Automated image registration of RGB, hyperspectral and chlorophyll fluorescence imaging data

The early and specific detection of abiotic and biotic stress factors and particularly their combinations are a major challenge for maintaining and increasing plant productivity in sustainable agriculture under changing environmental conditions. Optical imaging techniques enable cost-efficient and non-destructive quantification of the stress state of plants Monomodal detection of certain stressors are usually based on non-specific/indirect features and therefore commonly limited in their cross-specificity to other stressors. The fusion of multi-domain sensor systems can provide more potentially discriminative features for machine learning models and presumably also provide synergistic information to increase cross-specificity in plant disease detection.