Artificial Intelligence in Meat Processing: A Comprehensive Review of Data-Driven Applications and Future Directions

Kyungchang Jeong; Gyuchan Jo; Jong Ho Lee; Brad Kim; Jungseok Choi; Hongseok Oh; Ji-Hoon Jeong; Euijong Lee; Kyungchang Jeong; Gyuchan Jo; Jong Ho Lee; Yuan H. Brad Kim; Jungseok Choi; Hongseok Oh; Ji-Hoon Jeong; Euijong Lee

doi:10.22175/mmb.20157

Traditional meat processing technologies and methods are predominantly manual and labor intensive, but they can be significantly optimized through automation and advanced data-driven systems. Artificial intelligence (AI) and internet of things technologies enable noninvasive, automated, and real-time solutions that enhance efficiency, safety, and consistency, while also reducing labor demands. These capabilities mark an inflection point in meat processing, with AI-driven solutions spanning every stage of the livestock sector, from meat production to quality assessment and market analysis. This review comprehensively explores existing research throughout the meat processing cycle, with a specific focus on data- driven AI applications that perform classification, regression, and image analysis tasks. The analysis emphasizes the types of data collected, the preprocessing strategies employed, and the AI models adopted. It also identifies key challenges, emerging trends, and potential pathways for future development, specifically highlighting opportunities to improve efficiency, safety, and sustainability. The insights presented herein offer valuable guidance for researchers and industry professionals seeking to advance meat processing technologies through AI-driven innovation.

Keywords: artificial intelligence, machine learning, meat processing, meat production, meat quality analysis, market analysis

How to Cite: Jeong, K. , Jo, G. , Lee, J. , Kim, B. , Choi, J. , Oh, H. , Jeong, J. & Lee, E. (2025) “Artificial Intelligence in Meat Processing: A Comprehensive Review of Data-Driven Applications and Future Directions”, Meat and Muscle Biology. 9(1). doi: https://doi.org/10.22175/mmb.20157

Introduction

Fresh meat and processed meat products are fundamental to the human diet in that beef, pork, lamb, and other raw meats are major sources of high-quality protein, vitamins, and minerals (Baltic and Boskovic, 2015). Meat processing typically involves slaughtering, cutting, packaging, and storage, with the primary goal of extending shelf life while providing high-quality products. Each stage of the process can significantly impact both microbial safety and key meat quality attributes (Wang and Li, 2024). In particular, the heavy reliance on manual techniques, such as slaughtering, deboning, carcass fabrication and cutting during slaughter, and postharvest processing can lead to product damage or loss, inconsistencies in quality, increased waste, and a higher risk of cross-contamination (Daniel et al., 2020). Addressing these limitations has become a focal point of recent research, particularly through the introduction of noninvasive measurement equipment (e.g., computer vision systems) and automated evaluation methods that improve efficiency in terms of both time and labor (Shi et al., 2021).

Concurrently, researchers are increasingly integrating artificial intelligence (AI) with internet of things (IoT) technologies to reduce human labor and facilitate robust, data-driven decision-making (Lee et al., 2021, 2022c). Broadly, AI is defined as a technology enabling machines to simulate human-like intelligence and behavior, encompassing capabilities such as learning, problem-solving, planning, and creativity (Russell and Norvig, 1995). Recent technological advances have evolved from these foundational concepts to encompass a diverse range of applications. Generative AI represents a particularly transformative development, illustrated by instruction-tuned models such as ChatGPT, which have achieved widespread adoption across multiple sectors for generating sophisticated content including text, code, and visual media (Ouyang et al., 2022; Dwivedi et al., 2023). These diverse AI technologies are being successfully integrated across multiple domains with analytical applications demonstrating considerable utility in agriculture (Eli-Chukwu, 2019; Jeong et al., 2024c) and proving especially prominent in livestock farming operations (Tian et al., 2019; Jeong et al., 2024a). Within the livestock sector specifically, research on production prediction and quality assessment has advanced through computer vision and automated evaluation methods. These advancements have transformed traditional meat processing and evaluation workflows, significantly improving both accuracy and efficiency (Wang and Li, 2024; Alvarez-García et al., 2024).

As meat processing facilities increasingly adopt these AI systems, comprehensive reviews of noninvasive sensing technologies have outlined specific directions for further enhancing meat quality analysis and production efficiency throughout the processing pipeline (Shi et al., 2021; Wu et al., 2022a). The effectiveness of AI applications in meat processing is significantly influenced by data diversity and preprocessing techniques (García et al., 2016). For image data, preprocessing steps such as normalization, augmentation, and segmentation are crucial for highlighting relevant features while minimizing background noise (Shorten and Khoshgoftaar, 2019). Similarly, for tabular data, effective preprocessing through outlier removal, feature scaling, and dimensionality reduction substantially enhances model performance (Borisov et al., 2022). These data preparation strategies not only reduce noise and biases but also enable AI systems to identify meaningful patterns that might otherwise remain undetected, ultimately leading to more accurate predictions and assessments in meat processing applications (Bow, 2002).

In response to these emerging insights, this review provides a comprehensive synthesis of the current state of AI integration within the meat processing industry. The timeliness of this endeavor is underscored by the recent and significant surge in research activity within this domain, as illustrated in Figure 1. Although physical robotics constitutes another rapidly evolving field, particularly through IoT-enabled automation systems deployed for sophisticated operations such as carcass deboning in meat processing facilities (Lyu et al., 2025; Kim et al., 2023a), the nascent nature of AI methodologies in physical automation systems constrains the scope of examination to the seminal work conducted by Manko et al. (2022). Consequently, this review focuses on data–driven AI applications in meat processing that encompass classification, regression, and image analysis, which currently represent the most mature and comprehensively documented aspects of the field. The literature is systematically synthesized by identifying, for each research objective, the data–acquisition equipment and data types used, the preprocessing workflows applied, the AI modeling strategies adopted, and the resulting performance metrics. By systematically identifying prevailing challenges, explicating emergent research patterns, and delineating prospective research avenues, this comprehensive analysis delivers essential insights for researchers and industry stakeholders committed to advancing operational efficiency, product safety, and environmental sustainability throughout the meat production value chain. The main contributions of this review are summarized as follows:

•
A comprehensive review and analysis of research on the integration of AI into various stages of meat processing, including meat production, meat quality, and market analysis.
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A detailed examination of preprocessing techniques and AI methods in meat processing, complemented by visualizations of representative studies for each category.
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An identification of comprehensive challenges in current research, accompanied by a discussion of key factors for addressing these issues and suggestions for future research directions.

Figure 1.

Annual publication trend for artificial intelligence applications in the meat processing industry. The analysis is based on a keyword search for (“Meat” OR “Meat processing”) AND “AI” across the Web of Science, IEEE, and ACM databases from 2016 to 2024. ACM, Association for Computing Machinery; AI, artificial intelligence; IEEE, Institute of Electrical and Electronics Engineers.

Overview of Artifical Intelligence Technologies

This section provides a comprehensive overview of machine learning (ML) methodologies as a core component of the broader field of AI. In addition, the review defines the abbreviations and evaluation metrics employed throughout the study. AI represents a multidisciplinary field focused on developing computational systems that emulate human cognitive capabilities, particularly in areas of perception, decision-making, and complex problem-solving.

Machine learning

ML, a cornerstone of modern AI, enables computers to extract patterns directly from data rather than following explicitly programmed rules. This data-driven approach fundamentally differs from traditional programming paradigms where humans manually encode solution logic (Jordan and Mitchell, 2015). The primary objective of ML systems is to achieve robust generalization, the ability to make accurate predictions on previously unseen data through iterative refinement of internal model parameters during training. This automated approach to decision-making helps reduce human biases while promoting more objective analytical outcomes (Mitchell, 1997).

As illustrated in Figure 2, ML comprises 3 principal categories: supervised learning (Kotsiantis et al., 2007), unsupervised learning (Ghahramani, 2003), and reinforcement learning (Kaelbling et al., 1996). Supervised learning utilizes labeled training data that establish direct mappings between input features and desired outputs. Common applications include regression for predicting continuous values (such as market prices or consumer preferences) and classification for categorical assignments (such as product quality grades or defect detection). Unsupervised learning, conversely, operates without labeled data to uncover inherent data structures through techniques like clustering, dimensionality reduction, and anomaly detection. Reinforcement learning employs a distinct approach where an agent learns optimal behavior through environmental interaction, receiving rewards for beneficial actions and penalties for suboptimal choices.

Figure 2.

Overview of machine learning methodologies illustrating the 3 fundamental paradigms: supervised learning, unsupervised learning, reinforcement learning. DBSCAN, density-based spatial clustering of applications with noise; t-SNE, T-distributed stochastic neighbor embedding.

Although these 3 categories share foundational principles, they differ in how models are structured and tuned. Traditional ML methods, such as gradient boosting (GB) and random forest (RF), rely on hyperparameters like maximum tree depth and the number of nodes, which must be manually adjusted to optimize performance (Ren et al., 2016). Deep learning, on the other hand, employs multilayered neural networks that automatically learn hierarchical representations from large datasets. Rather than depending on fixed parameters set by human experts, deep learning emphasizes the design of layer architectures and node structures that capture increasingly abstract features as data propagate through successive layers. This architecture often yields exceptional performance in tasks such as image recognition and natural language processing (Lathuilière et al., 2019).

Figure 3 presents a comprehensive workflow of typical preprocessing steps and tasks performed on tabular and image data (Zelaya, 2019). Tabular data, which are typically organized into rows and columns, often contain missing values that must be imputed. Each feature may also require normalization to ensure consistent scaling. In addition, dimensionality reduction methods such as principal component analysis (PCA) can be applied to extract the most relevant features for subsequent classification or regression tasks (Karamizadeh et al., 2013). In contrast, image data typically need to be resized to meet the input requirements of convolutional neural networks (Li et al., 2021b), for instance 640 × 640 pixels. Depending on the analytical objective, images may be cropped or further processed to isolate regions of interest (RoI) or to perform object segmentation. When the dataset is limited, data augmentation techniques such as rotation, flipping, and adding noise can help increase variability. The preprocessed images can then be used for tasks that include object classification, detection, and segmentation, thereby leveraging the spatial properties inherent in visual data.

Figure 3.

Comprehensive and conceptual workflow of artifical intelligence methodologies in meat processing applications showing preprocessing techniques and common tasks for both tabular and image data. CNN, convolutional neural network; PCA, principal component analysis; RGB, red-green-blue; SVM, support vector machine.

