Imaging of Insect Hole in Living Tree Trunk Based on Joint Driven Algorithm of Electromagnetic Inverse Scattering.

Jiayin Song, Jie Shi, Hongwei Zhou, Wenlong Song, Hongju Zhou, Yue Zhao

Open access · goldAbstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2022. 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

0.3field-weighted citation impact, top 36% of its field

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, 5 citations in OpenAlex.

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

6 authors at 1 institution in 1 country.

Jiayin SongDepartment of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China.

Jie ShiDepartment of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China.

Hongwei ZhouDepartment of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China.

Wenlong SongDepartment of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China.

Hongju ZhouDepartment of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China.

Yue ZhaoDepartment of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China.

Northeast Forestry University · CN

Funding

Northeast Forestry University 2572022DP04State Forestry and Grassland Administration Hei[2022]TG21

6 · The paper itself

Abstract

Trunk pests have always been one of the most important species of tree pests. Trees eroded by trunk pests will be blocked in the transport of nutrients and water and will wither and die or be broken by strong winds. Most pests are social and distributed in the form of communities inside trees. However, it is difficult to know from the outside if a tree is infected inside. A new method for the non-invasive detecting of tree interiors is proposed to identify trees eroded by trunk pests. The method is based on electromagnetic inverse scattering. The scattered field data are obtained by an electromagnetic wave receiver. A Joint-Driven algorithm is proposed to realize the electromagnetic scattered data imaging to determine the extent and location of pest erosion of the trunk. This imaging method can effectively solve the problem of unclear imaging in the xylem of living trees due to the small area of the pest community. The Joint-Driven algorithm proposed by our group can achieve accurate imaging with a ratio of pest community radius to live tree radius equal to 1:60 under the condition of noise doping. The Joint-Driven algorithm proposed in this paper reduces the time cost and computational complexity of tree internal defect detection and improves the clarity and accuracy of tree internal defect inversion images.

Indexed as

InsectaTreesAlgorithmsAnimalsDiagnostic ImagingElectromagnetic Radiationconvolutional neural networkelectromagnetic inverse scatteringnon-invasive

Identifiers

PMID36560214

DOI10.3390/s22249840

PMCPMC9782739

OpenAlexW4311793369

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

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None linked

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.