of features from target regions and suppressed interference from cluttered background information. Secondly, an improved Efficient RepGFPN feature fusion network was utilized in the neck network to integrate low-level features with high-level semantic information, enabling the model to extract richer feature information. Lastly, the enhanced intersection over union (EIoU) loss function was employed instead of the complete intersection over union (CIoU) loss function to accelerate model convergence speed and further improve model accuracy, thus achieving rapid and accurate identification of fusarium head blight on wheat spikelets. Model validation on a self-built dataset revealed that the improved model (OCE-YOLO v8s) achieved a detection accuracy of 98.3% for fusarium head blight on wheat spikelets, which was an improvement of 2 percentage points compared with the original model. Compared with Faster R-CNN, CenterNet, YOLO v5s, YOLO v6s, and YOLO v7 models, the OCE-YOLO v8s model achieved improvements of 36 percentages, 25.7 percentages, 2.1 percentages, 2.6 percentages, and 3.9 percentages, respectively. The OCE-YOLO v8s model effectively met the requirements for precise detection of fusarium head blight on wheat spikelets and could provide valuable insights for real-time monitoring of crop diseases and pests in complex backgrounds of field environments.