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An Improved YOLOv5-Based Tapping Trajectory Detection Method for Natural Rubber Trees

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  • Zejin Sun

    (Mechanical and Electrical Engineering College, Hainan University, Haikou 570228, China)

  • Hui Yang

    (Mechanical and Electrical Engineering College, Hainan University, Haikou 570228, China)

  • Zhifu Zhang

    (Mechanical and Electrical Engineering College, Hainan University, Haikou 570228, China)

  • Junxiao Liu

    (Mechanical and Electrical Engineering College, Hainan University, Haikou 570228, China
    Sanya Nanfan Research Institute, Hainan University, Sanya 572025, China)

  • Xirui Zhang

    (Mechanical and Electrical Engineering College, Hainan University, Haikou 570228, China
    Sanya Nanfan Research Institute, Hainan University, Sanya 572025, China)

Abstract

The object detection algorithm is one of the core technologies of the intelligent rubber tapping robot, but most of the existing detection algorithms cannot effectively meet the tapping trajectory detection of natural rubber trees in the complex forest environment. This paper proposes a tapping trajectory detection method for natural rubber trees based on an improved YOLOv5 model to accomplish fast and accurate detection. Firstly, the coordinate attention (CA) mechanism is added to the Backbone network to embed the location information into the channel attention, which effectively improves the detection accuracy. Secondly, a module called convolution and GhostBottleneck (CGB) is designed, based on the Ghost module, to substitute the Cross Stage Partial Network (CSP) module in the Neck network, which ensures the detection accuracy while reducing model parameters. Finally, the EIoU loss function is introduced to enable a more accurate regression of the model. The experimental results show that the overall performance of the YOLOv5-CCE model outperforms the original YOLOv5 and other classical lightweight detection algorithms. Compared with the original YOLOv5 model, the YOLOv5-CCE model has a 2.1% improvement in mAP value, a 2.5% compression of model parameters, and a 7.0% reduction in the number of floating point operations (FLOPs). Therefore, the improved model can fully meet the requirements of real-time detection, providing a robust detection method for rubber tapping robots.

Suggested Citation

  • Zejin Sun & Hui Yang & Zhifu Zhang & Junxiao Liu & Xirui Zhang, 2022. "An Improved YOLOv5-Based Tapping Trajectory Detection Method for Natural Rubber Trees," Agriculture, MDPI, vol. 12(9), pages 1-19, August.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1309-:d:897936
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    References listed on IDEAS

    as
    1. Chuandong Zhang & Huali Ding & Qinfeng Shi & Yunfei Wang, 2022. "Grape Cluster Real-Time Detection in Complex Natural Scenes Based on YOLOv5s Deep Learning Network," Agriculture, MDPI, vol. 12(8), pages 1-12, August.
    2. Haiqing Wang & Shuqi Shang & Dongwei Wang & Xiaoning He & Kai Feng & Hao Zhu, 2022. "Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model," Agriculture, MDPI, vol. 12(7), pages 1-23, June.
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