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This research presents the Ghost-Net_Triplet_YOLOv8s algorithm, an enhancement of YOLO v8s for detecting corn leaf diseases, which integrates a lightweight GhostNet structure and a Triplet Attention mechanism to improve detection speed and accuracy. The algorithm achieves a precision rate of 87.50%, recall rate of 87.70%, and [email protected] of 91.40%, while reducing model size by 50.2% and FLOPs by 43.1%. Additionally, it has been implemented in a WeChat mini-program for real-time disease detection in maize fields

Author

Lenin Yuquilima

Language

EN