Method / resource 130,773 claims

Claim · model-written summary

GL-MobFormer achieved a classification accuracy of 95.13% on the tea leaf image dataset.

Taxon

teat0

Outcome

classification accuracye0

Value

95.13%e0

Verbatim

t0

“tea leaf phytopathogen diagnosis”

e0

“GL-MobFormer achieved a classification accuracy of 95.13%”

Claim · model-written summary

GL-MobFormer reached a Matthews Correlation Coefficient of 0.9417.

Taxon

teat0

Outcome

Matthews Correlation Coefficient (MCC)e0

Value

0.9417e0

Verbatim

t0

“tea leaf phytopathogen diagnosis”

e0

“a Matthews Correlation Coefficient (MCC) of 0.9417”

Claim · model-written summary

GL-MobFormer maintained its performance with a low computational footprint of 0.33 G FLOPs.

Taxon

teat0

Outcome

computational footprinte0

Value

0.33 G FLOPse0

Verbatim

t0

“tea leaf phytopathogen diagnosis”

e0

“a low computational footprint of merely 0.33 G FLOPs”

Claim · model-written summary

Masking only the top 5% of critical activation regions reduced classification confidence by an average of 70.61%, indicating the model relies on lesion features rather than background.

Taxon

teat0

Treatment

masking only the top 5% of critical activation regionse0

Outcome

classification confidencee0

Direction

reductione0

Value

70.61%e0

Verbatim

t0

“tea leaf phytopathogen diagnosis”

e0

“systematically masking only the top 5% of critical activation regions led to an average reduction of 70.61% in classification confidence”

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”

e1

“The dataset comprises 1953 images with four labeled classes”

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”

e1

“the resulting labels are provided in COCO 1.0, YOLO, and segmentation mask 1.1 format”

Claim · model-written summary

The fine-tuned EfficientNetB0 model achieved 99.51% test accuracy in detecting crop diseases.

Outcome

test accuracye0

Value

99.51%e0

Verbatim

e0

“It achieved 99.51% test accuracy with a loss of 0.0165”

Claim · model-written summary

The model had a test loss of 0.0165.

Outcome

losse0

Value

0.0165e0

Verbatim

e0

“It achieved 99.51% test accuracy with a loss of 0.0165”

Claim · model-written summary

A deep learning model identified Palmer amaranth and waterhemp with 93% accuracy, the most successful among the algorithms explored.

Outcome

accuracye1

Value

93%e1

Verbatim

e1

“Their deep learning model was most successful, achieving 93% accuracy in identifying the two species”

Claim · model-written summary

Hybrid soft voting achieved accuracy similar to the strongest individual transfer learning model on the cucumber disease dataset.

Taxon

CucumberCucumis sativuse0

Outcome

accuracye1

Comparator

individual modele1

Verbatim

e0

“the Cucumber dataset with multiple diseases”

e1

“Hybrid soft voting evaluated similar accuracy to an individual model that is strong accuracy transfer learning trained model, but inference time is much less than that of these strong models”