Review article
Artificial Intelligence in Meat Production - Applications, Challenges and Future Directions
Goran Kiš
; Sveučilište u Zagrebu Agronomski fakultet, Zagreb, Hrvatska
*
Vatroslav Bubalo
; Klinička bolnica Dubrava, Zagreb, Hrvatska
Matea Gale
; Veterinarski fakultet Sveučilišta u Zagrebu, Zagreb, Hrvatska
Maja Popović Nikolić Džolan
; Veterinarski fakultet Sveučilišta u Zagrebu, Zagreb, Hrvatska
* Corresponding author.
Abstract
Computer systems are increasingly being introduced and used in the meat industry to speed up operations, standardize product quality, ensure food safety, and facilitate the tracking of raw materials with minimal waste. Modern plants use machine learning and deep learning algorithms, computer vision, robotics, and predictive models to utilize the large amounts of data collected daily by cameras and sensors on production lines. This technology offers solutions to concrete problems in the meat sector. Due to the significant natural differences between animals, challenging working conditions in slaughterhouses, and strict hygiene regulations, the human eye and manual assessment are no longer sufficient for high precision. Modern programs and algorithms no longer focus solely on recognizing and classifying individual pieces of meat or products but rather predict the course of the entire production process, automatically collect data, and aid in adjusting work in real-time. However, the spread of this technology is still limited by factors such as the quality of available data in the field, problems with connecting software from different manufacturers, the high cost of equipment, and the need for models that employees and experts can easily understand and verify. This review provides insight into the application of artificial intelligence in the meat sector and analyzes its advantages, challenges, and future development directions.
Keywords
artificial intelligence; meat industry; machine learning; meat quality assessment; food safety; sustainable production
Hrčak ID:
352927
URI
Publication date:
2.11.2026.
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