Towards early automated detection of pre-symptomatic pathogen risk: mitigating the impact of airborne fungal plant pathogensTools Brittain, I.B. (2018) Towards early automated detection of pre-symptomatic pathogen risk: mitigating the impact of airborne fungal plant pathogens. PhD thesis, University of Nottingham.
AbstractSpore trapping and subsequent real-time PCR assays have been used to quantify concentrations of air dispersed fungal spores in many pathosystems. Concerns governing their application include the time it takes to achieve a diagnostic result, reliability in their application and correctly interpreting spore data. Research in this thesis builds on current methodologies, applying spore trapping with novel molecular techniques including real-time PCR and loop mediated isothermal amplification (LAMP), and trials the first fully automated spore detection system. Initially, traditional sticky tape Burkard spore traps and conventional qPCR techniques were implemented for the first time in the UK to monitor the dispersal characteristics of Hymenoscyphus fraxinea ascospores to help inform risk. Findings identified that large eruptions of spores can be detected during the early hours of the morning between 05:00 and 08:00 am primarily between the months of July and August, reflecting similar findings observed across Europe. A number of simple spore disruption techniques were then developed that were able to provide rapid detection of Puccinia striiformis, P. triticina and Z. tritici spores in spore trap samples using LAMP which was comparable to qPCR in detection efficiency. Infection periods were then identified using a disease risk model in combination with spore data, disease incidence and an understanding of their latency periods. This highlighted that an improvement in the disease risk model used could be achieved following the incorporation of spore data. The first fully automated spore detection system was later investigated and provided automated un-attended detection of P. striiformis and P. triticina spores. Finally, a rapid in-field detection method using LAMP was developed to investigate the latent development of Z. tritici in wheat leaves, that may be initiated by splash dispersed pycnidiospores. This method was able to provide an indication of the latency stage of Z. tritici in individual wheat leaves and as a result could help inform fungicide application. Molecular techniques are now rapid, simple and robust for incorporation into automated systems for deploying in-field. With a more translational approach between engineers and researchers emerging, the ability to provide fully automated and rapid smart sensor based networks capable of providing valuable data in real-time for informing disease management decisions is becoming more realistic.
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