Başak Demirci

I've built a real-time tomato leaf anomaly detection system for a TÜBİTAK 2209-A research project, using YOLOv8, OpenCV, and Python. I trained a dataset of approximately 10,000 images and achieved 99.3% mAP@50, 97.4% precision, and 98.1% recall.

I optimized the model for Raspberry Pi 5 deployment to enable real-time embedded AI inference for early plant anomaly detection.

During my internship at PAR-SAN, I supported troubleshooting and maintenance of PLC-based industrial automation systems with Siemens TIA Portal, including fault analysis, program updates, and root-cause investigations. At GGTEK, I contributed to RFID projects and supported microchip testing and validation.

I've also designed a capacitive sensing system with STM32 and C, implementing ADC-based acquisition, signal filtering, calibration, and MATLAB analysis. I'm motivated by intelligent systems, practical engineering problems, and learning emerging technologies.