Acoustic detection of the wood borer, <i>Semanotus bifasciatus,</i> as an early monitoring technology-Reference-Cited by-同舟云学术

Acoustic detection of the wood borer, Semanotus bifasciatus, as an early monitoring technology

Published:2022-08-05 Issue:11 Volume:78 Page:4689-4699
ISSN:1526-498X
Container-title:Pest Management Science
language:en
Short-container-title:Pest Management Science

Author:

Jiang Qi¹²^ORCID,Liu Yujie¹²^ORCID,Ren Lili¹²,Sun Yu³^ORCID,Luo Youqing¹²

Affiliation:

1. Beijing Key Laboratory for Forest Pest Control Beijing Forestry University Beijing China

2. Sino‐French Joint Laboratory for Invasive Forest Pests in Eurasia Beijing Forestry University ‐French National Research Institute for Agriculture, Food and Environment (INRAE) Beijing China

3. School of Cyber Science and Technology Beihang University Beijing China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Insect Science,Agronomy and Crop Science,General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ps.7089

Reference39 articles.

1. Nationwide survey for invasive wood-boring and bark beetles (Coleoptera) using traps baited with pheromones and kairomones

2. Period of adult activity and response to wood moisture content as major segregating factors in the coexistence of two conifer longhorn beetles, Callidiellum rufipenne and Semanotus bifasciatus (Coleoptera: Cerambycidae)

3. Effective accumulated temperature and developmental threshold temperature for Semanotus bifasciatus (Motschulsky) in Beijing

4. Progress in research on Semanotus bifasciatus;Gao SL;For Pest Dis,2007

5. Summarization of the studies on Semanotus bifasciatus (Motschulsky);Li B;For Pest Dis,2003

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MelSPPNET—A self-explainable recognition model for emerald ash borer vibrational signals;Frontiers in Forests and Global Change;2024-04-24

2. A Time-Frequency Domain Mixed Attention-Based Approach for Classifying Wood-Boring Insect Feeding Vibration Signals Using a Deep Learning Model;Insects;2024-04-16

3. From buzzes to bytes: A systematic review of automated bioacoustics models used to detect, classify and monitor insects;Journal of Applied Ecology;2024-04-03

4. The interpretability of the activity signal detection model for wood‐boring pests Semanotus bifasciatus in the larval stage;Pest Management Science;2023-06-12