Artificial Intelligence in the Food Industry: Transforming Safety, Efficiency, and Sustainability From Farm to Fork
7 affiliations
- 1Food Technology and Innovation Research Center of Excellence, School of Agricultural Technology and Food Industry Walailak University, Tha Sala Thailand
- 2Department of Food Science and Technology Government College Women University Faisalabad Pakistan
- 3Applied Animal Physiology Lab Abdelhamid Ibn Badis University Mostaganem Algeria
- 4Institute of Systems Biology (INBIOSIS) Universiti Kebangsaan Malaysia Bangi Selangor Malaysia
- 5Jamhuriya Research Center Jamhuriya University of Science and Technology Mogadishu Somalia
- 6Department of Chemistry, Preparatory Year Program Batterjee Medical College Jeddah Saudi Arabia
- 7College of Medicine and Health Sciences Wolaita Sodo Ethiopia
Abstract
Artificial intelligence (AI) is promptly changing the nature of food production and processing to increase safety, efficiency, quality assurance, and sustainability throughout the farm‐to‐fork chain. This review discusses the recent developments in AI in the food manufacturing industry, such as machine learning (ML), deep learning (Dl), computer vision (CV), hyperspectral imaging (HSI), robotics, Internet‐of‐Things (IoT) integration, and natural language processing. These technologies allow detecting contamination and fraud in real‐time, near‐perfect predictive maintenance less 30% unplanned downtime, automated quality checks with up to 96%–100% accuracy, AI‐enabled predictive maintenance (PdM) has demonstrated its effectiveness by decreasing unplanned downtime in industrial case studies through a reduction rate that reaches 30% of total downtime, resource‐efficient smart factories, and creative AI‐driven formulation of products. New paradigms include digital twins, explainable AI, computational gastronomy, and cultured meat production, highlighting the transformative power of AI. Some of the solutions include standardized datasets, lightweight architectures, federated learning, and scalable deployment training programs. When used responsibly, AI can help create food systems that meet regulatory requirements while achieving sustainable development goals and circular economy standards, and all three Sustainable Development Goals, like Zero Hunger (SDG 2), Responsible Consumption and Production (SDG 12), and Climate Action (SDG 13).
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BibTeX
@article{waqar2026artificial,
title = {{Artificial Intelligence in the Food Industry: Transforming Safety, Efficiency, and Sustainability From Farm to Fork}},
author = {Muhammad Waqar and Mariyam Shahid and Manat Chaijan and Worawan Panpipat and Djilali Benabdelmoumene and Ahmed Mediani and Abdifetah Ibrahim Omar and Sabrin R. M. Ibrahim and Qudrat Ullah and Temesgen Anjulo Ageru},
journal = {eFood},
year = {2026},
month = may,
doi = {10.1002/efd2.70161},
url = {https://doi.org/10.1002/efd2.70161},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial Intelligence in the Food Industry: Transforming Safety, Efficiency, and Sustainability From Farm to Fork
AU - Muhammad Waqar
AU - Mariyam Shahid
AU - Manat Chaijan
AU - Worawan Panpipat
AU - Djilali Benabdelmoumene
AU - Ahmed Mediani
AU - Abdifetah Ibrahim Omar
AU - Sabrin R. M. Ibrahim
AU - Qudrat Ullah
AU - Temesgen Anjulo Ageru
PY - 2026
DA - 2026/05/18
JO - eFood
DO - 10.1002/efd2.70161
UR - https://doi.org/10.1002/efd2.70161
AB - Artificial intelligence (AI) is promptly changing the nature of food production and processing to increase safety, efficiency, quality assurance, and sustainability throughout the farm‐to‐fork chain. This review discusses the recent developments in AI in the food manufacturing industry, such as machine learning (ML), deep learning (Dl), computer vision (CV), hyperspectral imaging (HSI), robotics, Internet‐of‐Things (IoT) integration, and natural language processing. These technologies allow detecting contamination and fraud in real‐time, near‐perfect predictive maintenance less 30% unplanned downtime, automated quality checks with up to 96%–100% accuracy, AI‐enabled predictive maintenance (PdM) has demonstrated its effectiveness by decreasing unplanned downtime in industrial case studies through a reduction rate that reaches 30% of total downtime, resource‐efficient smart factories, and creative AI‐driven formulation of products. New paradigms include digital twins, explainable AI, computational gastronomy, and cultured meat production, highlighting the transformative power of AI. Some of the solutions include standardized datasets, lightweight architectures, federated learning, and scalable deployment training programs. When used responsibly, AI can help create food systems that meet regulatory requirements while achieving sustainable development goals and circular economy standards, and all three Sustainable Development Goals, like Zero Hunger (SDG 2), Responsible Consumption and Production (SDG 12), and Climate Action (SDG 13).
SN - 2666-3066
ER -