Abstract
Infrared thermography is a useful technology for examining water status in terres- trial vegetation. This research was focused on assessing the water status of soybean plants (Glycinemax (L.) Merrill) using high resolution thermal infrared images. The plants were subjected to a range of moisture stress treatments in order to evaluate the water content in sampled leaves. The plants were irrigated with 8 different treat- ment levels (control (i.e., fully irrigated) and 1 to 7 days of water being withheld). One specific trifoliate was segmented from each of the thermal images for every plant sample, and both mean temperature and Crop Water Stress Index (CWSI) were computed for each plant. Leaf discs were taken from the same trifoliate to gravi- metrically measure relative water content (RWC). RWC had statistically significant correlation coefficients with both CWSI (r = -0.92, n = 56; p�< 0.001) and raw mean temperature (r = -0.84, n = 56; p�< 0.001). Two separate regression models were developed to predict RWC using mean raw trifoliate temperature and CWSI. Our results document that a CWSI-based regression model was better in predicting RWC than a model based on mean raw trifoliate temperature.
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Claims 3
Claim · model-written summary
Relative water content of soybean leaves had a significant negative correlation with CWSI (r = -0.92).
- Taxon
- soybean
Glycine maxe4 - Treatment
- moisture stress treatmentse0
- Outcome
- RWCe1
- Direction
- decreaseinferrede1
- Value
- r = -0.92e1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e4“This research was focused on assessing the water status of soybean plants (Glycinemax (L.) Merrill) using high resolution thermal infrared images”
e0“The plants were subjected to a range of moisture stress treatments in order to evaluate the water content in sampled leaves”
e1“RWC had statistically significant correlation coefficients with both CWSI (r = -0.92, n = 56”
Claim · model-written summary
Relative water content of soybean leaves had a significant negative correlation with raw mean trifoliate temperature (r = -0.84).
- Taxon
- soybeanGlycine maxe4
- Treatment
- moisture stress treatmentse0
- Outcome
- RWCe1
- Value
- r = -0.84e5
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e4“This research was focused on assessing the water status of soybean plants (Glycinemax (L.) Merrill) using high resolution thermal infrared images”
e0“The plants were subjected to a range of moisture stress treatments in order to evaluate the water content in sampled leaves”
e1“RWC had statistically significant correlation coefficients with both CWSI (r = -0.92, n = 56”
e5“and raw mean temperature (r = -0.84, n = 56”
Claim · model-written summary
A CWSI-based regression predicted relative water content of soybean better than a model based on mean raw trifoliate temperature.
- Taxon
- soybeanGlycine maxe4
- Outcome
- predicting RWCe3
- Direction
- bettere3
- Comparator
- model based on mean raw trifoliate temperaturee3
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e4“This research was focused on assessing the water status of soybean plants (Glycinemax (L.) Merrill) using high resolution thermal infrared images”
e3“a CWSI-based regression model was better in predicting RWC than a model based on mean raw trifoliate temperature”