Claim · model-written summary
MobileNetV2 F1 score was 98.67%.
- Taxon
- Wheat
Triticum aestivumt0 - Outcome
- F1 scorese0
- Value
- 98.67%e0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The respective F1 scores for MobileNetV2 and ShuffleNet were 98.67% and 94.87%”
t0“Wheat Leaf Rust Detection”
Title
Chilaka Rao; Santi Kumari Behera; Prabira Kumar Sethy 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) 2024. doi:10.1109/i-smac61858.2024.10714622
Other claims from this paper (16)
- Method / resource MobileNetV2 detected wheat leaf rust with 98.65% accuracy.
- Method / resource MobileNetV2 had 100% sensitivity.
- Method / resource MobileNetV2 had 97.30% specificity.
- Method / resource MobileNetV2 had 97.37% precision.
- Method / resource ShuffleNet detected wheat leaf rust with 94.59% accuracy.
- Method / resource ShuffleNet had 100% sensitivity.
- Method / resource ShuffleNet had 89.19% specificity.
- Method / resource ShuffleNet had 90.24% precision.
- Method / resource ShuffleNet F1 score was 94.87%.
- Method / resource MobileNetV2 MCC was 0.9733.
- Method / resource ShuffleNet MCC was 0.8971.
- Method / resource MobileNetV2 Kappa coefficient was 0.9730.
- Method / resource ShuffleNet Kappa coefficient was 0.8919.
- Method / resource MobileNetV2 computation took 14.2697 seconds.
- Method / resource ShuffleNet computation took 13.5902 seconds.
- Method / resource MobileNetV2 had superior overall wheat leaf rust detection performance.