Image-based symptom tracking to decompose quantitative resistance in the field
Quantitative disease resistance (QR) is a complex, dynamic trait that is most reliably quantified in field-grown crops. Traditional disease assessments offer limited potential to disentangle the contributions of different components to overall QR. Yet, a better functional understanding of QR could greatly support a more targeted, knowledge-based selection for QR and improve predictions of seasonal epidemics. We have developed a simple, affordable, and easy-to-operate imaging procedure for in-field acquisition of wheat leaf image sequences. The development of Septoria tritici blotch and leaf rusts was monitored over time via robust methods for symptom detection and segmentation, image registration, symptom tracking, and leaf- and symptom characterization. Our pilot data enabled the monitoring of 13,538 necrotic lesions on 300 leaves, which provided 72,005 lesion property measurements. Contrasting patterns in lesion numbers and lesion expansion dynamics were observed across wheat cultivars. The number of separate infection events and average lesion size contributed to varying degrees to overall disease intensity, possibly indicating distinct mechanisms of QR. The proposed methodology enables rapid, non-destructive, and reproducible measurement of several key epidemiological parameters under natural field conditions. Such data can support decomposition and functional understanding of QR as well as the parameterization, fine-tuning, and validation of epidemiological models.