https://www.selleckchem.com/products/ly2874455.html A case study of potato late blight survey data from across Great Britain was used for proof of concept. The results showed that Gaussian mixture model had the highest forecast accuracy at 97.0%, followed by one-class k-means at 96.9%. There was added value in combining the algorithms in an ensemble to provide a more accurate and robust forecasting tool that can be tailored to produce region-specific alerts. The techniques used here can easily be applied to outbreak data from other crop pathosystems to derive tools for agricultural decision support.Eyespot, caused by the related fungal pathogens Oculimacula acuformis and O. yallundae, is an important cereal stem-base disease in temperate parts of the world. Both species are dispersed mainly by splash-dispersed conidia but are also known to undergo sexual reproduction, yielding apothecia containing ascospores. Field diagnosis of eyespot can be challenging, with other pathogens causing similar symptoms, which complicates eyespot management strategies. Differences between O. acuformis and O. yallundae (e.g., host pathogenicity and fungicide sensitivity) require that both be targeted for effective disease management. Here, we develop and apply two molecular methods for species-specific and mating-type (MAT1-1 or MAT1-2) discrimination of O. acuformis and O. yallundae isolates. First, a multiplex PCR-based diagnostic assay targeting the MAT idiomorph region was developed, allowing simultaneous determination of both species and mating type. This multiplex PCR assay was successfully applied to type a global collection of isolates. Second, the development of loop-mediated isothermal amplification (LAMP) assays targeting β-tubulin sequences, which allow fast ( less then 9 min) species-specific discrimination of global O. acuformis and O. yallundae isolates, is described. The LAMP assay can detect very small amounts of target DNA (1 pg) and was successfully applied in planta.