In the past, the effect of pathogens on plant health was primarily done by visually scoring traits. As this approach is time consuming, has a low resolution and is prone to human bias, phenotyping has evolved towards phenomics, which refers to the characterization of plant phenotypes via the acquisition and analysis of high-dimensional phenotypic data. While phenomics is starting to become commonplace in plant breeding and abiotic stress experiments, its use in plant pathology still remains in its infancy. At LAMP, we developed the PathoViewer, a plant phenotyping system, intended for high-resolution multispectral imaging in a highly controllable environment. This permits to monitor the effect of a novel agrochemical or pathogen on a large numbers of seedlings and small plants in their natural or in an extreme environment. Due to the highly automated sensor-to-plant principle, the spread of a disease or the effect of an agrochemical or stressor can be traced throughout the plant in time. In Western-Europe, F. graminearum and F. poae are the predominant species present in symptomatic ears of Fusarium Head Blight (FHB). While F. graminearum is highly virulent, F. poae as a weak pathogen is unable to cause FHB symptoms so its omnipresence in symptomatic ears is quite unexpected. We used a time series co-inoculation strategy combined with a multispectral phenomics approach on detached leaves and on wheat ears to analyze the interaction between both species. To this end, we also tagged F. graminearum with a GFP and F. poae with a RFP. Combined with gene expression data, we constructed a comprehensive dataset that was used to unravel which underlying mechanisms determine the outcome of the F. graminearum and F. poae interaction. This led us to hypothesize that the early induction of SA- and JA- related defenses by F. poae hampers a subsequent F. graminearum infection.