Short review of current limits and challenges of application of machine learning algorithms in the dairy sector

Aug 4, 2026

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Livestock production is shifting toward a more sustainable model that reduces environmental impact while meeting the demand for high-quality food. Farms increasingly rely on technology and data streams from sensors and routine operations, enabling more objective decision-making through predictive analytics. Machine learning, as an Artificial Intelligence discipline for forecasting, inference, and clustering, is already used for issues such as early disease detection and is expected to support future welfare monitoring. This brief review summarizes the state of the art, emphasizing widely used algorithms and the main challenges limiting broad adoption in the dairy sector.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Introduction

Precision livestock farming and data challenges

Role of machine learning

Current limits and challenges in dairy applications

Common algorithm families

Obstacles to broad acceptance

Short review of current limits and challenges of application of machine learning algorithms in the dairy sector Lucia Trapanese1, Miel Hostens2, Angela Salzano3, Nicola Pasquino1 1 Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Italy 2 Department of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands 3 Department of Veterinary Medicine and Animal Production, University of Naples Federico II, Naples, Italy ABSTRACT In the last years, the livestock sector is moving towards a more sustainable animal-based industry, mitigating the environmental impact of livestock while meeting the demand for high-quality food. To achieve these goals, farms are using a more technological approach, adopting algorithms to manipulate the vast amount of data from sensors and routine operations. The results will be useful for making more objective decisions. In this context, machine learning ~~-~~ a branch of Artificial Intelligence applied to the study of prediction, inference, and clustering algorithms-can be successfully employed. Nowadays, machine learning algorithms are successfully used to solve many issues in the livestock sector, such as early disease detection, and they are expected to be employed in the future for welfare monitoring. This brief review gives an overview of the current state of the art of the most popular applications for dairy science and the most widely used and best-performing algorithms, highlighting the challenges and obstacles for broad acceptance of these techniques in the dairy sector. Section: RESEARCH PAPER Keywords: Precision livestock farming; machine learning; dairy sector Citation: L. Trapanese, M. Hostens, A. Salzano, N. Pasquino, Short review of current limits and challenges of application of machine learning algorithms in the dairy sector, Acta IMEKO, vol. 13 (2024) no. 1, pp. 1-7. DOI: 10. 21014/acta_imeko.v13i1 .1725 Section Editor: Leopoldo Angrisani, Università degli Studi di Napoli Federico II, Naples, Italy Received December 6, 2023; In final form March 4, 2024; Published March 2024 Copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This study was carried out within the Agritech National Research Center and received funding from the European Union Next-Generation EU (Piano Nazionale di Ripresa e Resilienza (PNRR) Missione 4 Componente 2, Investimento 1.4 D. D. 1032 17/06/2022, CN00000022) . This manuscript reflects only the authors views and opinions; neither the European Union nor the European Commission can be considered responsible for them. Corresponding author: Lucia Trapanese, e-mail: [lucia.trapanese2@unina.it](lucia.trapanese2@unina.it) 1. INTRODUCTION Nowadays, the livestock sector is facing many challenges, such as making animal-based food production more sustainable and efficient, mitigating the environmental impact of livestock, and satisfying the demand for high-quality food. To achieve these goals, the sector is moving toward a more technological approach by introducing a plethora of different solutions, such as i) automated milking systems to control milk quantity and quality, ii) electric feeders able to give the precise amount of food required, iii) wearable or environmental sensors to monitor animal health and welfare. These systems allow scientists, technicians, and breeders to control most aspects of the herd. This new approach is called precision livestock farming (PLF) . According to Berckmans et al. [1], PLF is the “management of livestock by continuous automated real-time monitoring of production/reproduction, health and welfare of livestock, and its environmental impact”. The adoption of new technologies in this field, such as embedded and

FAQs

What is precision livestock farming (PLF) and why is it used in dairy production?

PLF is management of livestock through continuous automated real-time monitoring of production/reproduction, health and welfare, and environmental impact. In dairy production, it is adopted to control herd-related aspects using sensor-driven and automated systems.

Why are classical statistical methods considered insufficient for dairy-sector sensor data?

Classical statistics may not handle “big data” well because it was designed for limited variables and smaller sample sizes. It also often requires strong prior hypotheses and knowledge about the data and observed phenomena.

What kinds of machine learning tasks and algorithm types are highlighted for dairy applications?

Machine learning supports forecasting, inference, and clustering, and can manage nonlinear, complex, noisy, and imprecise datasets common in dairy production. The review contrasts unsupervised and supervised learning, noting the role of labeled information in supervised methods.

Aug 4, 2026

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