Advancing the meat industry with machine learning: A study of progress, challenges, and potential

Abstract

The integration of machine learning (ML) in the meat industry is reshaping traditional practices by introducing data-driven approaches to improve product quality, operational efficiency, safety, and sustainability. This comprehensive overview explores the application of key ML techniques including supervised learning, unsupervised learning, reinforcement learning, and deep learning in various domains such as meat quality assessment, supply chain optimization, adulteration detection, automated processing, and consumer behavior analysis. As ML algorithms become increasingly sophisticated and accessible, their ability to process large datasets from imaging systems, sensors, and chemical analyses enables the detection of complex patterns and the automation of critical decisions. While the benefits of ML in the meat industry are substantial, the adoption of these technologies is not without challenges. Issues such as data availability, high computational requirements, integration with legacy systems, and the need for standardized regulations pose significant barriers. Nonetheless, ongoing technological advancements particularly in the realms of IoT, big data, and predictive analytics are paving the way for more efficient disease prevention strategies, enhanced food safety, and reduced environmental impact. This paper highlights the current state, challenges, and future trends of machine learning applications in the meat industry. It emphasizes the potential of ML to build a more intelligent, transparent, and sustainable meat production ecosystem, ultimately aligning industry practices with modern consumer expectations and global food safety standards.