Abbreviations and evaluation metrics

To facilitate understanding of the research literature analysis in this review, overlapping abbreviations and definitions are summarized in Table 1. This paper provides abbreviations and definitions for the general terms used throughout as well as for the equipment, data, preprocessing methods, models, and metrics discussed in subsequent section.

Table 1.

Abbreviations and definitions in meat processing research.

Category	Abbreviation	Definition
General terms	AI	Artificial intelligence
	ML	Machine learning
	DL	Deep learning
	IoT	Internet of things
	CV	Computer vision
Equipment	NIR	Near-infrared
	E-nose	Electronic nose
	E-tongue	Electronic tongue
	Vis-NIR	Visible and near-infrared
	HSI	Hyperspectral imaging
	MSI	Multispectral imaging
	GC-MS	Gas chromatography with mass spectrometry
	REIMS	Rapid evaporative ionization mass spectroscopy
	FTIR	Fourier-transform infrared
Data target	IMF	Intramuscular fat
	SFT	Subcutaneous fat thickness
	TBARS	Thiobarbituric acid reactive substances
	LC-SFA	Long-chain saturated fatty acid
	PCA	Principal component analysis
Preprocessing	SLIC	Simple linear iterative clustering
	RoI	Regions of interest
	MSF	Mean shift filtering
	FD	First-order derivative
	SD	Second-order derivative
	COW	Correlated optimized warping
	PLS	Partial least-squares
	SNV	Standard normal variate
	SG	Savitzky-Golay smoothing
	MSC	Multiplicative scatter correction
	GLCM	Gray-level co-occurrence matrix
	OSC	Orthogonal signal correction
	GI-AAE	Generative inference adversarial autoencoder
	CFS	Correlation-based feature selection
	FIR	Finite impulse response
	UVE	Uninformative variable elimination
	ACO	Ant colony optimization
	IWO	Improved whale optimization
	DFA	Discriminant function analysis
	FWT	Fast wavelet transform
	RBF	Radial basis function
	CNN	Convolutional neural network
Model	YOLO	You only look once
	VGG	Visual geometry group
	GB	Gradient boosting
	DT	Decision trees
	KNN	K-nearest neighbors
	RF	Random forest
	SR	Stepwise regression
	PLSR	Partial least-squares regression
	DNN	Deep neural network
	SVM	Support vector machine
	LS-SVM	Least-squares support vector machine
	LSM	Least-squares mean
	LDA	Linear discriminant analysis
	MLR	Multinomial logistic regression
	DBN	Deep belief network
	AFINN	Adaptive fuzzy inference neural network
	AFLS	Adaptive fuzzy logic system
	DCRNet	Detect cells rapidly network
	RNN	Recurrent neural network
	BERT	Bidirectional encoder representations from transformer
	ARIMA	Autoregressive integrated moving average
	LSTM	Long short-term memory
	MAE	Mean absolute error
Metrics	RMSE	Root mean squared error
	MAPE	Mean average percentage error
	IoU	Intersection over union
	AP	Average precision
	R²	Coefficient of determination

In addition, Table 2 explains the evaluation metrics for both regression and classification tasks. For regression problems, establishing reliable “ground truth” values represents a fundamental methodological challenge. Reference standards may be derived from direct empirical measurements or defined through acceptable tolerance ranges within predetermined categories, depending on specific research objectives and experimental constraints. To mitigate these reference standard limitations and enhance the reliability of AI-based investigations, standardized quantitative evaluation metrics are systematically employed. These include mean absolute error (MAE), root mean squared error (RMSE), and mean average percentage error (MAPE), which quantify the discrepancy between predicted and reference values. Lower values for these metrics indicate greater proximity to the established ground truth (Naidu et al., 2023). In contrast, higher values of R² and the correlation coefficient indicate better model performance and stronger agreement with reference standards. For classification tasks, higher evaluation metric values correspond to greater accuracy in predictions, but similar challenges exist in establishing reliable categorical boundaries and validation methodologies.

Table 2.

Metrics for evaluating performance in meat processing research.

Category	Metric	Equation	Brief Explanation
Regression	MAE	$1 n ∑ i = 1 n \| y i − y^i \|$	Average of absolute differences between predictions and true values.
	RMSE	$1 n ∑ i = 1 n (y i − y^i) 2$	Square root of the average of squared differences.
	MAPE	$100 % n ∑ i = 1 n \| y i − y^i y i \|$	Average of absolute percentage errors.
	R²	$1 − ∑ i = 1 n (y i − y^i) 2 ∑ i = 1 n (y i − y ¯) 2$	Proportion of variance explained by the model.
	Corr	$∑ i = 1 n (y i − y^) (y^i − y^¯ i) ∑ i = 1 n (y i − y ¯) 2 ∑ i = 1 n (y^i − y^¯ i) 2$	Measures the linear relationship between predictions and actual values.
Classification	Accuracy	$T P + T N T P + T N + F P + F N$	Ratio of correct predictions to total predictions.
	F₁-score	$2 · Precision · Recall Precision + Recall$	Harmonic mean of precision and recall.
	Precision	$T P T P + F P$	Ratio of true positives to all predicted positives.
	Recall	$T P T P + F N$	Ratio of true positives to all actual positives.
	IoU	$Area of Overlap Area of Union$	Measures the overlap between predicted and ground truth regions.
	mAP	$1 C ∑ c = 1 C A P (c)$	Mean of average precision over all classes.
	AP	$∑ n = 1 N (r n − r n − 1) p n$	Area under the precision-recall curve, computed as the weighted sum of precisions at different recall levels.

AP, average precision; C, number of classes; Corr, Pearson correlation coefficient; FN, false negatives; FP, false positives; IoU, intersection over union; MAE, mean absolute error; mAP, mean average precision; MAPE, mean average percentage error; n, total number of samples; p_n and r_n, precision and recall at the nth threshold, respectively; RMSE, root mean squared error; TN, true negatives; TP, true positives; ( $y ¯$ ), mean of actual values; $y i$ , actual value; $y^i$ , predicted value.

Application of Artificial Intelligence Technology in Meat Processing

This section provides a systematic review of data-driven AI applications in meat processing, organized into 3 primary domains: (1) meat production analysis, (2) meat quality analysis, and (3) market analysis and consumer preferences. To enhance narrative cohesion and facilitate insightful comparisons, the analysis within each domain is thematic, structured around data types and target variables rather than purely chronological progression. While detailed specifications of each study (including data types, methodologies, and performance metrics) are summarized in their respective tables, the main text of this review prioritizes the practical applications and outcomes of AI rather than a technical exegesis of the underlying models.

Meat production analysis

Meat production analysis research categorizes yield evaluation into 2 main areas: overall carcass yield assessment, which involves entire carcass segmentation along with rib-eye regions and bones, and specific cut yield analysis, which focuses on predicting intramuscular fat (IMF) content and back-fat thickness. Table 3 summarizes studies that have applied AI techniques to meat production.

Table 3.

Summary of literature on meat production, categorized by purpose (overall carcass yield, specific cut yield), focusing on collected data and prediction methods.

Category	Year	Animal	Data					Method
Category	Year	Animal	Equipment	Type	Features	Target	NR	Preprocessing	Model	Performance
Overall carcass	Gonçalves et al. (2021)	Beef	RGB camera (smartphone)	Image	3 264 × 2 448	Cla: 2 class (seg: carcasses)	226	SLIC	VGG-16	Acc: 96.0%; IoU: 92.2%
	Matthews et al. (2022)	Beef	Structured data, RGB camera (smartphone)	Tabular image	9 features	Reg: carcass yields	123 844	-	GB, CNN	RMSE: 2.92; RMSE: 2.82
	Lee et al. (2022b)	Beef	RGB camera (smartphone)	Image	224 × 224	Reg: marbling score	10 246	Segment (RoI), resize	MSENet	MAE: 0.55; Corr: 0.95
	Zhang et al. (2023)	Pork	RGB camera (smartphone)	Image	2 448 × 3 264	Cla: 3 class (seg: marbling)	173	Resize, MSF	Marbling-Net	IoU: 76.8%; F₁-score: 86.9%
	de Melo et al. (2022)	Beef	Ultrasound scanning	Image	309 × 213	Cla: 2 class (seg: rib-eye)	67	Resize	U-Net++	Acc: 97.3%
	Xu et al. (2023)	Pork	X-ray	Image	-	Cla: 2 class (seg: born)	27 837	Resize	Encoder- decoder	MAE: 0.12
	Manko et al. (2022)	Pork	RGB camera (smartphone)	Image	1 280 × 720	Cla: 2 class (key point detection)	25	Crop, resize, augment	U-Net	mAP: 0.98
Specific cut	Kvam and Kongsro (2017)	Pork	Ultrasound scanning	Image	-	Reg: IMF	3 037	Crop, normalize augment	CNN	RMSE: 1.8; R²: 0.74
	Liu et al. (2018)	Pork	RGB camera (smartphone)	Image	-	Cla: 3 class	85	Segment (RoI), 18 color features	SVM	Acc: 75.0%
	Shahinfar et al. (2019)	Lamb	Ultrasound scanning	Image	-	Reg: IMF	3 500	-	RF	MAE: 0.74
	Kucha et al. (2022)	Pork	NIR	Tabular	900-1 700 nm	Reg: IMF	144	SD, COW	SVM	RMSE: 0.37; R²: 0.89
	Chen et al. (2022b)	Pork	RGB camera (smartphone)	Image	-	Reg: IMF	1 481	Segment (RoI)	GB	Corr: 0.81
	Masferrer et al. (2018)	Pork	AutoFomIII	Tabular	11 features	Cla: 4 class	4 000	-	SVM	Acc: 73.0%
	Masferrer et al. (2019)	Pork	AutoFomIII	Tabular	11 features	Cla: 4 class	400	-	SVM	Acc: 75.3%
	Lee et al. (2022a)	Pork	RGB camera (smartphone)	Image	-	Reg: back-fat thickness	3 782	Crop	BTENet	MAPE: 6.4

Acc, accuracy; BTENet, back-fat thickness estimation network; Cla, classification; CNN, convolutional neural network; Corr, Pearson correlation coefficient; COW, correlated optimized warping; GB, gradient boosting; IMF, intramuscular fat; IoU, intersection over union; MAE, mean absolute error; mAP, mean average precision; MAPE, mean average percentage error; MSENet, mean and standard deviation-based ensemble network; MSF, mean shift filtering; NIR, near-infrared; NR, number of recordings; Reg, regression; RF, random forest; RGB, red-green-blue; RMSE, root mean squared error; RoI, regions of interest; Seg, segment; SLIC, simple linear iterative clustering; SVM, support vector machine.

