Claim · model-written summary
ShuffleNet computation took 13.5902 seconds.
- Taxon
- Wheat
Triticum aestivumt0 - Outcome
- computational timese0
- Value
- 13.5902 secondse0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The computational times for MobileNetV2 and ShuffleNet were 14.2697 seconds and 13.5902 seconds, respectively”
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 MobileNetV2 F1 score was 98.67%.
- 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 MobileNetV2 had superior overall wheat leaf rust detection performance.