Review on Machine Learning and Deep Learning and Its Benefit for Beef and Pork Classification - review

Aug 2, 2026

Machine learning and deep learning are widely used for classification tasks, and deep learning has recently drawn strong attention because of its ability to learn discriminative features. This review synthesizes the theory of machine learning and deep learning and focuses on prior studies that apply machine learning and deep learning methods to distinguish beef from pork. It also examines hybrid approaches designed to improve classification accuracy, supporting more reliable identification despite subtle textural differences.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Meat adulteration and consumer considerations

Digital solutions using deep learning and CNN architectures

Motivation for hybrid CNN-based transfer learning

Related Work

Global meat demand and regional socioeconomic factors

Beef and pork meat demand trends

Review on Machine Learning and Deep Learning and Its Benefit for Beef and Pork Classification Imam Syaukani1, Siti Zarina Mohd Muji2* 1 Faculty of Systems Engineering, Technology University of Sumbawa, Jalan Raya Olat Maras, BatuAlang, Moyo Hulu, Kabupaten Sumbawa, Nusa Tenggara Barat 84371, INDONESIA 2 Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, 86400, MALAYSIA *Corresponding Author: [szarina@uthm.edu.my](szarina@uthm.edu.my) DOI: [https://doi.org/10.30880/jmer.2025.02.01.001](https://doi.org/10.30880/jmer.2025.02.01.001) | Article Info | Abstract | | --- | --- | | Received: 24 January 2025 | Machine learning and deep learning has been widely applied in wide | | Accepted: 26 April 2025 | application. Notably, deep learning recently received considerable | | Available online: 30 June 2025 | attention in classification. This paper reviewed the theory regarding machine learning and deep learning and then concentrate the review | | Keywords | on previous research that used the deep learning and machine learning technique for classification between beef and pork. It also reviewed the | | CNN, support vector machine, beef, pork | hybrid method to enhanced the accuracy. | 1. Introduction Meat stands as a staple in human diets, offering a rich source of protein vital for cognitive and physical well-being. With its widespread consumption, the market is abundant with various meat types. Although categorized for sale, some traders capitalize on soaring beef prices, yielding significant profits with minimalinvestment. Amidst these practices, there is an unfortunate occurrence of meat adulteration [1]. Consumer choices in meat purchases often hinge on factors such as safety, quality, and popularity, encompassing considerations like color, tenderness, flavor, and aroma [2] . Trust plays a pivotal role in shaping consumer preferences and behaviors, particularly when it comes to food choices and consumption habits [3] . Referencing Fig 1, the visual depiction highlights the contrasting textures between beef and pork. (a) (b) Fig. 1 Texture difference,(a) Beef; (b) Pork [1] The pervasive influence of technological progress on daily life significantly enhances individuals' efficiency in executing various tasks. Technology catalyzes more effective and time-efficient task completion [4] . A prime example of this impact is evident in the livestock and food sectors, where technology can aid the public in discerning between natural beef and pork meat [5][6]. Given the challenge of distinguishing between the different textures of beef and pork, a digital approach is one of the alternatives to solve this problem. One such technological solution involves leveraging the power of Deep Learning, a rapidly advancing branch of Machine Learning, with Convolutional Neural Networks (CNN) at its forefront. Various architectural models, including LeNet, AlexNet, VGGNet, ResNet, EfficientNet, ResNet50, and DenseNet, exemplify the diversity within CNNs. Typically, these architectures consist of stacked convolutional layers, a pooling layer, and a fully connected layer. This technological integration holds promise for revolutionizing the identification and differentiation of meat types, addressing the challenges posed by subtle textural variations [7] . This work which successfully classified benign and malignant breast cancer histopathology imaging subtypes using a hybrid CNN-LSTM-based transfer learning approach. The dataset included 2480 clear images and5429 cancer images. The previous research achieved an outstanding 99% accuracy inthe binary classification of benign and malignant cancer [8]. This success inspires our exploration of a similar hybrid model approach to differentiate between beef and pork meat in our current study. This review focus on related work from previous research based on theory regarding machine learning and deep learning and then concentrate t

FAQs

Why is distinguishing beef and pork important?

Meat adulteration creates risks for consumers, and purchasing decisions depend on perceived safety and quality. Subtle differences in texture make reliable identification challenging, motivating digital classification approaches.

How do machine learning and deep learning support beef vs. pork classification?

Deep learning models, especially CNN-based architectures, learn visual features from images. The review discusses how CNN architectures with convolution, pooling, and fully connected layers can capture texture variations.

What kinds of methods does the review highlight for improving accuracy?

The review emphasizes hybrid methods that combine deep learning techniques and other learning strategies to enhance classification performance, and it links this to prior work reporting high accuracy in related binary classification tasks.

Aug 2, 2026

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