Claim · model-written summary
Layer-by-layer parameter analysis showed a well-structured model architecture.
- Outcome
- well-structured architecturee0
Not reportedTaxonMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionValueQualifier
Verbatim
e0“The layer-by-layer parameter analysis performed on the model demonstrated a well-structured architecture, and the precisionrecall study highlighted the model's equilibrium between accurate classification and recall of information.”
Title
Hybrid Deep Learning for Wheat Bunt Disease Severity Assessment
Shobhit; Er. Gagninder Kaur; Parshant Singh; et al. 2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech) 2023. doi:10.1109/icacctech61146.2023.00122
Other claims from this paper (4)
- Method / resource A dataset of 15,000 images covered six wheat bunt severity levels.
- Method / resource The hybrid model classified wheat bunt severity with 93.2% overall accuracy.
- Method / resource Precision-recall analysis indicated balance between classification precision and recall.
- Method / resource The hybrid model performed competitively against state-of-the-art models.