Acoustic Detection of Forest Wood-Boring Insects Under Co-Infestations.

Qi Jiang, Yujie Liu, Yu Sun, Lili Ren, Youqing Luo

Abstract read

In one paragraph

Article in Insects, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Qi JiangResearch Center for Natural Protected Areas Monitoring, Yunnan Institute of Forest Inventory and Planning, Kunming 650051, China.ORCID 0000-0003-3907-0885

Yujie LiuLaboratory for Pest Monitoring, Yinglin Branch Yunnan Institute of Forest Inventory and Planning, Kunming 650032, China.

Yu SunSchool of Cyber Science and Technology, Beihang University, Beijing 100191, China.

Lili RenBeijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing 100083, China.ORCID 0000-0003-0333-0681

Youqing LuoBeijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing 100083, China.ORCID 0000-0001-5306-8306

Funding

Fundamental Research Funds for the Central Universities QNTD202510

6 · The paper itself

Abstract

Acoustic detection technology has emerged as a promising, non-destructive and continuous monitoring method for pest early detection at the single tree level. However, field application still encounters problems, especially under complex infestation scenarios, i.e., co-infestations by multiple pest species. This study aims to develop a novel acoustic-based recognition model for detecting forest wood-boring pests, specially designed to enhance monitoring accuracy under complex infestation scenarios. We collected feeding vibration signals from four wood-boring pests:

Indexed as

acoustic detectionco-infestation scenariosdeep learning algorithmsfeeding vibration signalswood-boring insect

Identifiers

PMID41465679

DOI10.3390/insects16121241

PMCPMC12733569

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.