Overall carcass analysis

This approach assesses yield, marbling distribution, and the segmentation of rib–eye and bone. Gonçalves et al. (2021) employed a convolutional neural network (CNN)-based segmentation approach to identify carcass regions in smartphone-captured images, by first partitioning each image into superpixels and then classifying each segment as either carcass or background with 96.0% accuracy.

In the context of regression-based prediction, AI models have proven highly effective at estimating key carcass traits directly from image data. For example, Matthews et al. (2022) demonstrated the effectiveness of a 2-dimensional CNN model for accurately estimating the weight of specific cuts from a video analysis system. In a similar application for quality assessment, Lee et al. (2022b) developed a specialized CNN, mean and standard deviation-based ensemble network (or MSENet), to successfully predict marbling scores from red-green-blue (RGB) images taken at the intersection of the 12th and 13th ribs.

Research focusing on segmenting specific RoI within entire carcass structures has led to the development of specialized architectures such as Marbling-Net, which have demonstrated superior performance compared to standard baseline models like U-Net in precisely segmenting marbling regions (Zhang et al., 2023). Complementing these quality-focused applications, the versatility of segmentation techniques has been effectively demonstrated in anatomical analysis across multiple imaging platforms. For instance, CNN-based architectures have achieved successful detection of loin eye areas in ultrasound imaging (de Melo et al., 2022), while efficient encoder-decoder algorithms have been developed for real-time bone segmentation from X-ray images (Xu et al., 2023).

Beyond segmentation tasks, keypoint detection has emerged as another critical computer vision technique for meat processing applications, particularly from the perspective of meat automation systems. Manko et al. (2022) advanced robotic manipulation capabilities in meat processing by developing keypoint detection algorithms to identify critical anatomical landmarks on pig carcasses, enabling accurate prediction of limb orientation and identification of optimal gripping points for automated handling. Their methodology involved collecting RGB images from 6 different viewpoints to ensure comprehensive spatial coverage and robust keypoint localization across varying perspectives.

Specific cut analysis

Specific cut analysis frequently focuses on IMF content and back-fat thickness prediction. Kvam and Kongsro (2017) employed ultrasound imaging to successfully predict IMF in pigs using a custom CNN model trained on preprocessed images that were normalized to unit standard deviation and augmented through flipping to enhance model robustness. Similarly, Liu et al. (2018) developed an approach to predict IMF categories in pork using a support vector machine (SVM) model applied to RGB images. To segment the RoI from the images, they employed a gray-level histogram combined with the Otsu method for background removal and binarization. Eighteen color features were subsequently extracted and applied to the SVM model, achieving a classification accuracy of 75.0% for IMF levels (grades 1–3), demonstrating the feasibility of this approach for practical implementation.

For IMF prediction, several studies have employed diverse data sources and AI methodologies with notable success. Shahinfar et al. (2019) utilized a comprehensive dataset including animal weights recorded from birth through slaughter (130–700 d) and 3 500 ultrasound images collected concurrently, with the RF model achieving superior predictive performance among the evaluated AI approaches. Kucha et al. (2022) employed near-infrared (NIR) spectra from vacuum-packed pork samples, preprocessing the spectral data using SD transformation and the correlated optimized warping (or COW) method to eliminate polyethylene film interference, with the SVM model subsequently demonstrating robust performance in IMF content estimation. Chen et al. (2022b) integrated computer vision scores derived from RGB images with conventional pork quality traits such as meat color, marbling score, pH value, and drip loss. The datasets underwent background removal, grayscale conversion, and RoI segmentation to derive the computer vision score, with the GB model achieving a Pearson correlation coefficient of 0.81, highlighting the potential of combining computer vision features with conventional quality metrics.

For back-fat thickness classification and prediction, several studies have developed AI-driven approaches with varying methodologies. Masferrer et al. (2018) systematically varied both the number and type of predictor variables to improve SVM model performance for categorizing ham into 4 thickness groups: thin (0–10 mm), standard (11–15 mm), semi-fat (16–20 mm), and fat (>20 mm), with the SVM model successfully classifying ham categories using variables including lean meat percentage, sex, and breed. Masferrer et al. (2019) employed the AutoFomIII system to collect characteristic data from 400 pig carcasses and successfully classified them into 4 categories (HC1: <9 mm, HC2: 9–12 mm, HC3: 13–19 mm, and HC4: >19 mm) based on subcutaneous fat thickness using an SVM model. Lee et al. (2022a) proposed a CNN-based architecture called back-fat thickness estimation network (or BTENet) for predicting back-fat thickness in slaughtered pigs. The model successfully performed both segmentation and thickness estimation simultaneously by cropping the back-fat region from original images.

Figure 4 summarizes the research trends in meat production studies. Overall carcass analysis has primarily focused on predicting carcass yield and segmenting loin eye areas and bone structures, while specific cut analysis has concentrated on IMF content and back-fat thickness prediction through regression and classification tasks. Most studies employ image-based approaches, utilizing segmented RoI from original images followed by various preprocessing techniques such as resizing and simple linear iterative clustering (SLIC) before applying CNN-based models for enhanced accuracy in specific anatomical characteristic analysis. Nevertheless, such approaches utilizing superpixel-based partitioning in carcass detection studies may result in computational limitations and reduced accuracy. Therefore, segmentation-based methodologies should be prioritized as they demonstrate significant advantages in both processing efficiency and classification precision.

Figure 4.

Overview of the artificial-intelligence-driven meat production analysis framework: a red-green-blue camera-based workflow for evaluating carcass characteristics and specific cut parameters using advanced image segmentation techniques. IMF, intramuscular fat; RGB, red-green-blue; SLIC, simple linear iterative clustering.

Meat quality analysis

The meat quality analysis section examines research in 3 primary areas: meat quality assessment, freshness evaluation, and meat authentication. Meat quality assessment aims to predict intrinsic meat quality characteristics, such as pH, water activity, meat color, IMF, flavor, lipid/protein oxidation, and texture profile analysis, to evaluate whether the meat is commercially acceptable and to assess its performance under refrigerated or frozen storage conditions (Table 4). Freshness evaluation focuses on determining the current state of meat, often through sensory analysis and real-time monitoring of spoilage indicators (Table 5). Lastly, in the domain of meat authentication, the primary objective is to quantify the degree of adulteration among various meat types and to classify cuts based on distinctive muscle characteristics (Table 6). Detailed discussions of these topics are provided in the subsequent subsections.

Table 4.

Summary of literature on meat quality assessment, categorized by purpose (attribute analysis, meat quality, storage condition), focusing on collected data and prediction methods.

Category	Year	Animal	Data					Method
Category	Year	Animal	Equipment	Type	Features	Target	NR	Preprocessing	Model	Performance
Attribute analysis	Ndob and Lebert (2018)	Pork	pH meter, a_w meter	Tabular	21 features	Reg: pH, Water activity	143	Normalize	DNN	MAE: 0.2; MAE: 0.01
	Dixit et al. (2021)	Beef, lamb, venison	NIR	Image, tabular	900–1 700 nm, 235 spectrals	Reg: pH, IMF	2 196	Segment (RoI), SNV	CNN	R²: 0.89; R²: 0.89
	Wang et al. (2021)	Lamb	NIR	Tabular	900–1 700 nm, 256 spectrals	Reg: stearic acid	151	MSC, SNV, OSC, PCA	LS-SVM	RMSE: 0.18; R²: 0.76
	El Karam et al. (2023)	Pork	UV spectrometry	Tabular	288–560 nm	Cla: 5 class	72	Normalize	SVM	Acc: 97.6%
	Cheng et al. (2023b)	Pork	Vis-NIR	Image, tabular	400–1 002 nm, 240 spectrals	Reg: TBARS, carbonyl	240	SNV, SG, PLSR	Multitask CNN	R²: 0.97; R²: 0.96
	Cheng et al. (2023a)	Pork	Vis-NIR	Image, tabular	400–1 000 nm, 411 spectrals	Reg: TBARS	240	SNV, SG	3D-CNN	RMSE: 0.03; R²: 0.92
	Cui et al. (2024)	Red meat (lamb, pork, beef)	Vis-NIR	Image, tabular	400–1 000 nm, 128 spectrals	Reg: LC-SFA	-	Segment (RoI), GI-AAE	CNN	RMSE: 0.55; R²: 0.72
	Sun et al. (2018)	Pork	RGB camera (smartphone)	Image	-	Cla: 6 class	1 400	Segment (RoI), 18 color features	SVM	Acc: 92.5%
	Tang et al. (2023)	Pork	Vis-NIR	Image, tabular	400–1 000 nm, 204 spectrals	Reg: fat, lean, etc.	1 091	Segment (RoI)	DNN	R²: 0.72; R²: 0.73
	Wang et al. (2019)	Beef	E-tongue	Tabular	12 features	Cla: 5 class	60	PCA	SVM	Acc: 90.0%
	Cui et al. (2022)	Beef	GC × GC − MS	Tabular	-	Cla: 10 class	8	-	DNN	Acc: 90.0%
	Zhang et al. (2022b)	Lamb	NIR	Tabular	900–1 700 nm, 256 spectrals	Reg: hardness, gumminess, chewiness	84	OSC, mean centering	SVM	R²: 0.98; R²: 0.98; R²: 0.98
Meat quality	Wold et al. (2017)	Chicken	NIR	Tabular	760–1 040 nm, 15 spectrals	Cla: 2 class	197	PLSR	LDA	Acc: 99.5%
	Geronimo et al. (2019)	Chicken	NIR, RGB camera (smartphone)	Tabular, image	1 150 –2 150 nm, 1 600 × 1 200	Cla: 2 class	80	Segment (RoI), CLAHE	SVM	Acc: 97.5%; Acc: 91.8%
	Ahn et al. (2020)	Red meat	X-ray	Image	-	Cla: 2 class	29 149	CLAHE, hough, resize, augment	CNN	Acc: 99.8%
	Barbon et al. (2018)	Chicken	NIR	Tabular	1 050 spectrals	Cla: 4 class	158	CFS	REPTree	F₁-score: 73.5%
	Biglia et al. (2022)	Pork	Fat-O-meater	Tabular	11 features	Cla: 2 class	134	One-hot encoding	DNN	Acc: 86.8%
	Penning et al. (2020)	Beef	REIMS	Tabular	-	Cla: 5 class	1 800	PCA, feature selection	SVM	Acc: 99.0%
	Alaiz-Rodríguez and Parnell (2020)	Lamb	FTIR	Tabular	1 687 spectrals	Cla: 2	134	MSC, PCA	DNN	Acc: 93.8%
	García-Infante et al. (2024)	Lamb	Colorimeter thermocouple SPME + GC − MS	Tabular	144 features	Cla: 3 class	78	Data cleaning	DNN	Acc: 88.0%
Storage condition	Huang et al. (2016a)	Pork	NIR	Tabular	760–2 500 nm, 870 spectrals	Cla: 4 class	180	PCA	DNN	Acc: 93.3%
	Xu and Sun (2017)	Fish	HSI	Image, tabula	-	Cla: 2 class	400	SNV, normalize, segment (RoI), PCA	TreeBagger	Acc: 97.8%
	Górska-Horczyczak et al. (2017)	Pork	E-nose	Tabular	90 features	Cla: 7 class	1 008	Normalize	DNN	Acc: 85.4%
	Abie et al. (2021)	Pork	Electrical impedance	Tabular	10–500 kHz, 36 features	Cla: 2 class	180	-	LSTM	Acc: 95.0%
	Swanson and Gowen (2022)	Poultry	Vis-NIR	Image, tabula	443–726 nm, 204 spectrals	Cla: 2 class	10	SNV, SG	SVM	Acc: 88.0%
	Park et al. (2023)	Beef	Vis-NIR	Image, tabula	400–1 000 nm, 300 spectrals	Cla: 3 class	4,950	MSC, SNV, SG, normalize	SVM	Acc: 97.6%

