Claim · model-written summary
The full AGMS-FF variant achieved 99.84% accuracy.
- Taxon
- tomato
Solanum lycopersicumt0 - Outcome
- accuracye0
- Value
- 99.84%e0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“99.84% accuracy for full AGMS-FF”
t0“tomato”
Title
A novel YOLOv11 framework for enhanced tomato disease detection
Entesar Hamed I. Eliwa; Tarek Abd El-Hafeez PeerJ Computer Science 2025. doi:10.7717/peerj-cs.3200
Other claims from this paper (6)
- Method / resource Enhanced YOLOv11 achieved 99.93% overall accuracy for tomato disease classification.
- Method / resource Many disease classes reached 100.00% precision, recall and F1 scores.
- Method / resource The model required 126 min for training.
- Method / resource The model inference time was 31.4 ms.
- Method / resource The model used 3.2 GB of memory.
- Method / resource The model throughput was 38.2 FPS.