Image-based symptom scoring of plant diseases is a powerful tool to associate resistance or susceptibility to collections of plant varieties or genotypes. Technological advances are enabling new imaging, image processing, and statistical analysis of processes developing over time. Several tools are available for the analysis of symptoms on leaves and fruits of larger crop plants but almost none for small genetic model plants such as Arabidopsis thaliana (Arabidopsis). Arabidopsis and the model fungus Botrytis cinerea (Botrytis) form a potent model pathosystem for the identification of signalling pathways conferring immunity against this broad host-range necrotrophic fungal pathogen. Here we present two strategies to assess the disease symptoms for severity and progression in time in excised Arabidopsis leaves. For visible light (Red Green Blue, RGB) color hue values, a pixel classification strategy and the random forest algorithm was used to establish necrotic, chlorotic and healthy pixel categories. Secondly, from chlorophyll fluorescence imaging (ChlF), the photosynthetic capacity value (QYmax) was used for pixel thresholding to define diseased areas and to calculate the diseased pixel areas per total leaf pixel area. Both RGB and ChlF strategies were used to track disease progression over time. This is a robust and sensitive method with which to detect symptom development in sensitive and resistant genetic backgrounds. A full methodological workflow, from plant culture to data analysis, is described.