Abstract
Immediate detection and prediction tools for interactive stresses are essential to avoid yield losses. This study investigated the capability of hyperspectral (HSI) and chlorophyll fluorescence imaging (CFI) to characterize individual and interactive frost (−4 °C) and drought (40% soil moisture content) stress responses at the booting stage of wheat under controlled environmental conditions. HSI full range reflectance (280–2500 nm), CFI, and traditional responses (leaf water content, photosynthesis, and enzymatic activity) were measured from flag leaves. The potentiality of HSI to detect cellular damage in terms of enzymatic activity was explored by developing partial least square regression (PLSR) models, and spectral indices were calculated to characterize the responses to individual and combined stresses. Reflectance in 360, 700, 1400, 1900, and 2460 nm wavebands showed the most important variables in developing PLSR models for enzymes. Superoxide dismutase, peroxidase, and ascorbate peroxidase showed the highest R2c (0.99, 0.95, and 0.98), R2v (0.94, 0.90, 0.93), and the ratio of prediction to deviation (4.04, 3.06, and 3.85). CFI decreased under individual frost and combined frost and drought stresses. Ratio vegetation stress index (RVSI), green difference vegetation index (GDVI), difference vegetation index (DVI), and normalized difference water index (NDWI) were the most important variables and could be used to detect combined frost and drought by determining enzymatic activity and reactive oxygen species (ROS), verified by PLSR models. Spectral indices and enzyme activities showed a strong correlation in determining yield losses. Hence, HSI and CFI techniques successfully detected combined frost and drought stresses for rapid quantification.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 8
Claim · model-written summary
Five reflectance wavebands were most important for enzyme PLSR models.
- Taxon
- wheat
Triticum aestivumt0 - Outcome
- most important variables in developing PLSR models for enzymese0
- Value
- 360, 700, 1400, 1900, and 2460 nme0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Reflectance in 360, 700, 1400, 1900, and 2460 nm wavebands showed the most important variables in developing PLSR models for enzymes.”
t0“wheat”
Claim · model-written summary
Superoxide dismutase had model calibration and validation R2 of 0.99 and 0.94 and prediction to deviation ratio 4.04.
- Taxon
- wheatTriticum aestivumt0
- Gene
- Superoxide dismutasee0
- Outcome
- R2ce0
- Value
- 0.99e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Superoxide dismutase, peroxidase, and ascorbate peroxidase showed the highest R2c (0.99, 0.95, and 0.98), R2v (0.94, 0.90, 0.93), and the ratio of prediction to deviation (4.04, 3.06, and 3.85).”
t0“wheat”
Claim · model-written summary
Peroxidase had model calibration R2 of 0.95.
- Taxon
- wheatTriticum aestivumt0
- Gene
- peroxidasee0
- Outcome
- R2ce0
- Value
- 0.95e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Superoxide dismutase, peroxidase, and ascorbate peroxidase showed the highest R2c (0.99, 0.95, and 0.98), R2v (0.94, 0.90, 0.93), and the ratio of prediction to deviation (4.04, 3.06, and 3.85).”
t0“wheat”
Claim · model-written summary
Ascorbate peroxidase had model calibration R2 of 0.98.
- Taxon
- wheatTriticum aestivumt0
- Gene
- ascorbate peroxidasee0
- Outcome
- R2ce0
- Value
- 0.98e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Superoxide dismutase, peroxidase, and ascorbate peroxidase showed the highest R2c (0.99, 0.95, and 0.98), R2v (0.94, 0.90, 0.93), and the ratio of prediction to deviation (4.04, 3.06, and 3.85).”
t0“wheat”
Claim · model-written summary
Chlorophyll fluorescence imaging decreased under individual frost stress.
- Taxon
- wheatTriticum aestivumt0
- Treatment
- individual froste0
- Outcome
- CFIe0
- Direction
- decreasede0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“CFI decreased under individual frost and combined frost and drought stresses.”
t0“wheat”
Claim · model-written summary
Chlorophyll fluorescence imaging decreased under combined frost and drought stress.
- Taxon
- wheatTriticum aestivumt0
- Treatment
- combined frost and drought stressese0
- Outcome
- CFIe0
- Direction
- decreasede0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“CFI decreased under individual frost and combined frost and drought stresses.”
t0“wheat”
Claim · model-written summary
RVSI, GDVI, DVI and NDWI were useful for detecting combined stress through enzyme activity and ROS.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- detect combined frost and droughte0
- Comparator
- most important variablese0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Ratio vegetation stress index (RVSI), green difference vegetation index (GDVI), difference vegetation index (DVI), and normalized difference water index (NDWI) were the most important variables and could be used to detect combined frost and drought by determining enzymatic activity and reactive oxygen species (ROS), verified by PLSR models.”
t0“wheat”
Claim · model-written summary
Spectral indices and enzyme activities were strongly correlated in determining yield losses.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- yield lossese0
- Comparator
- Spectral indices and enzyme activitiese0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Spectral indices and enzyme activities showed a strong correlation in determining yield losses.”
t0“wheat”
The same questions in other papers
cold · physiology: photosynthesis · Triticum aestivum (wheat)Results differ
15 papers in the same direction as this paper · 11 in the opposite direction
- negative
- positive
- positive
23 papers more in this group
See all 27 papers →drought · physiology: photosynthesis · Triticum aestivum (wheat)Positive and negative results
204 papers in the same direction as this paper · 41 in the opposite direction
- positive
- positive
- positive
242 papers more in this group
See all 246 papers →Claims are grouped by organism, treatment and what was measured or targeted. A paper counts as positive when any of its claims in the group is positive.