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YOLO-Sp: A Novel Transformer-Based Deep Learning Model for Achnatherum splendens Detection

Author

Listed:
  • Yuzhuo Zhang

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Tianyi Wang

    (College of Engineering, China Agricultural University, Beijing 100083, China
    College of Agricultural Unmanned System, China Agricultural University, Beijing 100193, China)

  • Yong You

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Decheng Wang

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Dongyan Zhang

    (National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China)

  • Yuchan Lv

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Mengyuan Lu

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Xingshan Zhang

    (College of Engineering, China Agricultural University, Beijing 100083, China)

Abstract

The growth of Achnatherum splendens ( A. splendens ) inhibits the growth of dominant grassland herbaceous species, resulting in a loss of grassland biomass and a worsening of the grassland ecological environment. Therefore, it is crucial to identify the dynamic development of A. splendens adequately. This study intended to offer a transformer-based A. splendens detection model named YOLO-Sp through ground-based visible spectrum proximal sensing images. YOLO-Sp achieved 98.4% and 95.4% AP values in object detection and image segmentation for A. splendens , respectively, outperforming previous SOTA algorithms. The research indicated that Transformer had great potential for monitoring A. splendens . Under identical training settings, the AP value of YOLO-Sp was greater by more than 5% than that of YOLOv5. The model’s average accuracy was 98.6% in trials conducted at genuine test sites. The experiment revealed that factors such as the amount of light, the degree of grass growth, and the camera resolution would affect the detection accuracy. This study could contribute to the monitoring and assessing grass plant biomass in grasslands.

Suggested Citation

  • Yuzhuo Zhang & Tianyi Wang & Yong You & Decheng Wang & Dongyan Zhang & Yuchan Lv & Mengyuan Lu & Xingshan Zhang, 2023. "YOLO-Sp: A Novel Transformer-Based Deep Learning Model for Achnatherum splendens Detection," Agriculture, MDPI, vol. 13(6), pages 1-18, June.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:6:p:1197-:d:1163771
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    Cited by:

    1. Jingyu Wang & Miaomiao Li & Chen Han & Xindong Guo, 2024. "YOLOv8-RCAA: A Lightweight and High-Performance Network for Tea Leaf Disease Detection," Agriculture, MDPI, vol. 14(8), pages 1-20, July.

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