Detection of pine wood nematode infections in Chinese pine (Pinus tabuliformis) using hyperspectral drone images.

Journal: Pest management science
Published Date: May 31, 2025

Abstract

BACKGROUND: The pine wood nematode (PWN) has caused tremendous damage to pine forests in China. Accurately predicting the infestation stage of PWN is crucial for implementing appropriate management, such as chemically controlling early-infested trees and felling and removing trees in the severe stages of infestation. Unmanned aerial vehicle (UAV)-based hyperspectral technology can capture images with high spatial and spectral resolutions, facilitating more extensive coverage and enhanced detection efficiency. To date, few studies have used the correlation coefficient between full spectra and physiological traits to screen dual-band vegetation indices (VIs). Moreover, there is a lack of comprehensive comparison between the screened VIs, feature wavelengths, and full spectra using various machine learning methods to predict the infection stage of PWN.

Authors

  • Run Yu
    University of California Los Angeles (UCLA), Department of Internal Medicine, Division of Endocrinology, Diabetes, and Metabolism, Los Angeles, California.
  • Yujie Liu
    Department of Bone Tumor Surgery, Changzheng Hospital, Second Military Medical University, Shanghai, China.
  • Bingtao Gao
    Research Institute of Tropical Forestry, Chinese Academy of Forestry Guangzhou, Guangzhou, China.
  • Lili Ren
    Key Laboratory of Bionic Engineering Ministry of Education Jilin University Changchun Jilin 130022 P. R. China.
  • Youqing Luo
    Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing 100083, China.

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