Leaf- and canopy-level hyperspectral sensing of wheat-Fusarium head blight-Trichoderma gamsii interactions

Fusarium head blight (FHB) is a major mycotoxigenic disease of wheat, causing yield and quality losses and deoxynivalenol contamination. Rapid, non-destructive tools are needed to detect FHB, monitor wheat physiological responses, and evaluate sustainable management strategies, including biological control agents. Although vegetation spectroscopy is widely used for high-throughput phenotyping, most spectral studies focus on binary disease detection, while the capacity of hyperspectral data to capture concurrent host–pathogen–biocontrol responses across leaf and canopy scales remains underexplored. Here, we tested a full-range (400–2400 nm) hyperspectral phenotyping framework to track early interactions among winter wheat, FHB, and Trichoderma gamsii T6085. Two cultivars, Bingo and Rebelde, with higher and lower FHB susceptibility, respectively, were treated with a chemical fungicide (Chem) or T. gamsii T6085 (Bioc) under FHB pressure. Leaf- and canopy-level spectra were acquired at 2, 5, and 14 days post-inoculation, alongside gas exchange, water status, and chlorophyll measurements. Permutational multivariate analysis of variance (PERMANOVA) tested whole-spectrum effects, partial least squares discriminant analysis (PLS-DA) explored class separability, and partial least squares regression (PLSR) estimated physiological traits. PERMANOVA detected genotype × inoculation × treatment interactions from 5 days post-inoculation at leaf and canopy levels. PLS-DA revealed treatment- and cultivar-dependent spectral fingerprints, but overall low-to-fair validation performance indicates that these class-specific patterns should be interpreted as exploratory and not as evidence of operational treatment discrimination. PLSR provided high accuracy for chlorophyll content and osmotic potential, moderate accuracy for CO2-assimilation traits, and poor accuracy for transpiration and leaf water potential. While the workflow is scalable as an experimental and analytical framework, its operational deployment will require broader validation across sites, seasons, cultivars, disease-pressure conditions, and sensing platforms.