Claim · model-written summary
Improved YOLOv7-tiny outperformed its original version in detection accuracy but ranked below Improved YOLOv5s.
- Taxon
- Rice
Oryza sativat0 - Outcome
- detection accuracye0
- Direction
- outperformede0
- Comparator
- original YOLOv7-tinye0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Rice”
e0“Improved YOLOv7-tiny outperformed the original YOLOv7-tiny in detection accuracy, which was second only to Improved YOLOv5s.”
Title
Deep-Learning-Based Rice Disease and Insect Pest Detection on a Mobile Phone
Jizhong Deng; Chang Yang; Kanghua Huang; et al. Agronomy 2023. doi:10.3390/agronomy13082139
Other claims from this paper (14)
- Method / resource Improved YOLOv5s reached 0.931 F1-Score.
- Method / resource Improved YOLOv5s reached 0.961 mean average precision (mAP) (0.5).
- Method / resource Improved YOLOv5s reached 0.648 mAP (0.5:0.9).
- Method / resource Improved YOLOv5s reduced network parameters by 47.5.
- Method / resource Improved YOLOv5s reduced model size by 45.7.
- Method / resource Improved YOLOv5s reduced floating point operations per second (FLOPs) by 48.7%.
- Method / resource Improved YOLOv5s increased inference speed by 38.6% versus original YOLOv5s.
- Method / resource Improved YOLOv5s performed better on large rice disease and pest targets.
- Method / resource Improved YOLOv7-tiny was more accurate on small targets.
- Method / resource On mobile FP16, Improved YOLOv5s achieved 0.925 precision.
- Method / resource On mobile FP16, Improved YOLOv5s achieved 0.939 recall.
- Method / resource On mobile FP16, Improved YOLOv5s achieved 374 ms/frame inference speed.
- Method / resource Both improved models accurately identified rice diseases and insect pests.
- Method / resource The mobile application provided a reference for fast field diagnosis.