3D-CNN, 3-dimensional convolutional neural network; Acc, accuracy; aw, water activity; CFS, correlation-based feature selection; Cla, classification; CLAHE, contrast-limited adaptive histogram equalization; CNN, convolutional neural network; Corr, Pearson correlation coefficient; DNN, deep neural network; E-nose, electronic nose; E-tongue; electronic tongue; FTIR, Fourier-transform infrared; GC, gas chromatography; GI-AAE, generative interference adversarial autoencoder; HSI, hyperspectral imaging; IMF, intramuscular fat; LC-SFA, long-chain saturated fatty acid; LDA, linear discriminant analysis; LS-SVM, least-squares support vector machine; LSTM, long short-term memory; MAE, mean absolute error; MS, mass spectrometry; MSC, multiplicative scatter correction; NIR, near-infrared; NR, number of recordings; OSC, orthogonal signal correction; PCA, principal component analysis; PLSR, partial least-squares regression; Reg, regression; REIMS, rapid evaporative ionization mass spectroscopy; RGB, red-green-blue; RMSE, root mean squared error; RoI, regions of interest; SG, Savitzky-Golay smoothing; SNV, standard normal variate; SPME, solid phase microextraction; SVM, support vector machine; TBARS, thiobarbituric acid reactive substances; UV, ultraviolet; Vis-NIR, visible and near-infrared.

Table 5.

Summary of literature on meat freshness evaluation, categorized by purpose (freshness status analysis, microbe analysis), focusing on collected data and prediction methods.

Category	Year	Animal	Data					Method
Category	Year	Animal	Equipment	Type	Features	Target	NR	Preprocessing	Model	Performance
Freshness status analysis	Hasan et al. (2012)	Beef, fish	E-nose	Tabular	8 features	Cla: 4 class	1 372	FIR	KNN	Acc: 96.2%
	Haddi et al. (2015)	Red meat (beef, goat, sheep)	E-nose, E-tongue	Tabular	6 features, 21 features	Cla: 5 class	75	PCA	SVM	Acc: 81.3%; Acc: 100%
	Vajdi et al. (2019)	Fish	E-nose	Tabular	35 features	Cla: 3 class	64	PCA	DNN	Acc: 96.8%
	Huang et al. (2016b)	Fish	NIR	Tabular	10 000 –4 000 cm⁻¹, 1 557 spectrals	Cla: 4 class	180	PCA, MSC	DNN	Acc: 93.3%
	Yang et al. (2017)	Beef	HSI	Image, tabular	400–1 000 nm, 774 spectrals	Cla: 3 class	105	Random frog	LS-SVM	Acc: 97.1%
	Ropodi et al. (2017)	Beef, horse	MSI	Image, tabular	405–970 nm, 18 spectrals	Cla: 4 class	350	Segment (RoI), PCA	SVM	Acc: 95.3%
	Alshejari and Kodogiannis (2017)	Beef	Vis-NIR	Image, tabular	405–970 nm, 18 spectrals	Cla: 8 class	112	PCA	AFINN	Acc: 92.8%
	Zhang et al. (2022a)	Lamb	Vis-NIR	Image, tabular	400–1 000 nm, 125 spectrals	Cla: 3 class	300	PCA	RF	Acc: 91.0%
	Huang et al. (2023)	Beef	MOF@SnS₂	Tabular	-	Cla: 2 class	1 532	-	DNN	Acc: 78.5%
	Arsalane et al. (2018)	Beef	RGB camera (smartphone)	Image	2 592 × 1 944	Cla: 3 class	81	Segment (RoI), color space (HSV), PCA	SVM	Acc: 100%
	Guo et al. (2020)	Chicken, fish, beef	RGB camera (smartphone)	Image	-	Cla: 3 class	4 161	White balance	ResNet-101	Acc: 98.5%
	Amani and Sarkodie (2022)	Red meat	RGB camera (smartphone)	Image	1 280 × 720	Cla: 2 class	1 896	-	CNN	Acc: 100%
	Hewawasam et al. (2023)	Chicken	RGB camera (smartphone)	Image	-	Cla: 2 class	2 487	Resize, augment	MobileNet	Acc: 100%
	Kim et al. (2023b)	Pork	RGB camera (smartphone), air sensor	Image	-	Cla: 3 class	18	Normalize, augment	1D-CNN	Acc: 99.4%
	Kim et al. (2023c)	Pork	RGB camera (smartphone)	Image	-	Cla: 3 class	-	Color space (HSV)	1D-CNN	Acc: 98.7%
	Elangovan et al. (2024)	Red meat (lamb, pork, beef)	RGB camera (smartphone)	Image	1 280 × 720	Cla: 3 class	1 896, 2 266	Resize, augment	ConvNet	Acc: 99.4%; Acc: 96.6%
	Arsalane et al. (2024)	Beef	RGB camera (smartphone)	Image	2 592 × 1 944	Cla: 2 class	81	Resize, crop, FWT	KNN	Acc: 92.5%
	Zheng et al. (2024)	Pork	Microscope	Image	3 840 × 2 880	Cla: 2 class (object detection)	54 445	Crop, augment	DCRNet	AP: 81.2%
Microbe analysis	Papadopoulou et al. (2013)	Beef	E-nose	Tabular	8 features	Cla: 3 class, reg: TVC	177	PCA, DFA	SVM	Acc: 90.4%; Corr: 0.86
	Kodogiannis et al. (2014)	Beef	FTIR	Tabular	1 800–1 000 cm⁻¹	Cla: 3 class, reg: TVC	74	SG, PCA	AFLS	Acc: 95.9%; RMSE: 0.37
	Gu et al. (2017)	Pork	E-nose	Tabular	10 features	Cla: 3 class, reg: acid value	25	-	DNN, SVM	Acc: 100%; R²: 0.98
	Kaswati et al. (2020)	Chicken	Vis-NIR	Image, tabular	400–1 000 nm	Cla: 2 class, reg: pH	91	Segment (RoI)	RF, PLSR	Acc: 85.5%; R²: 0.84
	Huang et al. (2014)	Pork	E-nose, RGB camera (smartphone), NIR	Image, tabular	11 features, 640 × 480, 10 000 –4 000 cm⁻¹	Reg: TVB-N	90	SNV, segment (RoI), PCA	DNN	RMSE: 2.73; R²: 0.95
	Huang et al. (2015)	Pork	NIR	Tabular	3 spectrals	Reg: TVB-N	77	GLCM, PCA	BP-AdaBoost	RMSE: 6.94; Corr: 0.83
	Khulal et al. (2016)	Chicken	HSI	Image, tabular	1 628 × 618, 618 spectrals	Reg: TVB-N	75	SNV, PCA, ACO	DNN	RMSE: 6.38; Corr: 0.75
	Dai et al. (2016)	Prawn	HSI	Image, tabular	400–1 000 nm, 206 spectrals	Reg: TVB-N	240	UVE, feature selection	LS-SVM	RMSE: 0.72; R²: 0.95
	Liang et al. (2019)	Pork	Terahertz spectroscopy	Tabular	0.2–2.0 THz, 250 spectrals	Reg: K value	80	FD, SG, PCA	BP-AdaBoost	RMSE: 9.89; R²: 0.84
	Fengou et al. (2020)	Pork	FTIR and MSI	Image, tabular	1 800 –900 cm⁻¹, 405–970 nm	Reg: TVC	903	PCA, PLS, relief, AutoEncoder	SVM	RMSE: 0.88; Corr: 0.83
	Dourou et al. (2021)	Chicken	FTIR	Tabular	1 800 –900 cm⁻¹	Reg: TVC	878	SNV	SVM	RMSE: 0.72; R²: 0.76
	Kolosov et al. (2023)	Pork	MSI	Image, tabular	1 200 × 1 200, 18 spectrals	Reg: TVC	847	Resize, normalize	ResNet-34	MAE: 0.05
	Lakehal and Lakehal (2023)	Beef	RGB camera (smartphone)	Image	-	Reg: time	120	Color space (CIELAB)	DNN	RMSE: 0.06; R²: 0.97

1D-CNN, 1-dimensional convolutional neural network; Acc, accuracy; ACO, ant colony optimization; AFINN, adaptive fuzzy inference neural network; AFLS, adaptive fuzzy logic system; CIELAB, Commission Internationale de l’Eclairage color space; Cla, classification; CNN, convolutional neural network; Corr, Pearson correlation coefficient; DCRNet, detect cells rapidly network; DFA, XXX; DNN, deep neural network; E-nose, electronic nose; E-tongue, electronic tongue; FIR, finite impulse response; FTIR, Fourier-transform infrared; FWT, fast wavelet transform; GLCM, gray-level co-occurrence matrix; HSI, hyperspectral imaging; HSV, hue-saturation-value; KNN, K-nearest neighbors; LS-SVM, least-squares support vector machine; MAE, mean absolute error; MSC, multiplicative scatter correction; MSI, multispectral imaging; NIR, near-infrared; NR, number of recordings; PCA, principal component analysis; PLS, partial least-squares; PLSR, partial least-squares regression; Reg, regression; RF, random forest; RGB, red-green-blue; RoI, regions of interest; RMSE, root mean squared error; SG, Savitzky-Golay smoothing; SNV, standard normal variate; SVM, support vector machine; TVB-N, total volatile basic nitrogen; TVC, total viable count; Vis-NIR, visible and near-infrared.

