Evidence map›Paper›PMID 42179520›Full record

ArticleFrontiers in plant science2026

NRLC-YOLO for lightweight detection and grasp positioning of latex cups in rubber plantations.

Ruiwu Xu, Xirui Zhang, Yulan Liao, Zhifu Zhang, Junxiao Liu

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PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in plant science, 2026. 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

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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

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.

Ruiwu XuSchool of Information and Communication Engineering, Hainan University, Haikou, Hainan, China.
Xirui ZhangSchool of Mechanical and Electrical Engineering, Hainan University, Haikou, Hainan, China.
Yulan LiaoSchool of Mechanical and Electrical Engineering, Hainan University, Haikou, Hainan, China.
Zhifu ZhangSchool of Mechanical and Electrical Engineering, Hainan University, Haikou, Hainan, China.
Junxiao LiuSchool of Mechanical and Electrical Engineering, Hainan University, Haikou, Hainan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Natural rubber harvesting remains highly dependent on manual labor, particularly during latex cup collection, which limits efficiency and increases operational costs. Intelligent robotic harvesting systems require accurate visual perception and reliable grasp point positioning under rubber plantation environments. However, latex cups are typically small, visually diverse, and often affected by adjacent latex drains, making detection and manipulation challenging for existing vision models. To address these challenges, this study proposed a lightweight vision-based framework for latex cup detection and grasp-point positioning in automated rubber harvesting. The proposed NRLC-YOLO is developed based on YOLO11n-seg by integrating a lightweight backbone, a local-global attention enhancement module, and a dynamic convolution strategy. In addition, a fast grasp-point positioning method is designed to determine the gripping center of latex cups, enabling stable robotic manipulation. As validated by experimental results, our model yields a mAP

Indexed as

Hevea brasiliensisintelligent agriculturelightweight deep learningobject detectionrobotic harvesting

Identifiers

PMID42179520
PMCPMC13194159

What OpenQuestion holds

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LicenceCC BY
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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.