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Review
. 2025 May 23:12:1578318.
doi: 10.3389/frobt.2025.1578318. eCollection 2025.

A review of robotic and automated systems in meat processing

Yining Lyu #  1   2 , Fan Wu #  1 , Qingyu Wang  1   2 , Guanyu Liu  1 , Yingqi Zhang  3 , Huanyu Jiang  1   2 , Mingchuan Zhou  1   2
Affiliations
Review

A review of robotic and automated systems in meat processing

Yining Lyu et al. Front Robot AI. .

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.

Keywords: automated equipment; meat processing robot; meat processing system; meat production automation; robot processing system.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Flowchart of the technology demonstrated in this review.
FIGURE 2
FIGURE 2
Livestock processing robot: (a) Feature identification with the profile moved (Liu et al., 2017); (b) FCN image segmentation model (Mu et al., 2020); (c) The axis representation for carcass in operational space (Singh et al., 2012); (d) Beef preparation process and Z-cut (Guire et al., 2010a); (E) Metamorphic hand integrated with ABB robot (Wei et al., 2014).
FIGURE 3
FIGURE 3
Poultry processing robot: (a) The acquisition system of the chicken evisceration (Chen et al., 2021b); (b) The poultry portion identification system: Image processing and neural network arbitration (Khashman, 2012); (c) The framework of chicken portion sorting machine simulated in CATIA software (Teimouri et al., 2018); (d) WLD Whole Leg Deboner M3.0 (Meyn, 1993). (e) RAPID plus breast deboner M4.3 (Meyn, 2023a).
FIGURE 4
FIGURE 4
Seafood processing systems: (a) Representation of background removal, fish identification, and visual evaluation of goodness of fit for live body weight and carcass weight for models that considered only the segmented fish body area (Fernandes et al., 2020); (b) RGB and 3D images of an example fillet and the sequence of computer vision operations used to generate images, features, and the ground truth used for training of the classification algorithms. RGB pixel values of the normal muscle are higher (lighter color) than the pixel values of the blood spots (dark color) (Misimi et al., 2017); (c) Integration of hyperspectral imaging technology with spectroscopy and computing for quality evaluation of seafood products (Ismail et al., 2023).
See this image and copyright information in PMC

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