YOLOv8 showed greater robustness and accuracy than YOLOv5 for real-time wheat disease detection.

Claim · model-written summary

YOLOv8 showed greater robustness and accuracy than YOLOv5 for real-time wheat disease detection.

Taxon

WheatTriticum aestivumt0

Outcome

robustness and accuracye2

Direction

greatere2

Qualifier

for real-time detectione2

Comparator

YOLOv5e1

Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValue

Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Verbatim

t0

“Wheat disease monitoring using deep learning”

e1

“The results show that the YOLOv5 and YOLOV8 models achieved good performance”

e2

“YOLOv8 offers greater robustness and accuracy for real-time detection”