Evidence map›Paper›PMID 40485769›Full record

ReviewFrontiers in robotics and AI2025

A review of robotic and automated systems in meat processing.

Yining Lyu, Fan Wu, Qingyu Wang, Guanyu Liu, Yingqi Zhang, Huanyu Jiang, Mingchuan Zhou

Abstract readReview
In one paragraph

Review in Frontiers in robotics and AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
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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.

Yining Lyu *College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
Fan Wu *College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
Qingyu WangCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
Guanyu LiuCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
Yingqi ZhangChair of Robotics, Artificial Intelligence and Real-time Systems, Technische Universität München, München, Germany.
Huanyu JiangCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
Mingchuan ZhouCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tasks in the meat processing sector are physically challenging, repetitive, and prone to worker scarcity. Therefore, the imperative adoption of mechanization and automation within the domain of meat processing is underscored by its key role in mitigating labor-intensive processes while concurrently enhancing productivity, safety, and operator wellbeing. This review paper gives an overview of the current research for robotic and automated systems in meat processing. The modules of a robotic system are introduced and afterward, the robotic tasks are divided into three sections with the features of processing targets including livestock, poultry, and seafood. Furthermore, we analyze the technical details of whole meat processing, including skinning, gutting, abdomen cutting, and half-carcass cutting, and discuss these systems in performance and industrial feasibility. The review also refers to some commercialized products for automation in the meat processing industry. Finally, we conclude the review and discuss potential challenges for further robotization and automation in meat processing.

Indexed as

automated equipmentmeat processing robotmeat processing systemmeat production automationrobot processing system

Identifiers

PMID40485769
PMCPMC12141337

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.