Abstract
Appropriate water supply is crucial for high-yield and high-quality potato tuber production. However, potatoes are mainly planted in arid and semi-arid regions in China, where the precipitation usually cannot meet the water demand throughout the growth period. In view of the actual situation of water shortage in these areas, to monitor the water status of potato plants timely and accurately and thus precisely control the irrigation are of significance for water-saving management of potatoes. Hyperspectral remote sensing has unique advantages in diagnosing crop water stress. In this paper, the canopy spectral reflectance and plant water content were measured under five irrigation treatments. The spectral parameters that respond to plant water content were selected, and a hyperspectral water diagnosis model for leaf water content (LWC) and aboveground water content (AGWC) of potato plants was established. It was found that potato tuber yield was the highest during the entire growth period under sufficient irrigation, and the plant water content showed a downward trend as the degree of drought intensified. The peak hyperspectral reflectance of potato plant canopies appeared in the red wavelength, where the reflectance varied significantly under different water treatments and decreased with decreasing irrigation. Six models with sensitive bands, first-order derivatives, and moisture spectral indices were established to monitor water content of potato plants. The R2 values of partial least squares regression (PLSR), support vector machine (SVM), and BP neural network (BP) models are 0.8418, 0.9020, and 0.8926, respectively, between LWC and hyperspectral data; and 0.8003, 0.8167, and 0.8671, respectively, between the AGWC and hyperspectral data. These six models can all predict the water content of potato plants, but SVM is the best model for predicting LWC of potato plants. These results are of great significance for guiding precision irrigation of potato plants at different growth stages.
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Claims 12
Claim · model-written summary
Sufficient irrigation gave the highest tuber yield.
- Taxon
- Potato
Solanum tuberosumt0 - Treatment
- sufficient irrigatione0
- Outcome
- potato tuber yielde0
- Direction
- higheste0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation”
e0“potato tuber yield was the highest during the entire growth period under sufficient irrigation”
Claim · model-written summary
Plant water content fell as drought intensified.
- Taxon
- PotatoSolanum tuberosumt0
- Treatment
- degree of droughte0
- Outcome
- plant water contente0
- Direction
- downward trende0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation”
e0“plant water content showed a downward trend as the degree of drought intensified”
Claim · model-written summary
Peak canopy reflectance occurred at red wavelengths.
- Taxon
- PotatoSolanum tuberosumt0
- Outcome
- peak hyperspectral reflectance of potato plant canopiese0
- Qualifier
- red wavelengthe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation”
e0“The peak hyperspectral reflectance of potato plant canopies appeared in the red wavelength”
Claim · model-written summary
Reflectance varied significantly across water treatments.
- Taxon
- PotatoSolanum tuberosumt0
- Treatment
- different water treatmentse0
- Outcome
- reflectancee0
Not reportedComparatorDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation”
e0“reflectance varied significantly under different water treatments”
Claim · model-written summary
Canopy reflectance decreased with decreasing irrigation.
- Taxon
- PotatoSolanum tuberosumt0
- Treatment
- decreasing irrigatione0
- Outcome
- reflectancee0
- Direction
- decreasede0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation”
e0“reflectance varied significantly under different water treatments and decreased with decreasing irrigation”
Claim · model-written summary
PLSR predicted leaf water content with R2 of 0.8418.
- Taxon
- PotatoSolanum tuberosumt0
- Outcome
- R2 valuese0
- Value
- 0.8418e0
- Qualifier
- between LWC and hyperspectral datae0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation”
e0“The R2 values of partial least squares regression (PLSR), support vector machine (SVM), and BP neural network (BP) models are 0.8418, 0.9020, and 0.8926, respectively, between LWC and hyperspectral data”
Claim · model-written summary
SVM predicted leaf water content with R2 of 0.9020.
- Taxon
- PotatoSolanum tuberosumt0
- Outcome
- R2 valuese0
- Value
- 0.9020e0
- Qualifier
- between LWC and hyperspectral datae0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation”
e0“The R2 values of partial least squares regression (PLSR), support vector machine (SVM), and BP neural network (BP) models are 0.8418, 0.9020, and 0.8926, respectively, between LWC and hyperspectral data”
Claim · model-written summary
BP neural network predicted leaf water content with R2 of 0.8926.
- Taxon
- PotatoSolanum tuberosumt0
- Outcome
- R2 valuese0
- Value
- 0.8926e0
- Qualifier
- between LWC and hyperspectral datae0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation”
e0“The R2 values of partial least squares regression (PLSR), support vector machine (SVM), and BP neural network (BP) models are 0.8418, 0.9020, and 0.8926, respectively, between LWC and hyperspectral data”
Claim · model-written summary
PLSR predicted aboveground water content with R2 of 0.8003.
- Taxon
- PotatoSolanum tuberosumt0
- Outcome
- AGWCe0
- Value
- 0.8003e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation”
e0“0.8003, 0.8167, and 0.8671, respectively, between the AGWC and hyperspectral data”
Claim · model-written summary
SVM predicted aboveground water content with R2 of 0.8167.
- Taxon
- PotatoSolanum tuberosumt0
- Outcome
- AGWCe0
- Value
- 0.8167e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation”
e0“0.8003, 0.8167, and 0.8671, respectively, between the AGWC and hyperspectral data”
Claim · model-written summary
BP neural network predicted aboveground water content with R2 of 0.8671.
- Taxon
- PotatoSolanum tuberosumt0
- Outcome
- AGWCe0
- Value
- 0.8671e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation”
e0“0.8003, 0.8167, and 0.8671, respectively, between the AGWC and hyperspectral data”
Claim · model-written summary
SVM was the best leaf-water-content model.
- Taxon
- PotatoSolanum tuberosumt0
- Outcome
- predicting LWC of potato plantse0
- Comparator
- best modele0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation”
e0“SVM is the best model for predicting LWC of potato plants”
The same questions in other papers
irrigation · yield · Solanum tuberosum (potato)Results differ
74 papers in the same direction as this paper · 16 in the opposite direction · 1 reporting no difference from an alternative
- no effect
- no effect
- negative
88 papers more in this group
See all 92 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.