Evidence map›Paper›PMID 41608612›Full record

ArticleFrontiers in plant science2025

MSCF-LUNet: a lightweight three-stage pine wilt disease segmentation model with multi-scale context fusion mechanism.

Dejing Zhou, Junxian Chen, Wenxi Cai, Jie Lin, Tiantian Meng, Yuanhang Li, Baihan Liu, Mengting Luo, Yubin Lan, Tianyi Liu and 1 more

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In one paragraph

Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

11 authors.

Dejing Zhou *College of Electronic Engineering, South China Agricultural University, Guangzhou, China.
Junxian Chen *College of Electronic Engineering, South China Agricultural University, Guangzhou, China.
Wenxi CaiCollege of Electronic Engineering, South China Agricultural University, Guangzhou, China.
Jie LinCollege of Electronic Engineering, South China Agricultural University, Guangzhou, China.
Tiantian MengCollege of Electronic Engineering, South China Agricultural University, Guangzhou, China.
Yuanhang LiCollege of Electronic Engineering, South China Agricultural University, Guangzhou, China.
Baihan LiuCollege of Electronic Engineering, South China Agricultural University, Guangzhou, China.
Mengting LuoCollege of Electronic Engineering, South China Agricultural University, Guangzhou, China.
Yubin LanCollege of Electronic Engineering, South China Agricultural University, Guangzhou, China.
Tianyi LiuCollege of Electronic Engineering, South China Agricultural University, Guangzhou, China.
Jing ZhaoCollege of Electronic Engineering, South China Agricultural University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Pine wilt disease (PWD) is a highly destructive infectious disease that severely damages pine forests worldwide. Because symptoms emerge first in the tree crown, detection from unmanned aerial vehicles (UAVs) is efficient. However, most methods perform only binary classification and lack pixel-level staging, which leads to missed initial symptoms and confusion with other species. Methods: We propose MSCF-LUNet, a lightweight three-stage semantic segmentation model based on multi-scale context fusion. The model uses an improved multi-scale patch embedding guided by attention with relative position encoding (AWRP) to adapt the sampling field of view and to fuse local details with global context. Under contextual attention, the network learns fine-grained features and location cues. Results: In complex environments, MSCF-LUNet achieves 89.56% precision, 92.13% recall, 88.92% intersection over union (IoU), and 96.54% pixel accuracy (PA), balancing performance and computational cost. Discussion: The model effectively segments PWD-infected regions and determines disease stages from remote-sensing imagery.

Indexed as

complex forest stand environmentlightweightmulti-scale feature fusionpine wilt diseaseremote sensing image technologysegmentation

Identifiers

PMID41608612
PMCPMC12835376

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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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.