Evidence map›Paper›PMID 39843759›Full record

ArticleScientific reports2025

MD-Unet for tobacco leaf disease spot segmentation based on multi-scale residual dilated convolutions.

Zili Chen, Yilong Peng, Jiadong Jiao, Aiguo Wang, Laigang Wang, Wei Lin, Yan Guo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

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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

7 authors.

Zili ChenInstitute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou, 450002, China.
Yilong PengInstitute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou, 450002, China.
Jiadong JiaoCollege of Computer and Information Engineering, Henan Normal University/Henan Provincial Key Laboratory of Educational Artificial Intelligence and Personalized Learning, Xinxiang, 453007, Henan, China.
Aiguo WangZhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China.
Laigang WangInstitute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou, 450002, China.
Wei LinCollege of Computer and Information Engineering, Henan Normal University/Henan Provincial Key Laboratory of Educational Artificial Intelligence and Personalized Learning, Xinxiang, 453007, Henan, China. 121025@htu.edu.cn.
Yan GuoInstitute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou, 450002, China. 10914063@zju.edu.cn.

Funding

Henan Academy of Agricultural Sciences Remote Sensing Innovation Team: Research and Development of High-Precision Remote Sensing Monitoring Technology for Agricultural Information from Ground and Space 2024TD28Science and Technology Project of the China National Tobacco Corporation Henan Company: "Henan Strong Aroma" High-Quality Raw Material Production Process Intelligent Monitoring Technology System Construction 2023410000240025
6 · The paper itself

Abstract

Identification and diagnosis of tobacco diseases are prerequisites for the scientific prevention and control of these ailments. To address the limitations of traditional methods, such as weak generalization and sensitivity to noise in segmenting tobacco leaf lesions, this study focused on four tobacco diseases: angular leaf spot, brown spot, wildfire disease, and frog eye disease. Building upon the Unet architecture, we developed the Multi-scale Residual Dilated Segmentation Model (MD-Unet) by enhancing the feature extraction module and integrating attention mechanisms. The results demonstrated that MD-Unet achieved 92.75%, 90.94%, 84.93%, and 91.81% for the lesion CPA, recall, IoU, and F1 metrics, respectively, with an overall Dice score of 94.67%. Furthermore, the model parameters, floating-point operations, and inference time per single image for MD-Unet were 4.65 × 10

Indexed as

Image Processing, Computer-AssistedNicotianaPlant DiseasesPlant LeavesAlgorithmsConvolutional neural networksDeep learningLesion segmentationTobacco leaf diseases

Identifiers

PMID39843759
PMCPMC11754756

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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.