Abstract:Cucumber downy mildew significantly impacts yield and quality. Quantitative characterization of the infection structures of the cucumber downy mildew pathogen in microscopic images is crucial for assessing the degree of pathogen infection and analyzing infection behavior. To address challenges such as varying sizes and overlapping of different infection structures, the quantitative characterization of cucumber downy mildew pathogen infection structures was implemented based on an instance segmentation network. Firstly, an in?situ stained microscopic image dataset of the cucumber downy mildew pathogen was constructed. Secondly, an instance segmentation model for microscopic images of the cucumber downy mildew pathogen, DS?YOLO v8s, was developed, significantly improving the detection and segmentation capabilities of infection structures. Thirdly, a quantitative characterization system for the infection structures of the cucumber downy mildew pathogen was established by using morphological analysis methods. Finally, a quantitative characterization system for the infection structures of the cucumber downy mildew pathogen was designed and implemented. Experimental results demonstrated that the proposed DS?YOLO v8s model achieved mAP_box@.5 of 89.5% and mAP_mask@.5 of 80.1%. In terms of morphological distribution, the perimeters of spores, sporangia, branching structures, and hyphae were concentrated in 40~90 pixels, 200~350 pixels, 0~1 500 pixels, and 0~800 pixels, respectively;their areas were concentrated in 100~400 square pixels, 3 000~6 000 square pixels, 0~20 000 square pixels, and 0~6 000 square pixels, respectively. The circularity of spores and sporangia was concentrated in 0.68~0.78 and 0.70~0.85, respectively, and the hyphal length was concentrated in 0~300 pixels. The quantitative characterization system for cucumber downy mildew pathogen infection structures implemented functions such as user registration and login, image segmentation, and quantitative characterization. The research result can provide a reusable technical pathway for the early precise monitoring and intelligent control of cucumber downy mildew, and lay a methodological foundation for high?throughput phenotypic screening in disease?resistant breeding and the establishment of a standardized evaluation system for disease phenomics.