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Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The SeedlingNet deep learning model classified wheat seedling varieties with 99.26% accuracy, outperforming traditional machine learning methods and mainstream deep learning models.

Taxon: wheatTriticum aestivumt0
Outcome: classification accuracye0
Direction: outperforminge0
Value: 99.26%e0
Comparator: traditional machine learning methods and mainstream deep learning modelse0

Verbatim

t0
“wheat seedling variety recognition”
✓ located verbatim in the source · title, characters 59–93

e0
“SeedlingNet achieves a remarkable classification accuracy of 99.26%, outperforming traditional machine learning methods and mainstream deep learning models”
✓ located verbatim in the source · abstract, characters 923–1078

Title

Multi branch model based on cross scale feature fusion for wheat seedling variety recognition

Zhang Wenbo; Zhang Ziyang; Xi Chengyu; et al. Smart Agricultural Technology 2026. doi:10.1016/j.atech.2026.101785

Other claims from this paper (2)

Method / resourceobservedoriginal resultbasis n/a

Claim · model-written summary

The authors established an image dataset of 13,600 images representing 17 wheat varieties at the early growth stage.

Taxon: wheatTriticum aestivume0
Organ / stage: early growth stagee0
Outcome: image datasete0
Value: 13,600 imagese0

Verbatim

e0
“We established a comprehensive image dataset of 13,600 images representing 17 wheat varieties at the early growth stage”
✓ located verbatim in the source · abstract, characters 764–883

Title

Multi branch model based on cross scale feature fusion for wheat seedling variety recognition

Zhang Wenbo; Zhang Ziyang; Xi Chengyu; et al. Smart Agricultural Technology 2026. doi:10.1016/j.atech.2026.101785

Other claims from this paper (2)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

An HPLC-PDA method was developed that simultaneously and accurately quantifies 37 representative phenolic compounds in plant samples.

Outcome: simultaneous accurate quantification of 37 representative phenolic compoundse0
Value: 37e0

Verbatim

e0
“developed a high-performance liquid chromatography-photodiode array detection (HPLC-PDA)-based analytical technique for plant phenolics, enabling simultaneous accurate quantification of 37 representative phenolic compounds in samples”
✓ located verbatim in the source · abstract, characters 149–382

Title

HPLC-PDA method for simultaneous quantification of 37 phenolic compounds: Method validation and analysis of 19 plant samples

Zhihao Yang; Tianle Shen; Jinhu Tian; et al. Journal of Food Composition and Analysis 2026. doi:10.1016/j.jfca.2026.108867

Other claims from this paper (4)

Method / resource Method validation showed excellent linearity, precision and accuracy.
Method / resource Intra-day and inter-day precision were below 3%.
Method / resource Spiked recoveries ranged from 96.88% to 103.75%.
Method / resource The method was successfully applied to analyze 19 distinct plant specimens.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Method validation showed excellent linearity, precision and accuracy.

Outcome: linearity, precision, and accuracye0
Value: excellente0

Verbatim

e0
“Method validation demonstrated excellent linearity, precision, and accuracy”
✓ located verbatim in the source · abstract, characters 384–459

Title

HPLC-PDA method for simultaneous quantification of 37 phenolic compounds: Method validation and analysis of 19 plant samples

Zhihao Yang; Tianle Shen; Jinhu Tian; et al. Journal of Food Composition and Analysis 2026. doi:10.1016/j.jfca.2026.108867

Other claims from this paper (4)

Method / resource An HPLC-PDA method was developed that simultaneously and accurately quantifies 37 representative phenolic compounds in plant samples.
Method / resource Intra-day and inter-day precision were below 3%.
Method / resource Spiked recoveries ranged from 96.88% to 103.75%.
Method / resource The method was successfully applied to analyze 19 distinct plant specimens.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Intra-day and inter-day precision were below 3%.

Outcome: intra-day and inter-day precisione0
Value: below 3%e0

Verbatim

e0
“intra-day and inter-day precision below 3%”
✓ located verbatim in the source · abstract, characters 466–508

Title

HPLC-PDA method for simultaneous quantification of 37 phenolic compounds: Method validation and analysis of 19 plant samples

Zhihao Yang; Tianle Shen; Jinhu Tian; et al. Journal of Food Composition and Analysis 2026. doi:10.1016/j.jfca.2026.108867

Other claims from this paper (4)

Method / resource An HPLC-PDA method was developed that simultaneously and accurately quantifies 37 representative phenolic compounds in plant samples.
Method / resource Method validation showed excellent linearity, precision and accuracy.
Method / resource Spiked recoveries ranged from 96.88% to 103.75%.
Method / resource The method was successfully applied to analyze 19 distinct plant specimens.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Spiked recoveries ranged from 96.88% to 103.75%.

