Hyperparameter optimization of YOLOv4 tiny for palm oil fresh fruit bunches maturity detection using genetics algorithms

Indonesia's economic progress is closely linked to palm oil, a crucial plantation commodity. The price of this oil relies on the oil content found in the fruit of the oil palm, which is gauged by the fruit's maturity level, a task usually undertaken by experts. The maturity of oil palm FFB is generally divided into several classes, commonly unripe, under-ripe, ripe, and over-ripe. However, the detection of maturity still uses human vision, which causes detection errors. Therefore, this research will use the computer vision method with the YOLOv4 model to detect the maturity of the oil palm fruit. YOLOv4 itself is a real-time object detection model that has been widely used to detect fruit maturity, including oil palm fruit; this research uses 4,160 images of oil palm fruit maturity which is divided into six categories, namely empty fruit, underripe, abnormal, ripe, under-ripe, and over-ripe. The YOLOv4 model trained using the dataset will be optimized using GA for looking at the optimal hyperparameter to produce a better model. The result is YOLOv4 model experienced an increase in mAP compared to before using GA hyperparameter optimization.