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Cucumber downy mildew is caused by the spores of cucumber downy mildew from Cuba through infection, which seriously affects the quality and yield of cucumber. The number of the spores is closely related to the severity of the disease. Accordingly, there urgently needs to establish a rapid, simple and efficient quantitative detection method for the spores of cucumber downy mildew, in order to explore the way forward to achieve the control of cucumber downy mildew. Based on YOLO v5 model, an exploratory model was proposed for quantitative detection of cucumber downy mildew spores by Faster-NAM-YOLO. Firstly, a feature extraction module C3_ Faster was proposed, which was used to replace the C3 module in YOLO v5, which effectively reduced the calculation amount of model parameters and the depth of the model, and also improved the detection speed and accuracy of cucumber downy mildew spores. Secondly, the NAMAttention module was added to the backbone network and also improved the model's feature extraction ability and computational efficiency by applying weight sparsity penalty to suppress insignificant weights. In the end, the quantitative detection of the spores caused by cucumber downy mildew was realized. Faster-NAM-YOLO model on the test set [email protected] and [email protected]:0.95 reached 95.80% and 60.90%, respectively, to compare with the original YOLO model. It can be seen that the final results were increased by 1.80 percentage points and 1.20 percentage points, respectively, reducing the model size and FLOPs of the original YOLO v5 model by 5.27M and 1.49×1010, respectively. It was found that Faster-NAM-YOLO had significant advantages in detection accuracy, model size, FLOPs, and inference time compared with single stage target detection models such as YOLO v3, THP-YOLO v5, YOLO v7, YOLO v8, Faster RCNN, and SSD. In addition, under the comparison for the three different resolution scales of 1200 pixels×1200 pixels, 1500 pixels×1500 pixels and 1800 pixels×1800 pixels, as well as the different specifications and the varied amount of images, which suggested that the Faster-NAM-YOLO model was further validated to have strong robustness and generalization ability. The research result not only provided a more accurate basis for early online monitoring of cucumber downy mildew, but also laid a foundation for further exploring the relationship between the dynamic changes of spore number and morphological characteristics and the severity of the disease.