Table 6.

Summary of literature on meat authentication, categorized by purpose (meat type, meat cut), focusing on collected data and prediction methods.

Category	Year	Animal	Data					Method
Category	Year	Animal	Equipment	Type	Features	Target	NR	Preprocessing	Model	Performance
Meat type	Tian et al. (2013)	Lamb, pork	E-nose	Tabular	10 features	Reg: pork ratio in lamb	800	PCA	DNN	RMSE: 5.26; R²: 0.97
	Güney and Atasoy (2015)	Fish	E-nose	Tabular	4 800 features	Cla: 3 class	129	Subsampling	DT	Acc: 96.1%
	Alfar et al. (2016)	Beef, chicken, lard	NIR	Tabular	900–1 500 nm, 10 spectrals	Cla: 3 class	120	SNV, normalize	SVM	Acc: 98.3%
	Acquarelli et al. (2017)	Chicken, pork, turkey	FTIR	Image, tabular	1 000–1 800 cm⁻¹, 448 spectrals	Cla: 3 class	120	-	CNN	Acc: 100%
	Al-Sarayreh et al. (2018)	Lamb, beef, pork	HSI	Image, tabular	672–957 nm, 25 spectrals	Cla: 4 class	86 535	Superpixel SLIC, normalize	3D-CNN	Acc: 96.1%
	Zhao et al. (2019)	Beef	Vis-NIR	Image, tabular	400–1 000 nm, 250 spectrals	Reg: adulteration level	76	Segment (RoI), feature selection (IWO)	LS-SVM	RMSE: 5.67; Corr: 0.97
	Al-Sarayreh et al. (2020)	Red meat (lamb, beef, pork)	Line-scanning Vis-NIR	Image	548–1 701 nm, 235 spectrals	Cla: 4 class	559 032	Superpixel segmentation	3D-CNN	Acc: 98.6%
	Ayaz et al. (2020)	Beef, chicken, lamb	Vis-NIR	Image, tabular	400–1 000 nm; 224 spectrals	Cla: 3 class	60	Segment (RoI), SG, PCA	SVM	Acc: 88.8%
	Zhang et al. (2022c)	Pork, lamb	Vis-NIR	Image, tabular	373–1 033 nm, 616 spectrals	Cla: 5 class	120	MSC, SG	CNN	Acc: 99.9%
	Robert et al. (2021)	Beef, lamb, venison	Raman spectroscopy	Tabular	313–1 895 cm⁻¹	Cla: 3 class	90	SNV, SG	SVM	Acc: 93.0%
	Sun et al. (2022)	Beef, pork, lamb	Raman spectroscopy	Tabular	0–2 000 cm⁻¹	Cla: 3 class	2 400	Wavelet denoising, normalize, feature selection	DNN	F₁-score: 99.8%
Meat cut	Sanz et al. (2016)	Lamb	HSI	Image, tabular	380–1 028 nm, 1 040 spectrals	Cla: 4 class	120	PCA	LSM	Acc: 96.67%
	Li et al. (2021a)	Beef	Vis-NIR	Image, tabular	500–800 nm, 6 spectrals	Cla: 3 class	555	GLCM, color space (CIELAB)	LDA	Acc: 90.9%
	Prakash et al. (2021)	Beef	RGB camera (smartphone)	Image	-	Cla: 5 class	7 987	Segment (RoI), augment	Ensemble (CNN, MLR, DT)	Acc: 99.1%
	Huang et al. (2022)	Pork	RGB camera (smartphone)	Image	-	Cla: 4 class	1 992	Resize, crop, augment	ResNet-50	Acc: 94.4%

3D-CNN, 3-dimensional convolutional neural network; Acc, accuracy; CIELAB, Commission Internationale de l’Eclairage color space; Cla, classification; CNN, convolutional neural network; Corr, Pearson correlation coefficient; DNN, deep neural network; DT, decision trees; E-nose, electronic nose; FTIR, Fourier-transform infrared; GLCM, gray-level co-occurrence matrix; HSI, hyperspectral imaging; IWO, improved whale optimization; LDA, linear discriminant analysis; LSM, least-squares mean; LS-SVM, least-squares support vector machine; MLR, multinomial logistic regression; MSC, multiplicative scatter correction; NIR, near-infrared; NR, number of recordings; PCA, principal component analysis; Reg, regression; RGB, red-green-blue; RMSE, root mean squared error; RoI, regions of interest; SG, Savitzky-Golay smoothing; SLIC, simple linear iterative clustering; SNV, standard normal variate; SVM, support vector machine; Vis-NIR, visible and near-infrared.

Meat quality assessment

Various meat quality parameters and other relevant chemical traits have been determined using AI-based ML approaches. Ndob and Lebert (2018) successfully predicted pH and water activity in pork by integrating data from a pH meter and an a_w meter with a deep neural network (DNN) model. Building on this foundation, Dixit et al. (2021) utilized NIR technology to predict pH and IMF levels in red meat using hyperspectral imaging (HSI) datasets from beef, lamb, and venison, with a custom CNN model achieving an R² of 0.89 for both parameters. In a related study focusing on specific fatty acid analysis, Wang et al. (2021) utilized NIR spectroscopy to quantify stearic acid content in lamb meat from 3 different cuts, with the least-squares (LS)-SVM achieving an RMSE of 0.18 and R² of 0.76.

El Karam et al. (2023) investigated NaCl concentrations ranging from 1.1% to 1.9% in pork muscle using ultraviolet spectrometry, with a SVM model achieving 97.6% classification accuracy. Advancing beyond single-parameter analysis, Cheng et al. (2023b) developed a multitask CNN model to predict lipid and protein oxidation in frozen-thawed pork, achieving R² values of 0.97 for thiobarbituric acid reactive substances (TBARS) and 0.96 for carbonyl content. Complementing this work, Cheng et al. (2023a) introduced a lightweight 3-dimensional (3D) CNN model specifically for TBARS prediction, achieving an RMSE of 0.03 and R² of 0.92. Furthermore, Cui et al. (2024) explored long-chain saturated fatty acid content prediction in lamb, beef, and pork using visible (Vis) NIR spectral data, with the CNN model achieving improved performance on generative interference adversarial autoencoder enhanced datasets (RMSE of 0.55 and R² of 0.72) compared to raw datasets.

Moving to visual and textural quality assessments, Sun et al. (2018) employed a computer vision system combined with AI models to classify color and marbling in pork, with the SVM model achieving 92.5% accuracy in color classification and 75.0% in marbling classification. Expanding on this multi-attribute approach, Tang et al. (2023) predicted 14 meat quality traits using Vis-NIR imaging combined with a DNN model, demonstrating high predictive accuracy for fat (R² = 0.72) and chemical lean (R² = 0.73), with moderate performance for other quality attributes including moisture, collagen, and color parameters.

In the realm of sensory evaluation, Wang et al. (2019) utilized an electronic (E) tongue sensor array to detect ions in beef samples, with the SVM model achieving 90.0% accuracy in flavor scoring. Similarly, Cui et al. (2022) employed comprehensive two-dimensional gas chromatography-mass spectrometry (GC × GC – MS) and a DNN to predict beef flavor based on Maillard reaction products, demonstrating accuracy exceeding 90.0% in predicting flavor scores. For texture analysis, Zhang et al. (2022b) applied NIR spectroscopy to predict texture properties of lamb loins, with the SVM model achieving exceptionally high predictive accuracy for hardness (R² =0.98), gumminess (R² = 0.98), and chewiness (R² = 0.98).

Quality defect detection has shown considerable progress through AI-driven approaches. Wold et al. (2017) investigated wooden breast syndrome in chicken breast fillets using NIR spectroscopy, with a linear discriminant analysis (LDA) model achieving 99.5% accuracy in defect detection. Building on this success, Geronimo et al. (2019) combined computer vision with NIR spectroscopy to classify woody breast abnormalities in chicken, with an SVM model achieving 97.5% accuracy using NIR spectral data. Beyond biological defects, Ahn et al. (2020) detected needles in meat using X-ray images, with their CNN model reaching 99.8% classification accuracy. Additionally, breed and production method classification has been explored by Alaiz-Rodríguez and Parnell (2020) who employed Fourier-transform infrared (FTIR) spectroscopy to differentiate suckling lamb carcasses raised on milk replacer from those raised on ewe milk, with the DNN-PCA approach achieving 93.8% accuracy. García-Infante et al. (2024) further demonstrated the potential of multidata integration by classifying lamb carcasses from 3 native Spanish breeds using organoleptic, sensorial, and nutritional data, with DNN achieving 88.0% accuracy when combining both datasets.

Storage condition assessment has become increasingly important, with studies focusing on distinguishing between frozen and thawed meat. Huang et al. (2016a) classified pork samples into 4 categories based on freezing history using NIR spectroscopy, with a DNN model achieving 93.3% accuracy. Similarly, Xu and Sun (2017) employed HSI to identify freezer burn in frozen salmon fillets, with a TreeBagger ensemble classifier, achieving 97.8% accuracy. E-nose technology has also proven effective in this area, as demonstrated by Górska-Horczyczak et al. (2017), who introduced an E-nose to differentiate fresh pork from frozen, thawed, and spoiled counterparts, with a DNN model achieving 85.4% average precision across 7 categories.

Complementing spectroscopic approaches, Abie et al. (2021) leveraged bioimpedance to assess different thawing methods on meat quality, with a long short-term memory (LSTM) model achieving classification accuracies of 91.6% for fast thawing and 95.0% for slow thawing conditions. Recent portable technology applications have shown promising results, with Swanson and Gowen (2022) examining poultry samples using portable Vis-NIR with multivariate AI methods, achieving 88.0% accuracy for detecting thawed poultry with an SVM. Park et al. (2023) combined HSI with AI to classify beef samples into 3 storage states, with an SVM model achieving 97.6% classification accuracy, demonstrating the potential for rapid, noninvasive assessments of meat storage conditions.

Figure 5 provides a concise overview of spectroscopic techniques for meat–quality assessment and distinguishes workflows by data format. Tabular spectral measurements are smoothed and feature–selected before predictive models such as DNN or SVM are trained. Conversely, HSI data undergo RoI extraction followed by analysis with CNN. Both pipelines support regression of physicochemical attributes (pH, moisture, acidity) and classification of product quality and storage conditions (normal vs. defective, frozen vs. thawed), underscoring the versatility of AI–enabled spectroscopy.

