journal article·2026-01-31Open access

Real-Time Oestrus Detection in Free Stall Barns: Experimental Validation of a Low-Power System Connected to LPWAN

Marco Bonfanti, Margherita Caccamo, Iris Schadt, Simona M. C. Porto

Applied Sciences· Volume 16· Issue 3

Abstract

The growing demand for resources for production in intensive livestock farming requires research to operate with an environmentally sustainable perspective and respect for animal welfare, promoting circularity in the livestock industry. In this context, animal monitoring plays a key role in livestock management, not only to ensure their well-being but also to preserve the balance of the territory. In particular, early detection of oestrus events is one of the crucial elements in livestock monitoring. This study presents the development and on-farm validation of a low-power oestrus detection system for dairy cows, based on stand-alone smart pedometers (SASPs) connected through a Low-Power Wide-Area Network (LPWAN). The system implements an upgradeable, threshold-based algorithm that analyzes cow motor activity using a 24 h moving-mean approach and three behavioral indicators related to oestrus expression. Data are processed on board and transmitted to a cloud platform for visualization through a farmer-oriented WebApp, without requiring any fixed installation in the barn. The system was tested on a commercial free-stall dairy farm over three experimental campaigns (2021–2023). Oestrus events were validated through farmer visual observation and milk progesterone analysis, used as the reference method. A total of 22 confirmed oestrus events were analyzed. The system achieved a detection rate of 72.7% for certain oestrus events and 86.4% when including probable detections, with a mean oestrus duration of 18.1 ± 2.5 h, consistent with values reported in the literature. The proposed solution demonstrates the feasibility of a transparent, low-computational-cost oestrus detection approach compatible with LPWAN constraints. Its plug-and-play design, reduced infrastructure requirements, and upgradable firmware, although not able to self-update, limiting its potential compared to the machine learning-based methods present in the literature, make it suitable for practical adoption, particularly in farms where conventional connectivity and high-cost commercial systems are limiting factors.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Animal Behavior and Welfare Studies · Milk Quality and Mastitis in Dairy Cows · Reproductive Physiology in Livestock

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Veterinary Sciences, University of Messina, Viale G. Palatucci s.n., 98168 Messina, Italy

Consorzio per la Ricerca nel Settore della Filiera Lattiero-Casearia e dell’Agroalimentare (CoRFiLaC), 97100 Ragusa, Italy

Dipartimento di Agricoltura, Alimentazione e Ambiente, University of Catania, 95123 Catania, Italy

Funding

European Union Next-GenerationEU

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