Outcome: spiked recoveriese0
Value: 96.88% to 103.75%e0

Verbatim

e0
“spiked recoveries ranging from 96.88% to 103.75%”
✓ located verbatim in the source · abstract, characters 514–562

Title

HPLC-PDA method for simultaneous quantification of 37 phenolic compounds: Method validation and analysis of 19 plant samples

Zhihao Yang; Tianle Shen; Jinhu Tian; et al. Journal of Food Composition and Analysis 2026. doi:10.1016/j.jfca.2026.108867

Other claims from this paper (4)

Method / resource An HPLC-PDA method was developed that simultaneously and accurately quantifies 37 representative phenolic compounds in plant samples.
Method / resource Method validation showed excellent linearity, precision and accuracy.
Method / resource Intra-day and inter-day precision were below 3%.
Method / resource The method was successfully applied to analyze 19 distinct plant specimens.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The method was successfully applied to analyze 19 distinct plant specimens.

Outcome: applied to analyze 19 distinct plant specimense0
Value: 19e0

Verbatim

e0
“The methodology was successfully applied to analyze 19 distinct plant specimens”
✓ located verbatim in the source · abstract, characters 564–643

Title

HPLC-PDA method for simultaneous quantification of 37 phenolic compounds: Method validation and analysis of 19 plant samples

Zhihao Yang; Tianle Shen; Jinhu Tian; et al. Journal of Food Composition and Analysis 2026. doi:10.1016/j.jfca.2026.108867

Other claims from this paper (4)

Method / resource An HPLC-PDA method was developed that simultaneously and accurately quantifies 37 representative phenolic compounds in plant samples.
Method / resource Method validation showed excellent linearity, precision and accuracy.
Method / resource Intra-day and inter-day precision were below 3%.
Method / resource Spiked recoveries ranged from 96.88% to 103.75%.

Method / resourceobservedoriginal resultbasis n/a

Claim · model-written summary

The CocoaMoniliaDataSet comprises 1953 images of cocoa pods with four labeled classes.

Taxon: cocoaTheobroma cacaot0
Interactor: Monilia roreriMoniliophthora rorerit0pathogen
Outcome: labeled classese1
Value: 1953 imagese1

Verbatim

t0
“A cocoa pod dataset to detect and classify Monilia roreri”
✓ located verbatim in the source · title, characters 21–78

e1
“The dataset comprises 1953 images with four labeled classes”
✓ located verbatim in the source · abstract, characters 1272–1331

Title

CocoaMoniliaDataSet: A cocoa pod dataset to detect and classify Monilia roreri in real conditions

Joan Alvarado; Juan Felipe Restrepo-Arias; David Velásquez; et al. Data in Brief 2026. doi:10.1016/j.dib.2025.112447

Other claims from this paper (2)

Method / resourceobservedoriginal resultbasis n/a

Claim · model-written summary

Labels of the cocoa pod images are provided in COCO 1.0, YOLO and segmentation mask 1.1 formats for training object detection algorithms.

Taxon: cocoaTheobroma cacaot0
Outcome: labels are providede1
Value: COCO 1.0, YOLO, and segmentation mask 1.1 formate1

Verbatim

t0
“A cocoa pod dataset to detect and classify Monilia roreri”
✓ located verbatim in the source · title, characters 21–78

e1
“the resulting labels are provided in COCO 1.0, YOLO, and segmentation mask 1.1 format”
✓ located verbatim in the source · abstract, characters 1644–1729

Title

CocoaMoniliaDataSet: A cocoa pod dataset to detect and classify Monilia roreri in real conditions

Joan Alvarado; Juan Felipe Restrepo-Arias; David Velásquez; et al. Data in Brief 2026. doi:10.1016/j.dib.2025.112447

Other claims from this paper (2)

Method / resource The CocoaMoniliaDataSet comprises 1953 images of cocoa pods with four labeled classes.
Observation Monilia roreri causes yield losses of approximately 30-40% in cocoa.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The improved MSW-YOLOv8n-Seg model reached bounding box precision of 90.8% on the test sets for cherry tomato maturity segmentation.

Taxon: cherry tomatoSolanum lycopersicum var. cerasiformet0
Outcome: bounding box precisione0
Value: 90.8%e0

Verbatim

t0
“cherry tomato”
✓ located verbatim in the source · title, characters 70–83

e0
“the bounding box precision, recall and mean average precision (mAP)@0.5 of the improved model on the test sets were 90.8%, 86.3% and 83.9% respectively”
✓ located verbatim in the source · abstract, characters 1393–1544

Title

Lightweight MSW-YOLOv8n-Seg: the instance segmentation of maturity on cherry tomato with improved YOLOv8n-Seg

Ronghui Miao; Zhiwei Li Frontiers in Plant Science 2026. doi:10.3389/fpls.2025.1731580

Other claims from this paper (8)

Methods & resources 684,532 claims

684,532 claims