Citrus Disease Detection Based on Dilated Reparam Feature Enhancement and Shared Parameter Head.

Xu Guo, Xingmeng Wang, Wenhao Zhu, Simon X Yang, Lepeng Song, Ping Li, Qinzheng Li

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 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.

LCWG-DETR: a wavelet-enhanced detection transformer for citrus disease detection improving visual perception in harvesting.Frontiers in plant science · 2026
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

7 authors.

Xu GuoSchool of Big Data and Automation, Chongqing Chemical Industry Vocational College, Chongqing 401228, China.

Xingmeng WangSchool of Electronic and Electrical Engineering, Chongqing University of Science & Technology, Chongqing 401331, China.

Wenhao ZhuSchool of Electronic and Electrical Engineering, Chongqing University of Science & Technology, Chongqing 401331, China.

Simon X YangAdvanced Robotics and Intelligent Systems Laboratory, School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada.ORCID 0000-0002-6888-7993

Lepeng SongSchool of Electronic and Electrical Engineering, Chongqing University of Science & Technology, Chongqing 401331, China.

Ping LiChongqing Academy of Agricultural Sciences, Chongqing 400039, China.

Qinzheng LiSchool of Electronic and Electrical Engineering, Chongqing University of Science & Technology, Chongqing 401331, China.

Funding

Research Foundation of Chongqing Education Committee KJQN202404518Research Foundation of Chongqing University of Science and Technology KJDX2024005

6 · The paper itself

Abstract

Accurate citrus disease identification is essential for targeted orchard pesticide application. Current models struggle with accuracy and efficiency due to diverse leaf lesion patterns and complex orchard environments. This study presents YOLOv8n-DE, an improved lightweight YOLOv8-based model for enhanced citrus disease detection. It introduces the DR module structure for effective feature enhancement and the Detect_Shared architecture for parameter efficiency. Evaluated on public and orchard-collected datasets, YOLOv8n-DE achieves 97.6% classification accuracy, 91.8% recall, and 97.3% mAP, with a 90.4% mAP for challenging diseases. Compared to the original YOLOv8, it reduces parameters by 48.17%, computational load by 59.26%, and model size by 41.94%, while significantly decreasing classification and regression errors, and false positives/negatives. YOLOv8n-DE offers outstanding performance and lightweight advantages for citrus disease detection, supporting precision agriculture development in orchards.

Indexed as

CitrusPlant DiseasesAgricultureAlgorithmsPlant Leavescitrus disease detectiondilated reparam feature enhancementreal-time detectionshared parameter headYOLOv8n-DE

Identifiers

PMID40218484

DOI10.3390/s25071971

PMCPMC11991327

What OpenQuestion holds

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LicenceCC BY

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.