Detection and recognition of the invasive species, Hylurgus ligniperda, in traps, based on a cascaded convolution neural network.

Journal: Pest management science
PMID: 38629795

Abstract

BACKGROUND: Hylurgus ligniperda, an invasive species originating from Eurasia, is now a major forestry quarantine pest worldwide. In recent years, it has caused significant damage in China. While traps have been effective in monitoring and controlling pests, manual inspections are labor-intensive and require expertise in insect classification. To address this, we applied a two-stage cascade convolutional neural network, YOLOX-MobileNetV2 (YOLOX-Mnet), for identifying H. ligniperda and other pests captured in traps. This method streamlines target and non-target insect detection from trap images, offering a more efficient alternative to manual inspections.

Authors

  • Xiahui Zhang
    The Key Laboratory of Forest Pest Control, College of Forestry, Beijing Forestry University, Beijing, China.
  • Zhengyi Li
    Department of Ultrasound, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China.
  • Lili Ren
    Key Laboratory of Bionic Engineering Ministry of Education Jilin University Changchun Jilin 130022 P. R. China.
  • Xuanxin Liu
    School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China.
  • Tian Zeng
    Communication Service Center of Transportation Bureau of Chengde City, Chengde, China.
  • Jing Tao
    Department of Obstetrics and Gynecology, The Affiliated Hangzhou People's Hospital of Nanjing Medical University, Hangzhou, China.

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