Developed an Intelligent Olive Monitoring System on Raspberry Pi 3 for IoT, leveraging Python and the YOLOv8 model with PyTorch for object recognition, and integrating Raspbian OS for seamless operation and real-time data visualization. Achieved strong model performance, reaching 89% average precision, 79% average F1-score, and 99% average recall in detecting and classifying 3 types of olive anomalies.