Machine learning based blueberry detection method by CIE-YOLOv5

Yaning Zhai; Zhantao Liang; Ling Zhang; Shaolei Xu; Chengzi Huang; Xuelian Zhou; Mingfeng Li; Yang Huang

doi:10.1117/12.3026626

27 March 2024 Machine learning based blueberry detection method by CIE-YOLOv5

Yaning Zhai, Zhantao Liang, Ling Zhang, Shaolei Xu, Chengzi Huang, Xuelian Zhou, Mingfeng Li, Yang Huang

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131051U (2024) https://doi.org/10.1117/12.3026626
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yaning Zhai, Zhantao Liang, Ling Zhang, Shaolei Xu, Chengzi Huang, Xuelian Zhou, Mingfeng Li, and Yang Huang "Machine learning based blueberry detection method by CIE-YOLOv5", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131051U (27 March 2024); https://doi.org/10.1117/12.3026626

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