Abstract
Abstract The net photosynthetic rate (Pn) is one of the important indicators to measure photosynthetic capacity of crops. Therefore, it is critically important to find real‐time methods for accurately estimating Pn of winter wheat ( Triticum aestivum L.). This information could provide guidance on the management of waterlogging stress. To explore the optimal monitoring method for Pn of winter wheat under waterlogging stress, the correlations between Pn and 16 characteristic image indices were analyzed in irrigated and drained microplot experiments. Then, based on the indices values, Pn was estimated using the multiple linear regression (MLR), support vector machine (SVM), backpropagation neural network (BP), and random forest (RF) models, which were constructed based on the optimal monitoring image indices. Water logging when compared to no waterlogged wheat had similar Pn values <6 days. After 12 days, waterlogged wheat plants had a lower Pn value than in no waterlogged plants. All indices were correlated with the Pn ( p < 0.05), and Pn estimation accuracy was lower at the winter wheat flowering and complete ripeness periods than at the milky and waxy ripe maturity periods. Based on the results of the models tested, the RF model had higher R 2 (0.904) values than the other models. These findings suggest that machine learning models could be used to accurately predict Pn, and the random forest algorithm was the best.
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Claims 6
Claim · model-written summary
Before six days, waterlogged wheat had similar net photosynthetic rates to non-waterlogged wheat.
- Taxon
- winter wheat
Triticum aestivumt0 - Treatment
- Water logginge0
- Outcome
- Pn valuese0
- Direction
- similare0
- Qualifier
- <6 dayse0
- Comparator
- no waterlogged wheate0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Water logging when compared to no waterlogged wheat had similar Pn values <6 days.”
t0“winter wheat”
Claim · model-written summary
After 12 days, waterlogged wheat had lower net photosynthetic rate than non-waterlogged plants.
- Taxon
- winter wheatTriticum aestivumt0
- Treatment
- waterloggede0
- Outcome
- Pn valuee0
- Direction
- lowere0
- Qualifier
- After 12 dayse0
- Comparator
- no waterlogged plantse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“After 12 days, waterlogged wheat plants had a lower Pn value than in no waterlogged plants.”
t0“winter wheat”
Claim · model-written summary
All 16 image indices were correlated with wheat net photosynthetic rate.
- Taxon
- winter wheatTriticum aestivumt0
- Outcome
- Pne0
- Comparator
- indicese0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“All indices were correlated with the Pn ( p < 0.05)”
t0“winter wheat”
Claim · model-written summary
Photosynthetic rate estimation was less accurate at flowering and complete ripeness than at milky and waxy maturity.
- Taxon
- winter wheatTriticum aestivume0
- Organ / stage
- flowering and complete ripeness periodse0
- Outcome
- Pn estimation accuracye0
- Direction
- lowere0
- Comparator
- milky and waxy ripe maturity periodse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Pn estimation accuracy was lower at the winter wheat flowering and complete ripeness periods than at the milky and waxy ripe maturity periods”
Claim · model-written summary
The random forest model had a higher R squared value of 0.904 than the other tested models.
- Taxon
- winter wheatTriticum aestivumt0
- Outcome
- R 2e0
- Direction
- highere0
- Value
- 0.904e0
- Comparator
- other modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the RF model had higher R 2 (0.904) values than the other models”
t0“winter wheat”
Claim · model-written summary
The authors judged random forest the best algorithm for predicting wheat net photosynthetic rate.
- Taxon
- winter wheatTriticum aestivumt0
- Outcome
- random forest algorithm was the beste0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“machine learning models could be used to accurately predict Pn, and the random forest algorithm was the best”
t0“winter wheat”
The same questions in other papers
flooding · physiology: photosynthesis · Triticum aestivum (wheat)Results differ
36 papers in the same direction as this paper · 7 in the opposite direction
- positive
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See all 44 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.