Figure 5.

Overview of spectroscopic approaches for meat quality assessment: analysis of spectral data for predicting attributes, quality classification, and storage condition. CFS, correlation-based feature selection; PCA, principal component analysis; RoI, regions of interest; SG, Savitzky-Golay smoothing; SNV, standard normal variate.

Meat freshness evaluation

Freshness evaluation can be broadly categorized into freshness status analysis and microbe analysis. In freshness status analysis, the primary objective is to classify meat into discrete categories such as fresh, semi-fresh, and spoiled, with classification criteria varying considerably among studies. In contrast, microbe analysis focuses on predicting microbial counts generated during spoilage, enabling more precise evaluation of overall freshness.

A variety of sensor- and imaging-based approaches have been explored to categorize meat freshness. Hasan et al. (2012) employed an E-nose system to classify meat and fish samples into 4 categories, with SVM and K-nearest neighbors (KNN) models achieving accuracies of 94.5% and 96.2%, respectively. Building on this foundation, Haddi et al. (2015) combined an E-nose and E-tongue with AI models to classify beef, goat, and sheep freshness over 5 storage d, achieving 81.3% accuracy for E-nose data and 100% for E-tongue data. Vajdi et al. (2019) monitored fish headspace over a 15-d period using an E-nose device to classify samples into freshness categories, with a DNN model achieving 96.8% classification accuracy.

Expanding beyond electronic sensing, several studies have employed HSI techniques for freshness analysis. Huang et al. (2016b) compared NIR spectroscopy with computer vision systems to assess fish freshness, with a DNN model achieving 90.0% accuracy for image-based data and 93.3% for spectral data. Yang et al. (2017) utilized HSI data to classify cooked beef samples as fresh, medium fresh, or spoiled, achieving 97.1% accuracy with a LS-SVM. Ropodi et al. (2017) employed multispectral imaging (MSI) to distinguish beef from horse meat and detect adulteration, achieving 95.3% accuracy using an SVM. Alshejari and Kodogiannis (2017) investigated packaging conditions and storage temperatures on beef using multispectral images, with a DNN achieving 90.1% accuracy. Zhang et al. (2022a) advanced lamb freshness prediction using Vis-NIR imaging with chemical measurements, with a RF model achieving 91.0% accuracy for classifying lamb into premium, subfresh, and spoiled categories. Huang et al. (2023) integrated MOF@SnS₂ sensors with AI to determine beef storage duration, with a DNN model achieving 78.5% accuracy.

The accessibility of smartphone technology has led to extensive research utilizing RGB cameras for freshness analysis. Arsalane et al. (2018) evaluated beef freshness using RGB images, extracting color space features with an SVM model achieving 100% classification accuracy. Guo et al. (2020) employed a ResNet-101 model on smartphone images of chicken, fish, and beef to classify samples as fresh, less fresh, or spoiled, achieving 98.5% accuracy. Similarly, Amani and Sarkodie (2022) utilized a CNN model on meat images to differentiate fresh from spoiled meat, achieving 100% classification accuracy. Hewawasam et al. (2023) developed an integrated poultry management system using RGB cameras, with a MobileNet model achieving 100% accuracy in identifying chicken slaughter timing. Kim et al. (2023b) fused RGB images and air quality measurements using MobileNet and 1-dimensional (1D) CNN, achieving 99.4% classification accuracy for categorizing pork freshness. Kim et al. (2023c) employed a color sensor to measure hue-saturation-value (or HSV) values for pork classification, achieving 98.7% accuracy with a 1D-CNN.

Recent advances in computer vision have further enhanced freshness assessment capabilities. Elangovan et al. (2024) classified red meat freshness into 2 or 3 categories using RGB cameras, with ConvNet-18 achieving 99.4% accuracy for 2-category classification and ConvNet-24 achieving 96.6% for 3-category classification. Arsalane et al. (2024) predicted beef freshness by analyzing camera-acquired images using fast wavelet transform feature extraction, with KNN achieving 92.5% accuracy and SVM achieving 90.1%. Zheng et al. (2024) developed detect cells rapidly network (or DCRNet) for detecting and counting stained cells to assess meat quality, achieving an average precision score of 81.2% while reducing manual cell counting workload to less than 0.5%.

Some research efforts combine freshness classification with microbial activity estimation. Papadopoulou et al. (2013) employed a portable E-nose to assess beef fillet samples, with an SVM model achieving 90.4% classification accuracy and a correlation coefficient of 0.86 for predicting microbial counts. Kodogiannis et al. (2014) integrated FTIR spectroscopy with neuro-fuzzy modeling to classify beef samples while simultaneously estimating microbial counts, with their adaptive fuzzy logic system achieving 95.9% classification accuracy and a regression RMSE of 0.37. Gu et al. (2017) focused on lipid oxidation in Chinese style sausages by classifying 3 quality levels and performing regression analyses for acid value using an E-nose, with both DNN and SVM models achieving 100% classification accuracy and the SVM model attaining an R² of 0.98 for regression analysis. Kaswati et al. (2020) used Vis-NIR imaging to classify chicken samples as fresh or spoiled while predicting pH levels, with a RF model achieving 85.5% classification accuracy and partial least-squares regression model yielding R² values between 0.80 and 0.84 for pH prediction.

A substantial group of investigations has focused on estimating total volatile basic nitrogen (TVB-N), which is closely linked to microbial spoilage. Huang et al. (2014) employed NIR spectroscopy, computer vision, and E-nose techniques to predict TVB-N content in pork samples, with a DNN model combining all 3 data sources achieving an RMSE of 2.73 and R² of 0.95. Huang et al. (2015) proposed a NIR spectroscopy system with a BP-AdaBoost algorithm, achieving an RMSE of 6.94 and correlation coefficient of 0.83. Khulal et al. (2016) quantified TVB-N content in chicken using HSI, with a DNN model incorporating the ant colony optimization method achieving an RMSE of 6.38 and correlation coefficient of 0.75. Dai et al. (2016) predicted TVB-N content in prawns during cold storage using HSI data, with an LS-SVM model achieving an RMSE of 0.72 and correlation coefficient of 0.95.

Beyond TVB-N analysis, some approaches target additional spoilage indicators. Liang et al. (2019) employed terahertz spectroscopy to predict the K value in pork samples, with a BP-AdaBoost model achieving an RMSE of 9.89 and R² of 0.84. For total viable count (TVC) prediction. Fengou et al. (2020) combined FTIR spectroscopy and MSI data to evaluate TVC in meat samples, with integrated MSI and FTIR data yielding an RMSE of 0.88 and correlation coefficient of 0.83. Dourou et al. (2021) employed FTIR spectroscopy on chicken liver samples under various temperature conditions, with an SVM model achieving an RMSE of 0.72 and R² of 0.76 for noninoculated samples. Kolosov et al. (2023) integrated MSI data with a deep CNN model to measure TVC in pork samples, with ResNet-34 achieving MAE values of 0.05 for aerobically stored samples and 0.06 for modified atmosphere packaged samples. Lakehal and Lakehal (2023) collected beef samples across 6 freezing periods to predict storage time using Commission Internationale de l’Eclairage color space (or CIELAB) transformation, with a DNN model achieving an RMSE of 0.06 and R² of 0.97.

Figure 6 provides an overview of meat freshness evaluation using various equipment. In summary, meat freshness evaluation utilizes 2 main types of data collection: tabular data obtained from E-nose and spectroscopic sensors and image data captured through RGB imaging and HSI. For tabular data, the collected information was preprocessed through smoothing techniques (including standard normal variate [SNV], Savitzky-Golay smoothing [SG], and normalization) and dimension reduction using PCA. In addition, visual information of the meat was captured using RGB imaging. The images underwent further preprocessing through region of interest segmentation, augmentation (using rotations and flips), and resizing. These preprocessed data enabled the AI model to perform dual analytical tasks: classification of meat status (fresh, semi-fresh, and spoiled) and regression analysis of microbial content (TVC and TVB-N levels).

Figure 6.

Overview of diverse sensing approaches for meat freshness evaluation: data-specific preprocessing of electronic nose measurements, spectroscopic analysis, and red-green-blue imaging techniques for freshness status classification and microbial content prediction. PCA, principal component analysis; RGB, red-green-blue; RoI, regions of interest; SG, Savitzky-Golay smoothing; SNV, standard normal variate; TVB-N, total volatile basic nitrogen; TVC, total viable count.

Meat authentication

Meat authentication analysis typically involves predicting the extent of adulteration across different meat types or identifying specific muscle cuts. For meat type classification, the objective is to differentiate among pork, lamb, and beef based on their composition or to determine the level of admixture. For meat cut classification, the goal is to classify samples according to the distinctive features. Table 6 provides a summary of studies that apply AI to meat evaluation, with a specific focus on meat authentication.

Early research in this field utilized electronic sensing approaches. Tian et al. (2013) employed an E-nose to predict pork percentage in minced lamb and pork mixtures, with a DNN model achieving R² of 0.97 and RMSE of 5.26, outperforming multiple linear regression. Güney and Atasoy (2015) identified fish species using an E-nose with metal oxide gas sensors, with a 2-level binary decision trees (DT) model achieving 96.1% classification accuracy, surpassing naive Bayes (84.7%) and KNN (80.0%).

Expanding beyond E-nose approaches, several studies have employed spectral techniques with remarkable success. Alfar et al. (2016) authenticated and classified fats derived from beef, chicken, and lard using micro-NIR spectrometry, with a SVM model achieving 98.3% 3-class classification accuracy. Acquarelli et al. (2017) discriminated chicken, pork, and turkey products using FTIR spectroscopy, with a CNN model achieving 100% accuracy. Al-Sarayreh et al. (2018) classified lamb, beef, pork, and fat using HSI data, with a 3D-CNN achieving 96.1% accuracy and operating 4.7 times faster than SVM.

Adulteration detection has become increasingly important in meat authentication. Zhao et al. (2019) employed Vis-NIR techniques to detect adulteration in spoiled beef, with a LS-SVM model achieving an RMSE of 5.67 and correlation coefficient of 0.97. Al-Sarayreh et al. (2020) investigated snapshot HSI coupled with deep learning to classify red meat, with a 3D-CNN model achieving classification accuracies of 98.6%, 96.9%, and 97.1% on line-scanning, NIR, and visible snapshot datasets, respectively. Ayaz et al. (2020) used HSI to classify minced beef, chicken, and lamb, with an SVM model achieving 88.8% accuracy. Zhang et al. (2022c) introduced a CNN to predict pork content in adulterated lamb at various levels, achieving classification accuracies of 100%, 100%, and 99.9% for fresh, frozen-thawed, and mixed datasets, respectively.

