Machine learning based blueberry detection method by CIE-YOLOv5

Aim to solve the problems of high-cost and low-efficiency in manual blueberry picking under harsh natural environment, an automatic blueberry identification using machine-learning YOLOv5 (You Only Look Once) algorithm and CIE saturation enhancement is proposed. The color contrast of blueberries between different ripeness is enhanced by saturation and desaturation processing in the CIE Lu'v' color space. The augmented image dataset is then trained by YOLOv5 to obtain a target detection model for both ripe and unripe blueberries. Experiments are conducted by comparing the original YOLOv5 framework detection and the augmented YOLO detection results. By CIE saturation enhancement, the identification precision in training, recall and the mean average precision are found to be increased with 3.0%, 2.0% and 2.6% respectively.