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This study presents a real-time detection system for coffee cherry ripeness using the YOLOv11 algorithm, aimed at assisting farmers in optimal harvest timing. The model, trained on a dataset of 302 images across three ripeness levels, achieved strong performance in detecting ripe cherries but lower accuracy for unripe ones. With an inference time of 183.4 ms per image, the system is designed for efficient real-time applications in agricultural automation.

Author

Ardhito Primatama

Language

EN