Abstract
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.
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Claims 5
Claim · model-written summary
Hyperspectral imaging can accurately estimate Fusarium infection in wheat spikes and kernels and DON content in grain, according to the review.
- Taxon
- wheat
Triticum aestivume0 - Interactor
- Fusarium graminearum
_on_Corn_(Zea_mays)_at_Waimea%2C_Hawaii.jpg?width=60)
Fusarium graminearume0pathogen - Outcome
- infection in spikes and kernelse1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
_on_Corn_(Zea_mays)_at_Waimea%2C_Hawaii.jpg?width=500)
Verbatim
e0“Fusarium head blight (FHB) of wheat ( Triticum aestivum L.) is primarily caused by the fungal pathogen Fusarium graminearum”
e1“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”
Claim · model-written summary
Waveband-specific spectral approaches do not generalize well to new data in wheat FHB phenotyping.
- Taxon
- wheatTriticum aestivume0
- Interactor
- Fusarium graminearumFusarium graminearume0pathogen
- Outcome
- generalize well to new datae2
- Direction
- do not generalizee2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Fusarium head blight (FHB) of wheat ( Triticum aestivum L.) is primarily caused by the fungal pathogen Fusarium graminearum”
e2“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”
Claim · model-written summary
Hyperspectral imaging is significantly more resource-intensive than RGB or multispectral cameras, limiting its practicality for most breeding programs.
- Taxon
- wheatTriticum aestivume0
- Outcome
- practicality for most breeding programse2
- Direction
- limitinge2
- Comparator
- RGB or multispectral camerase2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Fusarium head blight (FHB) of wheat ( Triticum aestivum L.) is primarily caused by the fungal pathogen Fusarium graminearum”
e2“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”
Claim · model-written summary
Spectral-index and deep-learning digital image phenotyping shows the most potential for use in wheat breeding.
Verbatim
e3“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”
Claim · model-written summary
Widespread adoption of these phenotyping techniques depends on standardized imaging protocols and generalization across genotypes.
- Taxon
- wheatTriticum aestivume0
- Outcome
- widespread adoption of these techniquese4
- Qualifier
- standardized imaging protocolse4
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Fusarium head blight (FHB) of wheat ( Triticum aestivum L.) is primarily caused by the fungal pathogen Fusarium graminearum”
e4“However, the widespread adoption of these techniques will depend on standardized imaging protocols, robust generalization across diverse genotypes, and effective integration into breeding pipelines”