Poultry Quality Control: Systematic Review of Digital and Traditional Methods

Abstract

The poultry industry plays a fundamental role in global food security; however, ensuring the quality of its products remains a challenge, especially on small-scale farms with technological and infrastructure limitations. This systematic review aims to compare traditional and digital methods applied to quality control in poultry production, evaluating their accuracy. Using the PICO model and PRISMA methodology, 684 articles were identified in a database. Exclusion and inclusion criteria were applied, and 70 relevant studies published between 2019 and 2025 were selected from the Scopus database. The results show a growing acceptance of digital technologies such as the Internet of Things, artificial intelligence, computer vision, hyperspectral analysis combined with neural networks, and portable molecular diagnostic systems, which demonstrate that conventional methods are 93 to 99% accurate, fast, and reduce errors. However, its implementation faces challenges such as high costs, the need for technical training, and limitations in infrastructure and environmental adaptability. It is concluded that, although digital technologies represent a significant advance in quality control, their large-scale adoption requires strategic management in training, standardization, and accessibility to achieve sustainable implementation, strengthening the technological tools used for efficient management within the poultry sector.