Tracking Lesion Growth in the Field: Imaging and Deep Learning Reveal Components of Quantitative Resistance

Measuring individual components of pathogen reproduction is key to understanding mechanisms underlying rate-reducing quantitative resistance (QR). Simulation models predict that lesion expansion plays a key role in seasonal epidemics of foliar diseases, but measuring lesion growth with sufficient precision and scale to test these predictions under field conditions has remained impractical.

We used deep learning-based image analysis to track 6889 individual lesions caused by Zymoseptoria tritici on 14 wheat cultivars across two field seasons, enabling 27,218 precise and objective measurements of lesion growth in the field.

Lesion appearance traits reflecting specific interactions between particular host and pathogen genotypes were consistently associated with lesion growth, whereas overall effects of host genotype and environment were modest. Both host cultivar and cultivar-by-environment interaction effects on lesion growth were highly significant and moderately heritable ( h ² ≥ 0.40). After excluding a single outlier cultivar, a strong and statistically significant association between lesion growth and overall QR was found.

Lesion expansion appears to be an important component of QR to STB in most—but not all—wheat cultivars, underscoring its potential as a selection target. By facilitating the dissection of individual resistance components, our approach can support more targeted, knowledge-based breeding for durable QR.