By organism › Green plants › Vitaceae › Vitis

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

On a real-world dataset of 1,216 images across five cultivars, the detector improved AP metrics and error counts over the YOLOv8n baseline at comparable runtime.

Taxon: grapevineVitist0
Outcome: AP metrics and error countse0
Direction: improvementse0
Comparator: YOLOv8n baselinee0

Verbatim

t0
“grapevine”
✓ located verbatim in the source · title, characters 53–62

e0
“Experiments on a real-world dataset of 1,216 images across five cultivars show consistent improvements over the YOLOv8n baseline in AP metrics and error counts at comparable runtime”
✓ located verbatim in the source · abstract, characters 1721–1902

Title

One-stem-one-box: A consistency-driven framework for grapevine primary stem detection

Zhangqi Fang; Qian Zhang; Yonglei Tao; et al. Results in Engineering 2026. doi:10.1016/j.rineng.2026.109747

Other claims from this paper (3)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Hybrid-Linear post-processing reached an inference throughput of about 32 FPS on a server-grade GPU.

Taxon: grapevineVitist0
Outcome: inference throughpute0
Value: ≈32 FPSe0

Verbatim

t0
“grapevine”
✓ located verbatim in the source · title, characters 53–62

e0
“Hybrid-Linear achieves an inference throughput of ≈32 FPS on a server-grade GPU under our evaluation protocol”
✓ located verbatim in the source · abstract, characters 1908–2017

Title

One-stem-one-box: A consistency-driven framework for grapevine primary stem detection

Zhangqi Fang; Qian Zhang; Yonglei Tao; et al. Results in Engineering 2026. doi:10.1016/j.rineng.2026.109747

Other claims from this paper (3)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Hybrid-Linear post-processing drives duplicate detections to near zero while preserving recall.

Taxon: grapevineVitist0
Outcome: duplicatese0
Value: near-zeroe0

Verbatim

t0
“grapevine”
✓ located verbatim in the source · title, characters 53–62

e0
“effectively driving duplicates to near-zero while preserving recall”
✓ located verbatim in the source · abstract, characters 1652–1719

Title

One-stem-one-box: A consistency-driven framework for grapevine primary stem detection

Zhangqi Fang; Qian Zhang; Yonglei Tao; et al. Results in Engineering 2026. doi:10.1016/j.rineng.2026.109747

Other claims from this paper (3)

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Sensitivity analysis yielded two operating modes: a default favouring tighter localization and a recall-oriented one for crowded scenes.

Taxon: grapevineVitist0
Outcome: operating modese0
Value: twoe0

Verbatim

t0
“grapevine”
✓ located verbatim in the source · title, characters 53–62

e0
“Sensitivity analysis further yields two operating modes”
✓ located verbatim in the source · abstract, characters 2019–2074

Title

One-stem-one-box: A consistency-driven framework for grapevine primary stem detection

Zhangqi Fang; Qian Zhang; Yonglei Tao; et al. Results in Engineering 2026. doi:10.1016/j.rineng.2026.109747

Other claims from this paper (3)

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 / resourceobservedoriginal resultmeasured

Claim · model-written summary

SVM models achieved balanced accuracy (BACC) of 0.74-0.98.

Taxon: GrapevineVitist0
Outcome: BACCe1
Value: 0.74–0.98e1
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, BACC of 0.74–0.98”
✓ located verbatim in the source · abstract, characters 1565–1625

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 F1 scores of 0.67-1.00.
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.

Vitis 53,584 claims

53,584 claims