Abstract: Wheat scab is one of the most serious diseases that threaten global wheat production. The primary causal agents can be from the fungi Fusarium graminearum and Fusarium asiaticum. The Fusarium spores can be disseminated in the transmission and infection of wheat scab. Consequently, the early and precise identification of pathogen spore concentrations is of paramount importance to prompt the detection of wheat scab, particularly for the high wheat yield and quality, as well as food safety. Conventional techniques, such as microscopic observation and medium isolation, have posed a great challenge to the rapid response and the transmission of large-scale crop disease, due to time-consuming and sophisticated technical expertise. Furthermore, there are missed and erroneous detections of densely distributed small targets in scab spore images. In this study, a small target detection model, YOLOv8-FECA was developed to overcome this challenge. Firstly, a small target detection layer with a feature map size of 160×160 was added to the original YOLOv8 baseline model. The network was then improved to capture the semantic information of small targets, in order to enhance the accuracy of feature description. Secondly, a focus attention mechanism, FECA, was designed to combine the CBAM and ECA modules. This module was then added at the neck end of the network to reduce information loss during downsampling. Ultimately, Wise-IoU Loss and DFL Loss were integrated as the regression loss for the bounding box, in order to enhance the convergence and the precision of bounding box estimation. The experimental results demonstrate that the small target detection layer was added to introduce the focus attention mechanism, compared with the original YOLOv8 baseline model. Wise-IoU Loss and DFL were also introduced after Loss improvement. The average detection accuracy of the new YOLOv8-FECA on spore data set mAP@0.5 and mAP@0.5:0.95 increased by 4.3 percentage points and 6.3 percentage points, respectively, compared with the benchmark model Yolov8n. In terms of performance, the YOLOv8-FECA model was improved in both accuracy and recall, compared with the YOLOv8n. The improved model demonstrated an elevated degree of accuracy and comprehensiveness in identifying the wheat scab spores, with an increase of 2.6 percentage points in Precision and 3.8 percentage points in Recall. There was a decrease in the frame rate per second (FPS) of YOLOv8-FECA, compared with YOLOv8n (from 134 to 106). Nevertheless, a high detection speed was achieved among performance indicators. The YOLOv8-FECA successfully improved the detection accuracy during real-time detection, where mAP @ 0.5 reached 96.8 %. The robustness of the model was verified as well. The results demonstrated that the accurate detection of spores was achieved even in challenging scenarios, such as the high spore density and low light conditions. Moreover, compared with the prevalent target models, including YOLOv5s, YOLOv7-tiny, SSD network, and the exemplar two-stage Faster-R-CNN, YOLOv8-FECA exhibited superior performance, in terms of mAP. The new model was superior in detecting small targets, such as scab spores. This finding can provide technical support to the automatic detection of wheat scab spores in the field, especially for the early warning of scab.