Advanced spectroscopic techniques have further enhanced meat species identification capabilities. Robert et al. (2021) employed Raman spectroscopy to distinguish among beef, venison, and lamb, with an SVM model achieving 93.0% accuracy with a radial basis function kernel. Sun et al. (2022) integrated laser-induced breakdown spectroscopy and Raman spectroscopy to classify meat species (pork, beef, and lamb), with a DNN model achieving F₁-scores of 99.8% for beef, 99.4% for lamb, and 99.1% for pork.

Research focusing on meat cut classification has demonstrated the potential for precise muscle-specific identification. Sanz et al. (2016) classified 4 types of lamb muscles using HSI data, with a least-squares mean classifier achieving 96.6% accuracy. Li et al. (2021a) developed a classification model for 3 beef cuts (sirloin, shank, and flank) by integrating Vis-NIR imaging with AI, with a LDA model reaching 90.9% accuracy. Recent developments in computer vision have opened new avenues for meat authentication. Prakash et al. (2021) employed an ensemble AI approach that merged CNN, multinomial logistic regression, and DT to categorize 5 muscle types of meat using RGB images, with the ensemble model achieving 95.0% accuracy using grayscale images and 99.1% with original colored images. Huang et al. (2022) investigated the classification of 4 pork primal cuts (ham, loin, belly, and neck) using mobile phone images, with a ResNet-50 model enhanced with convolutional block attention module achieving 94.4% classification accuracy.

Figure 7 synthesizes spectroscopic meat–authentication studies that use tabular signals from E–nose and other sensors, along with HSI and RGB imagery. Tabular spectra are enhanced by smoothing (SNV, SG, normalization) and feature–selection methods such as PCA and improved whale optimization, while HSI/RGB images undergo superpixel– and SLIC–based segmentation to capture spatial context. These pipelines enable AI models to distinguish meat species (beef, pork, lamb) and cuts (neck, loin, ham) with high accuracy, underscoring the strength of AI–enabled spectroscopy for comprehensive meat authentication.

Figure 7.

Overview of spectroscopic analysis for meat authentication: data-specific preprocessing of spectral measurements and hyperspectral imaging data for meat type and cut identification, followed by artificial-intelligence-based classification. IWO, improved whale optimization; PCA, principal component analysis; SG, Savitzky-Golay smoothing; SLIC, simple linear iterative clustering; SNV, standard normal variate.

Market analysis and consumer preferences

Market analysis for the meat industry often centers on forecasting prices and understanding consumer preferences, thereby facilitating data-driven decision-making and delivering economic benefits. Price forecasting studies generally aim to predict the trends of meat prices based on daily, monthly, or annual averages, while preference prediction research focuses on forecasting consumer preferences based on individual carcass characteristics. Table 7 provides a summary of studies that apply AI-based methods to market analysis.

Table 7.

Summary of literature on market analysis, categorized by purpose (price prediction, preference prediction), focusing on collected data and prediction methods.

Category	Year	Animal	Data					Method
			Equipment	Type	Features	Target	NR	Preprocessing	Model	Performance
Price	Chen et al. (2022a)	Pork	Online	Tabular	-	Reg: monthly	192	-	Bi-RNN-LSTM	RMSE: 0.69; MAPE: 3.36
	Chuluunsaikhan et al. (2020)	Pork	Online	Tabular	News	Reg: daily	2 466	Topic modeling, TF-IDF	LSTM	RMSE: 1,155; MAPE: 5.1
	Chen et al. (2021)	Pork	Online	Tabular	News	Reg: daily	1 031	BERT	Multi-BERT-LSTM	RMSE: 0.96; MAPE: 0.17
	Ma et al. (2019)	Pork	Online	Tabular	22 factors	Reg: yearly	16	-	DBN	RMSE: 1.20; MAPE: 7.13
	Yang et al. (2021)	Pork	Online	Tabular	12 factors	Reg: monthly	65	-	ARIMA-LSTM	RMSE: 2.03
	Suaza-Medina et al. (2023)	Pork	Online	Tabular	8 factors	Reg: weekly	322	-	ARIMA-RNN	R²: 0.98; R²: 0.97
	Rahmani et al. (2024)	Beef	Online	Tabular	4 factors	Reg: monthly	225	Normalize	AdaBoost	RMSE: 0.16; MAPE: 38.29
Preference	Ko et al. (2023)	Pork	Ultrasound (AutoFomIII)	Tabular	40 features	Reg: flavor, appearance preference score	6 917	Feature selection, label encoding, normalize	Stacking ensemble	RMSE: 0.35; MAPE: 11.17; RMSE: 0.14; MAPE: 5.02
Preference	Jeong et al. (2024b)	Pork	Ultrasound (AutoFomIII)	Tabular	17 features	Reg: flavor preference score	2 321	Feature selection, label encoding, normalize	Stacking ensemble	RMSE: 0.16; MAPE: 5.34

Acc, accuracy; ARIMA-LSTM, autoregressive integrated moving average long short-term memory; ARIMA-RNN, autoregressive integrated moving average recurrent neural network; BERT, bidirectional encoder representations from transformer; Bi-RNN-LSTM, bi-recurrent neural network long short-term memory; Cla, classification; Corr, Pearson correlation coefficient; DBN, deep belief network; LSTM, long short-term memory; MAPE, mean average percentage error; Multi-BERT-LSTM, multi-bidirectional encoder representations from transformer long short-term memory; NR, number of recordings; Reg, regression; RMSE, root mean squared error; RNN, recurrent neural network; TF-IDF, term frequency-inverse document frequency.

Market analysis

Chen et al. (2022a) forecasted monthly pork prices using a bi-recurrent neural network (RNN) LSTM model on historical price records, achieving a RMSE of 0.69 and MAPE of 3.36, outperforming RNN and standard LSTM models. Recognizing the importance of external factors, some researchers have incorporated additional variables to enhance predictive accuracy. Chuluunsaikhan et al. (2020) integrated pig-related news with historical price data in an LSTM model, reducing RMSE from 1 928 to 1 155 and MAPE from 8.4 to 5.1 compared to autoregressive integrated moving average with exogenous variables (or ARIMAX). Chen et al. (2021) embedded online news articles using a bidirectional encoder representations from transformer (BERT) model within a multi-BERT-LSTM framework, achieving an RMSE of 0.96 and MAPE of 0.17, significantly outperforming standard LSTM.

Building on this multifactor approach, Ma et al. (2019) integrated external features including soybean meal prices and macroeconomic indicators into AI algorithms, with a deep belief network model achieving an RMSE of 1.20 and MAPE of 7.13, outperforming autoregressive integrated moving average (ARIMA). Yang et al. (2021) compared various models using price data and macroeconomic indicators, with a hybrid ARIMA-LSTM model achieving the best performance (RMSE of 2.038). Suaza-Medina et al. (2023) focused on the Lleida market by incorporating regional market data, with ARIMA achieving an R² of 0.98, outperforming RNN and LSTM. Rahmani et al. (2024) expanded the dataset to include multiple economic indicators, with RF and AdaBoost achieving lower RMSE values of 0.16 and 0.16, respectively, compared to ARIMA.

Consumer preferences

Ko et al. (2023) proposed a deep learning-based framework to predict flavor and appearance preferences in pork. The study collected 1 767 flavor preference data points and 5 150 appearance preference data points, as well as pork characteristics measured using ultrasound equipment (i.e., AutoFomIII) and additional sex and breed information. The collected data were preprocessed by applying label encoding to the sex and breed features and normalizing each feature before being used in a stacking ensemble of linear models, RF, and a DNN. The final system achieved a MAPE of 11.17 for flavor and 5.02 for appearance on a scale of 1 to 5. Jeong et al. (2024b) further expanded this concept by integrating an AutoFomIII device to collect pork characteristics and conducting a taste preference survey, yielding 2 321 preference scores. Their deep learning-based stacking model reached a MAPE of 5.34, outperforming baseline DNN (7.53) and extreme GB (7.93) models. Additionally, SHapley Additive exPlanations values revealed that consumer age, gender, and pork fat properties significantly influence taste preferences. Overall, these studies underscore the value of combining historical data, external market indicators, consumer demographics, and advanced AI techniques to improve meat price forecasting and optimize product offerings according to consumer preferences.

These studies underscore the value of combining historical data, external market indicators, consumer demographics, and advanced AI techniques to improve meat price forecasting and optimize product offerings according to consumer preferences. Figure 8 presents an overview of the methodology for predicting consumer preferences using ultrasound measurement equipment. The study utilized the AutoFomIII device to collect comprehensive carcass measurements for this purpose. The resulting tabular data underwent several preprocessing steps, including feature selection to identify relevant measurements, label encoding for categorical variables (such as sex and breed), and normalization to smooth the data. The processed data were then analyzed using an ensemble approach that stacked multiple models and combined them through a meta-regressor that successfully predicted consumer preference scores for both flavor and appearance attributes, demonstrating the effectiveness of integrated measurement and AI approaches in understanding consumer preferences for meat products.

Figure 8.

Overview of preference prediction using artifical intelligence technologies in market analysis; illustrating the workflow from data collection through model implementation.

Challenges and Future Direction

This section analyzes existing research on integrating AI technologies into meat processing, focusing on the challenges and future directions from the perspectives of data collection and methodological approaches. Figure 9 presents 8 key challenges along with their corresponding future directions across the domains of data acquisition, methodology, and application. Detailed discussions are provided in the subsequent subsections.

Figure 9.

Overview of current challenges and future directions in meat processing research using artificial intelligence methods. AI, artificial intelligence; GAN, generative adversarial network.

Data acquisition

A first challenge in data acquisition lies in ensuring sufficient diversity of the available data, which is often constrained by narrow experimental or environmental conditions. In many studies, data are acquired under restricted constraints, and the performance of the proposed predictive models is evaluated with data that were not used for training but still conform to the same constraints. As a result, these models may not generalize effectively to data collected under different conditions, potentially undermining the validity of the AI model’s generalization performance. To overcome this, recent studies have demonstrated that verifying model performance with openly available datasets is critical for enhancing reliability and generalizability. For instance, Xu et al. (2023) employed an X-ray type open dataset to validate their proposed model’s accuracy across multiple datasets. In addition, Elangovan et al. (2024) utilized open meat freshness datasets from Kaggle to evaluate accuracy under various data conditions.

