Evidence of a trans-kingdom plant disease complex between a fungus and plant-parasitic nematodes.

ABSTRACT: Disease prediction tools improve management efforts for many plant diseases. Prediction and downstream prevention demand information about disease etiology, which can be complicated for some diseases, like those caused by soilborne microorganisms. Fortunately, the availability of machine learning methods has enabled researchers to elucidate complex relationships between hosts and pathogens without invoking difficult-to-satisfy assumptions. The etiology of a destructive plant disease, Verticillium wilt of mint, caused by the fungus Verticillium dahliae was reevaluated with several supervised machine learning methods. Specifically, the objective of this research was to identify drivers of wilt in commercial mint fields, describe the relationships between these drivers, and predict wilt. Soil

SUBMITTER: Wheeler DL 

PROVIDER: S-EPMC6373923 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature