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Machine learning applications for postharvest poultry processing: a review.

Zhen Jia¹, Boce Zhang², Lucas Harper³

¹Department of Poultry Science, Auburn University, Auburn, AL, USA.

Critical Reviews in Food Science and Nutrition

|April 9, 2026

Summary

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This summary is machine-generated.

Machine learning (ML) enhances poultry processing efficiency and safety. This review explores ML applications in quality control, safety monitoring, and foreign material detection, paving the way for a smarter food supply chain.

Area of Science:

Food Science and Technology
Artificial Intelligence in Agriculture
Poultry Processing Engineering

Background:

Global poultry production is increasing, necessitating efficient and safe processing methods.
Ensuring product quality and regulatory compliance is crucial for consumer trust and market access.
Traditional processing methods face challenges in meeting growing demands and complex quality standards.

Purpose of the Study:

To review and elucidate the diverse applications of Machine Learning (ML) in postharvest poultry meat processing.
To explore ML-driven techniques including imaging, spectroscopy, sensors, and genomic sequencing.
To identify challenges, barriers, and future directions for ML adoption in the poultry industry.

Main Methods:

Comprehensive literature review of Machine Learning applications in poultry meat processing.

Keywords:

Adulteration Foreign material Intelligent packaging Machine learning Poultry meat processing Quality control Safety control and risk assessment

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Analysis of ML integration across various processing stages: operations, quality control, safety, and detection.

Exploration of specific ML-enabled technologies such as imaging, spectroscopy, and sensors.

Main Results:

Machine learning is successfully applied in smart processing, quality control, safety monitoring, risk assessment, foreign material detection, and adulteration identification.
ML-powered imaging, spectroscopy, and sensor technologies offer advanced analytical capabilities.
Challenges in ML adoption include data integration, cost, and workforce training, with potential solutions identified.

Conclusions:

Machine learning offers significant potential to revolutionize the poultry industry, enhancing sustainability, intelligence, and resilience.
Adoption of ML can lead to a more digitalized poultry sector and food supply chain.
Further research and strategic implementation are key to overcoming adoption barriers and realizing ML's full benefits.