In order to solve the problem of Apple recognition in complex environment, an apple target detection algorithm based on YOLOv5s pre training model is proposed. The apple orchard photos taken under different illumination and different angles were annotated and normalized, and the image was linearly smoothed by Gaussian filtering. The preprocessed data set is used to train the model, and the trained model is used to count the number, position and maturity of apples in the data set image. The results showed that the AP value of 0 (i.e. fully mature apple) was 0.974, indicating that the model had good detection effect on mature apple. The lightweight model is expected to be carried on the apple picking robot with embedded GPU to realize accurate identification and picking of mature apples and reduce the expensive labor cost.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Apple Recognition Algorithm Based on YOLOv5


    Contributors:
    Xu, Wenzhe (author) / Yao, Ziqian (author) / Zhou, Xuelin (author) / Wu, Mulei (author)


    Publication date :

    2024-10-23


    Size :

    999805 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Traffic Sign Recognition Algorithm Based on Improved YOLOv5

    Sang, Zhengxiao / Xia, Fuming / Huang, Han et al. | IEEE | 2022


    Traffic sign recognition based on YOLOv5

    Hou, Fujin / huo, Yanqiang / Lu, Youfu et al. | SPIE | 2022


    Ship Remote Sensing Target Recognition Based on YOLOV5

    Hao, Ning / Li, Yunwei / Ma, Yusen et al. | Springer Verlag | 2024


    Vehicle Recognition under Autonomous Driving Based on YOLOv5

    Zhou, Xiaozhou / Song, Hongwei / Gao, Jiaxing | IEEE | 2024