To ensure that data sharing efforts yield maximum scientific value, the research community increasingly advocates for managing datasets in accordance with the FAIR (findable, accessible, interoperable, and reusable) principles (Wilkinson et al., 2016). A critical component of achieving FAIR data compliance, particularly for ensuring interoperability, is the establishment of comprehensive data standards. This standardization principle extends beyond the definition of anatomical features, such as standardized keypoints for carcass joints (Manko et al., 2022) analogous to facial landmarks in computer vision (Kazemi and Sullivan, 2014). Rather, it encompasses the broader framework of data types, acquisition protocols, and metadata schemas that define the purpose and contextual information of collected datasets. The establishment of such comprehensive standards is essential for creating datasets that achieve both technical compatibility and semantic interoperability. Ultimately, this ecosystem of open and standardized data serves as the crucial prerequisite for establishing fair benchmarks to objectively evaluate and advance different AI methodologies.

A second significant challenge in data acquisition involves the limited quantity and representativeness of available data, as some studies rely on less than 100 samples to train deep learning models. In particular, studies that employ spectroscopy equipment, such as NIR and Vis-NIR (Ayaz et al., 2020; Zhang et al., 2022c), typically collect fewer data than those that use RGB cameras (Sun et al., 2018; Prakash et al., 2021). Nonetheless, increasing the dataset size allows models to capture a broader range of patterns and improves deep learning performance. To address this issue, 2 principal approaches have been proposed. First, transfer learning leverages models previously trained on large-scale datasets in other domains, thereby reducing the need for extensive, domain-specific data. For example, Huang et al. (2022) applied transfer learning for meat cut classification, which not only improved model accuracy but also reduced training time. By utilizing preexisting feature representations, transfer learning proves particularly valuable when acquiring large amounts of new data is impractical. Second, generative adversarial networks can be applied to generate synthetic data that resemble real-world observations. By integrating artificially generated data into training sets, researchers can increase both the quantity and diversity of samples, thereby enabling models to learn more robust representations in data-scarce environments. For instance, Cui et al. (2024) employed a generative inference adversarial autoencoder model to generate synthetic data for training, effectively enhancing their dataset and model performance.

A third critical challenge in data acquisition arises from the cost, in both time and labor, associated with labeling. For instance, labeling individual data points and manually drawing bounding boxes in object detection tasks demands substantial human resources. Although most current research projects involve datasets ranging from a few hundred to a few thousand samples, labeling costs can escalate exponentially when the scale of data reaches ten- or even-hundreds of millions. One promising approach to mitigate this issue is the use of active learning (Monarch, 2021; Wu et al., 2022b), which focuses labeling efforts on samples where the model’s predictions exhibit high uncertainty, thereby making more efficient use of limited labeling resources. This strategy has gained considerable attention as a means to minimize the decline in model performance while reducing labeling burdens in various applications that require large-scale data, and its practical implementations are steadily expanding.

Methodology

In terms of methodology, the first challenge is ensuring predictive reliability, given the black-box nature of AI models. Although state-of-the-art deep learning techniques have achieved notable success in meat processing tasks such as defect detection and quality assessment, it remains difficult to interpret the predictive outcomes generated by these complex systems. This lack of transparency can compromise stakeholder confidence in critical domains such as food safety and quality control, particularly in contexts where regulatory compliance and liability considerations are paramount. Explainable AI offers a potential remedy by illuminating the model’s decision-making process and identifying the specific features or data regions that contribute most significantly to its predictions. For instance, Jeong et al. (2024b) demonstrated that the prediction model is most strongly influenced by consumer age, gender, and pork fat properties when forecasting taste preferences. Such transparency not only strengthens stakeholder confidence in the model’s predictions but also highlights practical avenues for improving its performance.

Beyond interpretability concerns, establishing robust validation methodologies constitutes another fundamental challenge, as the determination of appropriate reference standards and ground truth values exhibits substantial variability across research investigations. For instance, in IMF prediction, researchers may choose to predict continuous measured values or classify samples into predefined ranges with each approach requiring different validation frameworks, making it challenging to determine the most appropriate methodological approach.

A second methodological issue arises from the reliance on a single data type. Many studies utilize only one form of data acquired from devices such as NIR spectrometers or RGB cameras. However, in the work of Huang et al. (2014), data collected from an E-nose, an RGB camera, and NIR equipment were combined to yield more precise predictions of microbial activity in meat. Multimodal approaches that involve early, middle, or late fusion of different data types can significantly enhance predictive accuracy (Pawłowski et al., 2023). By analyzing multiple data modalities simultaneously, a broader range of features is captured, leading to improved reliability. Consequently, integrating multimodal data not only boosts prediction performance but also enhances the robustness and generalizability of meat quality evaluation models.

A third methodological challenge arises from data drift, a phenomenon in which the statistical properties of input data shift over time (Agrahari and Singh, 2022). For instance, images initially captured under bright lighting conditions may subsequently be taken in darker settings, and environmental factors such as inflation rates or pandemics can cause prices to rise or fall beyond typical baselines, leading to significant drifts in economic data. To address this issue, incremental learning (Polikar et al., 2001) and continual learning (Wang et al., 2024) techniques enable models to adapt to evolving data distributions by updating their internal parameters or acquiring new tasks while preserving previously learned knowledge. Consequently, by proactively managing data drift in IoT environments established within livestock facilities, stakeholders can safeguard model reliability, sustain long-term performance, and ensure that AI-driven insights remain both accurate and actionable.

Application

In the application domain, the first challenge is ensuring that AI models achieve high predictive accuracy in modern slaughterhouses and factory environments while also meeting the demands of high implementation costs. As a result, lightweight deep learning strategies (Wang et al., 2024) and model compression techniques (Dantas et al., 2024), such as pruning, quantization, and knowledge distillation, have attracted considerable attention. These approaches reduce computational overhead without substantially compromising accuracy, thereby minimizing operational costs and inference times and facilitating the transition from research prototypes to large-scale industrial deployments. Ultimately, these methods enable rapid, accurate, and resource-efficient decision-making in real-world meat processing workflows.

However, beyond computational efficiency, successful industrial implementation faces additional practical challenges that are often underestimated in research settings. Equipment integration complexities arise from the need to seamlessly incorporate AI systems with existing processing infrastructure, while real-time processing requirements demand consistent performance under varying operational conditions. These multifaceted implementation barriers highlight the necessity for comprehensive deployment strategies that address not only algorithmic performance but also the broader operational requirements of modern meat processing facilities. Ultimately, these methods enable rapid, accurate, and resource-efficient decision-making in real-world meat processing workflows, provided that these practical implementation challenges are adequately addressed.

In the application domain, a second challenge arises from the fact that most AI-driven research in meat processing has predominantly focused on quality analysis. However, significant opportunities remain to extend AI applications across the entire meat processing cycle. For instance, although studies applying AI to cultured meat are still relatively few, recent work on optimizing culture media conditions (Cosenza et al., 2021; Nikkhah et al., 2023) and on automated calculation of the fusion index during muscle formation (Weisrock et al., 2024; Jeong et al., 2025) illustrate the potential for deeper exploration in this area.

In addition, further research into robotic automation is essential for improving efficiency, particularly in overcoming the challenge posed by the anatomical diversity of animal carcasses. Building on initial work in detecting anatomical joint points designed for automated cutting (Manko et al., 2022), future investigations should focus on developing systems for real-time identification and guidance of optimal cutting directions. Furthermore, a significant opportunity lies in leveraging generative AI and large language models such as ChatGPT, which could be developed into advanced systems for a wide range of applications. These applications can be broadly categorized into 2 main domains: operational support and knowledge transfer. In the operational domain, they can serve as advanced tools for real-time process control, dynamic scheduling, and interactive decision support. In the knowledge transfer domain, they show promise as interactive training programs for novice operators and as specialized chatbots providing expert guidance in livestock management (Dwivedi et al., 2023). Future investigations into these emerging applications hold promise for significantly enhancing automation capabilities, optimizing resource utilization, and elevating food safety standards throughout the entire meat supply chain.

Conclusion

This comprehensive review has examined the integration of AI technologies across the meat processing industry, encompassing meat production, meat quality, and market analysis. Our analysis reveals significant progress in applying AI methods to enhance productivity, ensure product quality and safety, and optimize management throughout the meat supply chain. These advancements can be systematically categorized into 3 primary domains.

In the domain of meat production, AI technologies have demonstrated considerable success in predicting carcass composition and estimating specific cut characteristics. The implementation of computer vision systems and deep learning algorithms has enabled more accurate and efficient assessment of key quality parameters such as IMF content, color, water-holding capacity, and/or tenderness. Also, AI-based methods have shown promising results in areas including freshness detection, defect identification, and storage condition monitoring. The integration of multiple sensor technologies with advanced AI algorithms has improved the accuracy and reliability of quality assessments by enabling noninvasive evaluation techniques. In market analysis, AI applications have effectively enhanced price forecasting capabilities and consumer preference prediction. These advancements support data-driven decision-making processes and contribute to greater economic efficiency within the industry.

While these achievements across all 3 domains demonstrate the transformative potential of AI in meat processing, several critical challenges remain to be addressed, including data acquisition limitations, methodological constraints, and implementation barriers within industrial settings. Future research endeavors should prioritize the development of more robust and comprehensive datasets, enhancement of model interpretability and explainability, and expansion of AI applications to emerging sectors such as cultured meat production and fully automated handling systems. As AI technologies continue to advance rapidly, their strategic integration into meat processing operations presents unprecedented opportunities for enhancing industry capabilities while addressing contemporary challenges in food production efficiency, waste reduction, and safety assurance. The continued development and systematic refinement of these technologies will be instrumental in establishing more efficient, sustainable, and reliable meat processing systems that meet evolving industry demands.

To fully realize these opportunities, future research may need to further explore the underlying sensing technologies, including detailed examinations of their detection principles, sensor configurations, and operational characteristics. Such comprehensive analyses will provide the foundation necessary for informed technology selection and optimization in commercial meat processing applications. Furthermore, our future research will expand into emerging applications including automated animal inspection, animal welfare monitoring, disease surveillance, and automated animal monitoring systems, recognizing the growing importance of these areas in modern livestock management and processing facilities.

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author Contribution

Kyungchang Jeong: writing—original draft; Gyuchan Jo: writing—original draft; Jong Ho Lee: validation; Yuan H. Brad Kim: conceptualization; Jungseok Choi: investigation; Hongseok Oh: methodology; Ji-Hoon Jeong: methodology; and Euijong Lee: writing—review and editing.

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