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Applications of artificial intelligence in the dairy Industry: From farm to product development

  • Anandu Chandra Khanashyam
  • , Sandeep Jagtap
  • , Tarun Kumar Agrawal
  • , Priyamvada Thorakkattu
  • , Om Prakash Malav
  • , Hana Trollman
  • , Abdo Hassoun
  • , Bharathi Ramesh
  • , Vishnu Manoj
  • , Kaavya Rathnakumar
  • , Alaa El Din A. Bekhit
  • , Nilesh Nirmal
  • University of Minnesota Twin Cities
  • Lund University
  • Cranfield University
  • Chalmers University of Technology
  • Kansas State University
  • Guru Angad Dev Veterinary and Animal Sciences University
  • Mahidol University
  • University of Leicester School of Business
  • Sustainable AgriFoodtech Innovation and Research (SAFIR)
  • University of Aleppo
  • Icahn School of Medicine at Mount Sinai
  • TKM Institute of Technology
  • University of Wisconsin-Madison
  • University of Otago

Research output: Contribution to journalReview articlepeer-review

4 Citations (Scopus)

Abstract

The dairy industry faces increasing demand for enhanced productivity, sustainability, and innovation. Artificial Intelligence (AI) has emerged as a transformative tool capable of addressing these challenges by enabling data-driven decision-making across the dairy supply chain. AI integrates machine learning (ML), big data analytics (DA), and predictive algorithms (PA) to optimize processes, improve efficiency, and foster innovation. This review examines the diverse applications of AI in the dairy industry, including dairy farming, processing, and product development. In this context, an overview of AI, including ML, DA, and various algorithms used in these processes, is discussed. A major discussion has been provided on AI for animal performance (e.g., disease detection, reproductive management, milk yield enhancement, nutrition) and sustainable practices (e.g., emission control, precision farming). Furthermore, AI in dairy processing (quality control and process optimization) and product development (flavor and texture prediction, and customized products) has been developed. Finally, the challenges of AI integration, including data privacy, ethical considerations, and technical barriers, are reported. The findings indicate that AI revolutionizes traditional practices by enabling precise farming, energy-efficient processing, and the creation of customized, high-quality products. Despite its transformative potential, challenges, such as ethical concerns and technological limitations, must be addressed.

Original languageEnglish
Article number110879
JournalComputers and Electronics in Agriculture
Volume238
DOIs
Publication statusPublished - Nov 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 2 - Zero Hunger
    SDG 2 Zero Hunger
  2. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy
  3. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure
  4. SDG 12 - Responsible Consumption and Production
    SDG 12 Responsible Consumption and Production
  5. SDG 17 - Partnerships for the Goals
    SDG 17 Partnerships for the Goals

Keywords

  • Artificial intelligence
  • Dairy
  • Industry
  • Precision farming
  • Process
  • Sustainability

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