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

ABSTRACT:

Background

The early and specific detection of abiotic and biotic stresses, particularly their combinations, is 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 plant stress states. Monomodal detection of certain stressors is usually based on non-specific/indirect features and therefore is 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 potentially provide synergistic information to increase cross-specificity in plant disease detection when image data are fused at the pi

SUBMITTER: Bethge HL 

PROVIDER: S-EPMC11572093 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature