By research area › Breeding, agronomy & horticulture › Method / resource

Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

Removing hybrids predicted above a height threshold and with unacceptable anthracnose resistance retained 514 hybrids with significantly higher observed grain yield than the mean yield of 381 discarded hybrids.

Taxon: sorghumSorghumt0
Outcome: observed grain yielde1
Direction: highere1
Value: 514 retained hybridse1
Comparator: mean yield of 381 discarded hybridse1

Verbatim

e1
“514 retained hybrids that had a significantly higher observed grain yield than the mean yield of 381 discarded hybrids”
✓ located verbatim in the source · abstract, characters 1778–1896

t0
“sorghum hybrid adaptation”
✓ located verbatim in the source · title, characters 61–86

Title

Tandem genomic selection with independent culling to improve sorghum hybrid adaptation to the eastern United States

R. E. Boyles; C. Ballén‐Taborda; M. A. Conti; et al. Crop Science 2026. doi:10.1002/csc2.70249

Other claims from this paper (5)

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Hyperspectral leaf reflectance models predicted celery soluble solids with external validation R2 = 0.84.

Taxon: celeryApium graveolens Dulce Groupe0
Outcome: soluble solidse0
Value: 0.84e0

Verbatim

e0
“Spectra collected at two crop stages of celery were used to model soluble solids, total N, NO 3 -N, and dry matter contents (external validation R 2 = 0.84, 0.76, 0.73, 0.65, respectively)”
✓ located verbatim in the source · abstract, characters 1324–1512

Title

Hyperspectral modeling of leaf nitrogen, nitrate, and other quality attributes in celery across diverse organic nutrient management programs

Zachary T. Ray; Stephen Lantin; Moses Chilenje; et al. Smart Agricultural Technology 2026. doi:10.1016/j.atech.2026.102008

Other claims from this paper (3)

Method / resource Hyperspectral leaf reflectance models predicted celery total N with external validation R2 = 0.76.
Method / resource Hyperspectral leaf reflectance models predicted celery NO 3 -N with external validation R2 = 0.73.
Method / resource Hyperspectral leaf reflectance models predicted celery dry matter contents with external validation R2 = 0.65.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Hyperspectral leaf reflectance models predicted celery total N with external validation R2 = 0.76.

Taxon: celeryApium graveolens Dulce Groupe0
Outcome: total Ne0
Value: 0.76e0

Verbatim

e0
“Spectra collected at two crop stages of celery were used to model soluble solids, total N, NO 3 -N, and dry matter contents (external validation R 2 = 0.84, 0.76, 0.73, 0.65, respectively)”
✓ located verbatim in the source · abstract, characters 1324–1512

Title

Hyperspectral modeling of leaf nitrogen, nitrate, and other quality attributes in celery across diverse organic nutrient management programs

Zachary T. Ray; Stephen Lantin; Moses Chilenje; et al. Smart Agricultural Technology 2026. doi:10.1016/j.atech.2026.102008

Other claims from this paper (3)

Method / resource Hyperspectral leaf reflectance models predicted celery soluble solids with external validation R2 = 0.84.
Method / resource Hyperspectral leaf reflectance models predicted celery NO 3 -N with external validation R2 = 0.73.
Method / resource Hyperspectral leaf reflectance models predicted celery dry matter contents with external validation R2 = 0.65.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Hyperspectral leaf reflectance models predicted celery NO 3 -N with external validation R2 = 0.73.

Taxon: celeryApium graveolens Dulce Groupe0
Outcome: NO 3 -Ne0
Value: 0.73e0

Verbatim

e0
“Spectra collected at two crop stages of celery were used to model soluble solids, total N, NO 3 -N, and dry matter contents (external validation R 2 = 0.84, 0.76, 0.73, 0.65, respectively)”
✓ located verbatim in the source · abstract, characters 1324–1512

Title

Hyperspectral modeling of leaf nitrogen, nitrate, and other quality attributes in celery across diverse organic nutrient management programs

Zachary T. Ray; Stephen Lantin; Moses Chilenje; et al. Smart Agricultural Technology 2026. doi:10.1016/j.atech.2026.102008

Other claims from this paper (3)

Method / resource Hyperspectral leaf reflectance models predicted celery soluble solids with external validation R2 = 0.84.
Method / resource Hyperspectral leaf reflectance models predicted celery total N with external validation R2 = 0.76.
Method / resource Hyperspectral leaf reflectance models predicted celery dry matter contents with external validation R2 = 0.65.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Hyperspectral leaf reflectance models predicted celery dry matter contents with external validation R2 = 0.65.

Taxon: celeryApium graveolens Dulce Groupe0
Outcome: dry matter contentse0
Value: 0.65e0

Verbatim

e0
“Spectra collected at two crop stages of celery were used to model soluble solids, total N, NO 3 -N, and dry matter contents (external validation R 2 = 0.84, 0.76, 0.73, 0.65, respectively)”
✓ located verbatim in the source · abstract, characters 1324–1512

Title

Hyperspectral modeling of leaf nitrogen, nitrate, and other quality attributes in celery across diverse organic nutrient management programs

Zachary T. Ray; Stephen Lantin; Moses Chilenje; et al. Smart Agricultural Technology 2026. doi:10.1016/j.atech.2026.102008

Other claims from this paper (3)

Method / resource Hyperspectral leaf reflectance models predicted celery soluble solids with external validation R2 = 0.84.
Method / resource Hyperspectral leaf reflectance models predicted celery total N with external validation R2 = 0.76.
Method / resource Hyperspectral leaf reflectance models predicted celery NO 3 -N with external validation R2 = 0.73.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Leaf spectra collected in December, at the fruit set stage, gave the highest cultivar classification accuracy.

Taxon: GrapevineVitist0
Organ / stage: fruit set phenological stagee1
Outcome: highest classification accuracye1

Not reportedValue

Verbatim

t0
“Grapevine Cultivar Discrimination”
✓ located verbatim in the source · title, characters 114–147

e1
“Leaf spectra collected in December—coinciding with the fruit set phenological stage—provided the highest classification accuracy”
✓ located after whitespace/quote normalisation · abstract, characters 988–1115

Title

Robust Spectral Classification Under Sample Type and Seasonal Variability: A Proximal Remote Sensing Approach for Grapevine Cultivar Discrimination

Kyle Loggenberg; Albert Strever; Zahn Münch Precision Agriculture 2026. doi:10.1007/s11119-026-10333-w

Other claims from this paper (11)

Method / resource Models showed limited generalisability across sample types, with substantial declines in accuracy when trained on one type and tested on another.
Method / resource Combining leaf and canopy spectra in training improved performance but it remained lower than when models were trained and tested on the same sample type.
Method / resource SRD analysis identified Support Vector Machine and 1D Convolutional Neural Network as the most robust classifiers.
Method / resource SVM models achieved F1 scores of 0.67-1.00.
Method / resource SVM models achieved balanced accuracy (BACC) of 0.74-0.98.
Method / resource SVM models achieved Matthews Correlation Coefficient (MCC) of 0.65-0.90.
Method / resource SVM models achieved AUC values of 0.87-0.94.
Method / resource 1D CNN models achieved F1 of 0.62-0.98.
Method / resource 1D CNN models achieved BACC of 0.79-0.99.
Method / resource 1D CNN models achieved MCC of 0.57-0.97.
Method / resource 1D CNN models achieved AUC of 0.93-1.00.

Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

Models showed limited generalisability across sample types, with substantial declines in accuracy when trained on one type and tested on another.

Taxon: GrapevineVitist0
Outcome: limited generalisabilitye1
Qualifier: substantial declines in accuracy when trained on one type and tested on anothere1

Not reportedValue

Verbatim

t0
“Grapevine Cultivar Discrimination”
✓ located verbatim in the source · title, characters 114–147

e1
“Models showed limited generalisability across data scales (i.e., sample types), with substantial declines in accuracy when trained on one type and tested on another”
✓ located after whitespace/quote normalisation · abstract, characters 1117–1279

Title

Robust Spectral Classification Under Sample Type and Seasonal Variability: A Proximal Remote Sensing Approach for Grapevine Cultivar Discrimination

Kyle Loggenberg; Albert Strever; Zahn Münch Precision Agriculture 2026. doi:10.1007/s11119-026-10333-w

Other claims from this paper (11)

Method / resource Leaf spectra collected in December, at the fruit set stage, gave the highest cultivar classification accuracy.
Method / resource Combining leaf and canopy spectra in training improved performance but it remained lower than when models were trained and tested on the same sample type.
Method / resource SRD analysis identified Support Vector Machine and 1D Convolutional Neural Network as the most robust classifiers.
Method / resource SVM models achieved F1 scores of 0.67-1.00.
Method / resource SVM models achieved balanced accuracy (BACC) of 0.74-0.98.
Method / resource SVM models achieved Matthews Correlation Coefficient (MCC) of 0.65-0.90.
Method / resource SVM models achieved AUC values of 0.87-0.94.
Method / resource 1D CNN models achieved F1 of 0.62-0.98.
Method / resource 1D CNN models achieved BACC of 0.79-0.99.
Method / resource 1D CNN models achieved MCC of 0.57-0.97.
Method / resource 1D CNN models achieved AUC of 0.93-1.00.

Method / resourceobservedoriginal resultmixedmeasured

Claim · model-written summary

Combining leaf and canopy spectra in training improved performance but it remained lower than when models were trained and tested on the same sample type.

