P-675 - Automated spore sampling to track spatiotemporal variation of spore prevalence in vineyards/orchards and comparison against weather-based predictions

Abstract Text: Around 20% of major global crops are lost to pests and pathogens, for which monitoring is necessary to guide sustainable and effective control. Identification and quantification of spores within susceptible fields is vital for predicting disease outbreaks and managing crops accordingly. Manual sampling and molecular analysis, however, incur significant cost, are labour intensive and introduce latency, causing many practitioners to rely instead on weather-based predictions, which are incredibly variable in their reliability.
In this project, we are developing automated molecular monitoring devices to address the uncertainties around spore sampling which lead to less effective deployment and less reliable disease prediction in the field. These portable devices collect spores, extract nucleic acids and carry out targeted LAMP assays for automated and remote disease monitoring. To validate this, we are first investigating how the spatial configuration of spore samplers affects results. This information could be used to optimise the deployment of spore samplers for disease management (IPM) systems. The results will guide the deployment of novel automated technologies in further field trials. Preliminary comparisons demonstrate that in-field sampling generates data markedly distinct from weather-based predictions, suggesting that development of automated devices for in-field molecular monitoring will significantly enhance the accuracy of disease monitoring.