Phenotypic approaches for Fusarium head blight resistance in wheat: A review

Abstract Fusarium head blight (FHB) of wheat ( Triticum aestivum L.) is primarily caused by the fungal pathogen Fusarium graminearum . This disease can cause significant economic loss due to decreasing yield, reducing seed quality, and the production of deoxynivalenol (DON); therefore, resistance to the disease is a primary concern for breeders. Phenotyping methods largely depend on the resistance mechanism being evaluated, but traditional approaches are often time‐consuming, subjective, and largely inaccurate. This review explores and contrasts traditional and modern phenotypic methods for various FHB resistance components. Digital image‐based phenotyping spans low‐cost RGB (red, green, blue) (i.e., Bayer array) sensors, multispectral sensors, and hyperspectral sensors. Across these sensors, approaches using spectral indices or deep learning have shown strong promise for detecting and classifying infection in both wheat spikes and kernels. Hyperspectral imaging has been largely explored and can be used to accurately estimate infection in spikes and kernels, as well as estimate DON content in the grain, using spectral indices or models input with specific wavebands. However, waveband‐specific approaches do not generalize well to new data, and hyperspectral imaging is significantly more resource‐intensive than RGB or multispectral cameras, limiting its practicality for most breeding programs. Phenotypic approaches using spectral indices and/or deep learning on digital images show the most potential for use in wheat breeding, due to their scalability and low cost. However, the widespread adoption of these techniques will depend on standardized imaging protocols, robust generalization across diverse genotypes, and effective integration into breeding pipelines.