Taxon: GrapevineVitist0
Outcome: performancee1
Direction: improvede1
Comparator: trained and tested on the same sample typee1

Verbatim

t0
“Grapevine Cultivar Discrimination”
✓ located verbatim in the source · title, characters 114–147

e1
“Combining leaf and canopy spectra in the training data improved performance but remained lower than when models were trained and tested on the same sample type”
✓ located after whitespace/quote normalisation · abstract, characters 1281–1438

Title

Robust Spectral Classification Under Sample Type and Seasonal Variability: A Proximal Remote Sensing Approach for Grapevine Cultivar Discrimination

Kyle Loggenberg; Albert Strever; Zahn Münch Precision Agriculture 2026. doi:10.1007/s11119-026-10333-w

Other claims from this paper (11)

Method / resource Leaf spectra collected in December, at the fruit set stage, gave the highest cultivar classification accuracy.
Method / resource Models showed limited generalisability across sample types, with substantial declines in accuracy when trained on one type and tested on another.
Method / resource SRD analysis identified Support Vector Machine and 1D Convolutional Neural Network as the most robust classifiers.
Method / resource SVM models achieved F1 scores of 0.67-1.00.
Method / resource SVM models achieved balanced accuracy (BACC) of 0.74-0.98.
Method / resource SVM models achieved Matthews Correlation Coefficient (MCC) of 0.65-0.90.
Method / resource SVM models achieved AUC values of 0.87-0.94.
Method / resource 1D CNN models achieved F1 of 0.62-0.98.
Method / resource 1D CNN models achieved BACC of 0.79-0.99.
Method / resource 1D CNN models achieved MCC of 0.57-0.97.
Method / resource 1D CNN models achieved AUC of 0.93-1.00.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

SRD analysis identified Support Vector Machine and 1D Convolutional Neural Network as the most robust classifiers.

Taxon: GrapevineVitist0
Outcome: most robust classifierse1
Qualifier: Support Vector Machine (SVM) and 1D Convolutional Neural Network (CNN)e1

Not reportedValue

Verbatim

t0
“Grapevine Cultivar Discrimination”
✓ located verbatim in the source · title, characters 114–147

e1
“SRD analysis identified Support Vector Machine (SVM) and 1D Convolutional Neural Network (CNN) as the most robust classifiers”
✓ located after whitespace/quote normalisation · abstract, characters 1440–1563

Title

Robust Spectral Classification Under Sample Type and Seasonal Variability: A Proximal Remote Sensing Approach for Grapevine Cultivar Discrimination

Kyle Loggenberg; Albert Strever; Zahn Münch Precision Agriculture 2026. doi:10.1007/s11119-026-10333-w

Other claims from this paper (11)

Method / resource Leaf spectra collected in December, at the fruit set stage, gave the highest cultivar classification accuracy.
Method / resource Models showed limited generalisability across sample types, with substantial declines in accuracy when trained on one type and tested on another.
Method / resource Combining leaf and canopy spectra in training improved performance but it remained lower than when models were trained and tested on the same sample type.
Method / resource SVM models achieved F1 scores of 0.67-1.00.
Method / resource SVM models achieved balanced accuracy (BACC) of 0.74-0.98.
Method / resource SVM models achieved Matthews Correlation Coefficient (MCC) of 0.65-0.90.
Method / resource SVM models achieved AUC values of 0.87-0.94.
Method / resource 1D CNN models achieved F1 of 0.62-0.98.
Method / resource 1D CNN models achieved BACC of 0.79-0.99.
Method / resource 1D CNN models achieved MCC of 0.57-0.97.
Method / resource 1D CNN models achieved AUC of 0.93-1.00.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

SVM models achieved F1 scores of 0.67-1.00.

Taxon: GrapevineVitist0
Outcome: F1 scorese1
Value: 0.67–1.00e1
Qualifier: SVM modelse1

Verbatim

t0
“Grapevine Cultivar Discrimination”
✓ located verbatim in the source · title, characters 114–147

e1
“SVM models achieved F1 scores of0.67–1.00”
✓ located verbatim in the source · abstract, characters 1565–1606

Title

Robust Spectral Classification Under Sample Type and Seasonal Variability: A Proximal Remote Sensing Approach for Grapevine Cultivar Discrimination

Kyle Loggenberg; Albert Strever; Zahn Münch Precision Agriculture 2026. doi:10.1007/s11119-026-10333-w

Other claims from this paper (11)

Method / resource Leaf spectra collected in December, at the fruit set stage, gave the highest cultivar classification accuracy.
Method / resource Models showed limited generalisability across sample types, with substantial declines in accuracy when trained on one type and tested on another.
Method / resource Combining leaf and canopy spectra in training improved performance but it remained lower than when models were trained and tested on the same sample type.
Method / resource SRD analysis identified Support Vector Machine and 1D Convolutional Neural Network as the most robust classifiers.
Method / resource SVM models achieved balanced accuracy (BACC) of 0.74-0.98.
Method / resource SVM models achieved Matthews Correlation Coefficient (MCC) of 0.65-0.90.
Method / resource SVM models achieved AUC values of 0.87-0.94.
Method / resource 1D CNN models achieved F1 of 0.62-0.98.
Method / resource 1D CNN models achieved BACC of 0.79-0.99.
Method / resource 1D CNN models achieved MCC of 0.57-0.97.
Method / resource 1D CNN models achieved AUC of 0.93-1.00.

Method / resource 289,819 claims

289,819 claims