Claim · model-written summary
Conv-7 DCNN classifies pepper-bell leaf diseases within a fifteen-category system.
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionQualifier
Verbatim
e0“The proposed Conv-7 DCNN model classifies the leaf diseases of three plants such as tomato, potato, and pepper-bell into fifteen categories.”
Title
DeepGreen: a real-time deep learning system for smart agriculture monitoring
Ajit Singh Rathor; Sushabhan Choudhury; Abhinav Sharma; et al. Scientific Reports 2026. doi:10.1038/s41598-026-45395-3
Other claims from this paper (10)
- Method / resource Conv-7 DCNN classifies tomato leaf diseases within a fifteen-category system.
- Method / resource Conv-7 DCNN classifies potato leaf diseases within a fifteen-category system.
- Method / resource The model outperformed several pretrained and trending deep learning models.
- Method / resource Classification accuracy was 99.18%.
- Method / resource Average precision was 99.17%.
- Method / resource Area under the curve was 1.
- Method / resource Conv-7 DCNN achieved FPS of 112.49.
- Method / resource Inference time was 18.34 s.
- Method / resource GFLOPS was 13.98.
- Recommendation The model is suitable for real-time smart agriculture applications.