Hyperspectral imaging to characterize the vegetative tissue biochemical changes in response to water deficit conditions in sorghum (Sorghum bicolor)
Hyperspectral imaging has been used to determine plant stress status. However, the biological interpretation of the spectral changes remain less explored. This can be addressed by building associations between stress-induced biochemical changes and variations in spectral reflectance. To this end, we...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1515998/full |
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| Summary: | Hyperspectral imaging has been used to determine plant stress status. However, the biological interpretation of the spectral changes remain less explored. This can be addressed by building associations between stress-induced biochemical changes and variations in spectral reflectance. To this end, we tested spectral response of sorghum brown midrib (bmr) mutants under varying water stress levels using hyperspectral imaging (650–1650 nm). The bmr mutants have reduced lignin concentrations in their vegetative tissue which was reflected as spectral differences. Under water stress, the spectral signatures diverged more between the wildtype and mutants compared to control conditions. The genotype-dependent variation in spectral trends under water limitation was associated with differential sensitivity of the genotypes to water-limitation induced changes in energy density. We show that the energy density and relative water content of the plant tissue can be estimated accurately from spectral reflectance. To reduce the computational load, LASSO was used to obtain 22 wavelengths across the camera spectral range (650–1650 nm) in dried samples, to accurately predict energy density comparable to PLSR estimates. The reported wavelengths represent a useful screening tool for fast and reliable calorimetric estimations in bioenergy breeding programs. |
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| ISSN: | 1664-462X |