The proposed model had an F1-score of 98.81%.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Outcome: ${F}1$ -scoree0
Value: 98.81%e0

Not reportedTaxonMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionQualifier

Verbatim

e0
“an ${F}1$ -score of 98.81%”
✓ located verbatim in the source · abstract, characters 1884–1910

Title

Enhanced CNN for Fruit Disease Detection and Grading Classification Using SSDAE-SVM for Postharvest Fruits

Himanshu B. Patel; Nitin J. Patil IEEE Sensors Journal 2023. doi:10.1109/jsen.2023.3342833

Other claims from this paper (4)

Method / resource The proposed model achieved 97.25% accuracy in fruit disease detection and grading, surpassing previous disease prediction models.
Method / resource The proposed model had a minimal prediction error of 0.15.
Method / resource The proposed model had a high specificity of 95.62%.
Method / resource The proposed model had a recall rate of 98.98%.

Record rev-b273f6a170682fd210c933913b104f3531c02b0166515a4c1b3bf6b0383cf81f · schema claims-v0.9 · position 4 in the model’s answer