Abstract
Rice blast is an important fungal disease caused by Magnaporthe oryzae, and the air-borne disease can erupt in a short time, causing large-scale yield losses. Rapid and accurate detection of rice blast spores in microscopic images is crucial for monitoring spore density in the field, guiding farmers in timely pesticide application for effective prevention and control. However, there is a lack of an efficient and accurate method for detecting rice blast spores under complex field conditions currently. Traditional machine learning algorithms are better suited for detecting a single category under controlled conditions and rely on manual feature extraction, which limits their transferability. Therefore, this paper proposed a novel Rice Blast Spore Detector based on You Only Look Once algorithm - YOLO-RBSD, which can effectively detect rice blast spores and three categories of impurity particles in microscopic images against complex field backgrounds. Firstly, the detector introduced the triplet attention mechanism on the basis of YOLOv8s, enhancing its ability to capture cross-dimensional features. In addition, in order to further reduce model parameters and enhance model speed, the detector replaced partial CSPDarknet53 to 2-Stage FPN (C2f) modules with Depthwise Separable C2f (DSC2f) modules, realizing the optimal model structure design. Finally, YOLO-RBSD achieved a mean average precision (0.5) of 96.1% and a macro F1 score of 92.6%, processing 125 images per second, surpassing the mainstream models in both speed and accuracy. As an effective and lightweight tool, YOLO-RBSD provides a strong foundation for automated rice blast spore density monitoring in the field. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13007-026-01526-5.