Asparagus stem blight is a devastating crop disease, and the early detection of its pathogenic spores is essential for effective disease control and prevention. However, spore detection is still hindered by complex backgrounds, small target sizes, and high annotation costs, which limit its practical application and widespread adoption. To address these issues, a semi-supervised spore detection framework is proposed for use under complex background conditions. Firstly, a difficulty perception scoring function is designed to quantify the detection difficulty of each image region. For regions with higher difficulty scores, a masking strategy is applied, while the remaining regions are adversarial augmentation is applied to encourage the model to learn from challenging areas more effectively. Secondly, a Gaussian Mixture Model is employed to dynamically adjust the allocation threshold for pseudo-labels, thereby reducing the influence of unreliable supervision signals and enhancing the stability of semi-supervised learning. Finally, the Wasserstein distance is introduced for object localization refinement, offering a more robust positioning approach. Experimental results demonstrate that the proposed framework achieves 88.9% mAP50 and 60.7% mAP50–95, surpassing the baseline method by 4.2% and 4.6%, respectively, using only 10% of labeled data. In comparison with other state-of-the-art semi-supervised detection models, the proposed method exhibits superior detection accuracy and robustness. In conclusion, the framework not only offers an efficient and reliable solution for plant pathogen spore detection but also provides strong algorithmic support for real-time spore detection and early disease warning systems, with significant engineering application potential.

Asparagus stem blight, also known as “asparagus cancer”, is a serious plant disease with a regional distribution. The widespread occurrence of the disease has had a negative impact on the yield and quality of asparagus and has become one of the main problems threatening asparagus production. To improve the ability to accurately identify and localize phenotypic lesions of stem blight in asparagus and to enhance the accuracy of the test, a YOLOv8-CBAM detection algorithm for asparagus stem blight based on YOLOv8 was proposed. The algorithm aims to achieve rapid detection of phenotypic images of asparagus stem blight and to provide effective assistance in the control of asparagus stem blight. To enhance the model’s capacity to capture subtle lesion features, the Convolutional Block Attention Module (CBAM) is added after C2f in the head. Simultaneously, the original CIoU loss function in YOLOv8 was replaced with the Focal-EIoU loss function, ensuring that the updated loss function emphasizes higher-quality bounding boxes. The YOLOv8-CBAM algorithm can effectively detect asparagus stem blight phenotypic images with a mean average precision (mAP) of 95.51%, which is 0.22%, 14.99%, 1.77%, and 5.71% higher than the YOLOv5, YOLOv7, YOLOv8, and Mask R-CNN models, respectively. This greatly enhances the efficiency of asparagus growers in identifying asparagus stem blight, aids in improving the prevention and control of asparagus stem blight, and is crucial for the application of computer vision in agriculture.