Abstract
A pot experiment was conducted to determine the impact of water availability on the discriminatory status of nitrogen (N) in plants using hyperspectral imaging. Nitrogen deficiency causes a significant decrease in chlorophyll concentration in plant leaves regardless of water availability. Five different classification algorithms were used to discriminate between nitrogen concentrations in plants at different levels of water availability. Several statistical parameters, including kappa and overall classification accuracy for calibration and prediction, were used to determine the efficiency and accuracy of the models. The Random Forest model had the highest overall accuracy of over 81% for sugar beet and over 78% for celery. Additionally, characteristic electromagnetic wavelengths were identified in which reflectance correlated with nitrogen and water content in plants could be recorded. It was also noted that the spectral resolution between the N and High Water (HW)/Low Water (LW) treatments was lower in the short-wave infrared (SWIR) region than in the visible and near-infrared (VNIR) region.
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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 5
Claim · model-written summary
Nitrogen deficiency significantly decreased leaf chlorophyll regardless of water availability.
- Taxon
- Sugar Beet and Celery
Beta vulgaris subsp. vulgaris%2C_Elswick%2C_Northumberland_4.jpg?width=60)
Apium graveolens Dulce Groupt0 - Treatment
- Nitrogen deficiencye0
- Outcome
- chlorophyll concentration in plant leavese0
- Direction
- decreasee0
- Qualifier
- regardless of water availabilitye0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
%2C_Elswick%2C_Northumberland_4.jpg?width=500)
Verbatim
e0“Nitrogen deficiency causes a significant decrease in chlorophyll concentration in plant leaves regardless of water availability”
t0“Sugar Beet and Celery”
Claim · model-written summary
Random Forest achieved over 81% overall accuracy for sugar beet, the highest among models.
- Taxon
- sugar beetBeta vulgaris subsp. vulgarise0
- Outcome
- overall accuracye0
- Direction
- higheste0
- Value
- over 81%e0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The Random Forest model had the highest overall accuracy of over 81% for sugar beet and over 78% for celery”
Claim · model-written summary
Random Forest achieved over 78% overall accuracy for celery, the highest among models.
- Taxon
- celeryApium graveolens Dulce Groupe0
- Outcome
- overall accuracye0
- Direction
- higheste0
- Value
- over 78%e0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The Random Forest model had the highest overall accuracy of over 81% for sugar beet and over 78% for celery”
Claim · model-written summary
Characteristic wavelengths recorded reflectance correlated with plant nitrogen and water content.
- Taxon
- Sugar Beet and CeleryBeta vulgaris subsp. vulgarisApium graveolens Dulce Groupt0
- Outcome
- reflectance correlated with nitrogen and water contente0
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“characteristic electromagnetic wavelengths were identified in which reflectance correlated with nitrogen and water content in plants”
t0“Sugar Beet and Celery”
Claim · model-written summary
Spectral resolution between nitrogen and water treatments was lower in SWIR than VNIR.
- Taxon
- Sugar Beet and CeleryBeta vulgaris subsp. vulgarisApium graveolens Dulce Groupt0
- Treatment
- N and High Water (HW)/Low Water (LW) treatmentse0
- Outcome
- spectral resolutione0
- Direction
- lowere0
- Comparator
- visible and near-infrared (VNIR) regione0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the spectral resolution between the N and High Water (HW)/Low Water (LW) treatments was lower in the short-wave infrared (SWIR) region than in the visible and near-infrared (VNIR) region”
t0“Sugar Beet and Celery”
The same questions in other papers
nitrogen · physiology: pigments · Beta vulgaris (beet)Results agree
7 papers in the same direction as this paper
This paper is about lacking the nutrient, so it counts the other way round here.
- positive
- positive
- positive
4 papers more in this group
See all 8 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.