Real-Time Detection of Starch Gelatinization Using YOLO-based Deep Learning Models

This research proposes a novel, vision-based approach for real-time detection and confirmation of starch gelatinization using the state-of-the-art YOLO (You Only Look Once) deep learning models to overcome the limitations of traditional methods, which are manual and subjective for industrial control. The primary contribution of this work is twofold: it introduces a first-of-its-kind image-based framework for starch gelatinization detection, and it conclusively demonstrates that YOLO models provide a highly accurate and practical solution for automated, non-contact process control. A custom dataset was developed by capturing temporal images of a heated potato starch solution, with each frame annotated as "Non-Gelatinized" or "Gelatinized". Four YOLO architectures (v8, v9, v11, v12) were trained and evaluated on the developed dataset. All models demonstrated exceptional and nearly identical performance, achieving maximum precision, recall, and F1-scores (1.0), alongside a high mean Average Precision