SporaScan: cost-effective, high-precision leaf-disc disease severity assessment for grapevine downy mildew.

BACKGROUND

Accurate assessment of disease severity is essential for evaluating fungicide performance and breeding disease-resistant crop varieties. Manual scoring of infection on individual leaf discs is labor-intensive and variable, while traditional computer vision methods require manual parameter tuning and lack robustness. Existing deep learning approaches often struggle to simultaneously localize leaf discs and accurately segment disease symptoms, limiting their practical application.

RESULTS

We developed SporaScan, an automated pipeline combining YOLO v8n for leaf disc localization, Mobile SAM for background removal, and UNet for sporulation segmentation. It achieved high accuracy (mAP@50 >99%, mIoU@50 >96%), with background removal reducing misclassification (0.21% for sporulation and 2.75% for leaf discs). Severity estimates showed strong agreement with manual annotations (R2 = 0.99). In a blind test, technicians selected SporaScan as superior in 37.2% of cases, manual annotation in 26.2%, and equal performance in 36.6% (P < 0.001). Compared with Mask R-CNN, the sequential design improved accuracy and efficiency.

CONCLUSION

These results demonstrate that SporaScan provides an efficient and practical approach for automated assessment of downy mildew severity, supporting applications in disease evaluation, breeding, and fungicide assessment (http://116.10.197.212:9060/segment/#/). © 2026 Society of Chemical Industry.