An intelligent method for detection of small target fungal wheat spores based on an improved YOLOv5 with microscopic images.

Abstract

Wheat is significantly impacted by fungal diseases, which result in severe economic losses. These diseases result from pathogenic spores invading wheat. Rapid and accurate detection of these spores is essential for post-harvest contamination risk assessment and early warning. Traditional detection methods are time-consuming and labor-intensive, and difficult to detect small target spores in complex environments. Therefore, a YOLO-ASF-MobileViT detection algorithm is proposed to detect pathogenic wheat spores with varying sizes, shapes, and textures. Four types of common pathogenic wheat spores are used as the study object, including Fusarium graminearum, Aspergillus flavus, Tilletia foetida (sporidium maturum), and Tilletia foetida (sporidium immaturum). The Attentional Scale Sequence Fusion (ASF) is integrated into the original YOLOv5s to enhance the capture of small details in spore images and fuse multi-scale feature information of spores. Additionally, the Mobile Vision Transformer (MobileViT) attention mechanism is incorporated to enhance both local and global feature extraction for small spores. Experimental results show that the proposed YOLO-ASF-MobileViT model achieves an overall mAP@0.5 of 97.0%, outperforming advanced detectors such as TPH-YOLO (95.6%) and MG-YOLO (95.5%). Compared to the baseline YOLOv5s model, it improves the average detection accuracy by 1.6%, with a notable 4.3% increase in detecting small Aspergillus flavus spores (reaching 90.8%). The model maintains high robustness in challenging scenarios such as spore adhesion, occlusion, blur, and noise. This approach enables efficient and accurate detection of wheat fungal spores, supporting early contamination warning in post-harvest management.