Abstract
BACKGROUND: Wheat (Triticum aestivum L.) is an important grain crops in the world, and its growth and development in different stages is seriously affected by saline-alkali stress, especially in seedling stage. Therefore, nondestructive detection of wheat seedlings under saline-alkali stress can provide more comprehensive technical support for wheat breeding, cultivation and management. RESULTS: This research focused on moisture signal prediction and classification of saline-alkali stress in wheat seedlings using fusion techniques. After collecting and analyzing transverse relaxation time and Multispectral imaging (MSI) information of wheat seedlings, four regression models were used to predict the moisture signal. K-Nearest Neighbor (KNN) and Gaussian-Naïve Bayes (GNB) models were combined with fivefold cross validation to classify the prediction of wheat seedling stress. The results showed that wheat seedlings would increase the bound water content through a certain mechanism to enhance their saline-alkali stress. Under the same Na concentration, the effect of alkali stress on moisture, growth and spectrum of wheat seedlings is stronger than salt stress. The Gradient Boosting Decision Regression Tree model performs the best in predicting wheat moisture signals, with a coefficient of determination (R2P) of 0.98 and a root mean square error of 109.60. It also had a short training time (1.48 s) and an efficient prediction speed (1300 obs/s). The KNN and GNB demonstrated significantly enhanced predictive performance when classifying the fused dataset, compared to using single datasets individually. In particular, the GNB model performing best on the fused dataset, with Precision, Recall, Accuracy, and F1-score of 90.30, 88.89%, 88.90%, and 0.90, respectively. CONCLUSIONS: Under the same Na concentration, the effects of alkali stress on water content, spectrum, and growth of wheat were stronger than that of salt stress, which was more unfavorable to the growth of wheat. The fusion of low-field nuclear magnetic resonance and MSI technology can improve the classification of wheat stress, and provide an effective technical method for rapid and accurate monitoring of wheat seedlings under saline-alkali stress.
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Claims 14
Claim · model-written summary
Wheat seedlings increased bound water content under saline-alkali stress.
- Taxon
- wheat seedlings
Triticum aestivume0 - Outcome
- bound water contente0
- Direction
- increasee0
- Qualifier
- saline-alkali stresse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“wheat seedlings would increase the bound water content through a certain mechanism to enhance their saline-alkali stress.”
Claim · model-written summary
At equal Na concentration, alkali stress affected wheat seedling moisture more strongly than salt stress.
- Taxon
- wheat seedlingsTriticum aestivume0
- Treatment
- alkali stresse0
- Outcome
- moisturee0
- Direction
- strongere0
- Qualifier
- same Na concentratione0
- Comparator
- salt stresse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Under the same Na concentration, the effect of alkali stress on moisture, growth and spectrum of wheat seedlings is stronger than salt stress.”
Claim · model-written summary
At equal Na concentration, alkali stress affected wheat seedling growth more strongly than salt stress.
- Taxon
- wheat seedlingsTriticum aestivume0
- Treatment
- alkali stresse0
- Outcome
- growthe0
- Direction
- strongere0
- Qualifier
- same Na concentratione0
- Comparator
- salt stresse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Under the same Na concentration, the effect of alkali stress on moisture, growth and spectrum of wheat seedlings is stronger than salt stress.”
Claim · model-written summary
At equal Na concentration, alkali stress affected wheat seedling spectrum more strongly than salt stress.
- Taxon
- wheat seedlingsTriticum aestivume0
- Treatment
- alkali stresse0
- Outcome
- spectrume0
- Direction
- strongere0
- Qualifier
- same Na concentratione0
- Comparator
- salt stresse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Under the same Na concentration, the effect of alkali stress on moisture, growth and spectrum of wheat seedlings is stronger than salt stress.”
Claim · model-written summary
Gradient Boosting Decision Regression Tree best predicted wheat moisture signals with R2P 0.98.
- Taxon
- wheatTriticum aestivume0
- Outcome
- coefficient of determination (R2P)e0
- Direction
- beste0
- Value
- 0.98e0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The Gradient Boosting Decision Regression Tree model performs the best in predicting wheat moisture signals, with a coefficient of determination (R2P) of 0.98 and a root mean square error of 109.60.”
Claim · model-written summary
Gradient Boosting Decision Regression Tree had prediction root mean square error 109.60.
- Taxon
- wheatTriticum aestivume0
- Outcome
- root mean square errore0
- Direction
- beste0
- Value
- 109.60e0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The Gradient Boosting Decision Regression Tree model performs the best in predicting wheat moisture signals, with a coefficient of determination (R2P) of 0.98 and a root mean square error of 109.60.”
Claim · model-written summary
The best model trained in 1.48 s.
Not reportedTaxonComparator
Verbatim
e0“It also had a short training time (1.48 s) and an efficient prediction speed (1300 obs/s).”
Claim · model-written summary
The best model predicted at 1300 observations per second.
Not reportedTaxonComparator
Verbatim
e0“It also had a short training time (1.48 s) and an efficient prediction speed (1300 obs/s).”
Claim · model-written summary
KNN classification performance significantly improved with fused versus individual datasets.
Not reportedTaxon
Verbatim
e0“The KNN and GNB demonstrated significantly enhanced predictive performance when classifying the fused dataset, compared to using single datasets individually.”
Claim · model-written summary
GNB classification performance significantly improved with fused versus individual datasets.
Not reportedTaxon
Verbatim
e0“The KNN and GNB demonstrated significantly enhanced predictive performance when classifying the fused dataset, compared to using single datasets individually.”
Claim · model-written summary
GNB on the fused dataset achieved precision 90.30.
Not reportedTaxonComparator
Verbatim
e0“the GNB model performing best on the fused dataset, with Precision, Recall, Accuracy, and F1-score of 90.30, 88.89%, 88.90%, and 0.90, respectively.”
Claim · model-written summary
GNB on the fused dataset achieved recall 88.89%.
Not reportedTaxonComparator
Verbatim
e0“the GNB model performing best on the fused dataset, with Precision, Recall, Accuracy, and F1-score of 90.30, 88.89%, 88.90%, and 0.90, respectively.”
Claim · model-written summary
GNB on the fused dataset achieved accuracy 88.90%.
Not reportedTaxonComparator
Verbatim
e0“the GNB model performing best on the fused dataset, with Precision, Recall, Accuracy, and F1-score of 90.30, 88.89%, 88.90%, and 0.90, respectively.”
Claim · model-written summary
GNB on the fused dataset achieved F1 score 0.90.
Not reportedTaxonComparator
Verbatim
e0“the GNB model performing best on the fused dataset, with Precision, Recall, Accuracy, and F1-score of 90.30, 88.89%, 88.90%, and 0.90, respectively.”
The same questions in other papers
alkalinity · growth · Triticum aestivum (wheat)Positive and negative results
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