Application of precision livestock farming: Challenges and opportunities

This systematic literature review aims to evaluate advancements in sensor systems for detecting health conditions in dairy cows especially on mastitis, fertility, locomotion, and metabolic disorders. Relevant articles published between 2014 and 2024 were identified from Scopus. Each article was categorized by health condition and assigned to one of four development levels: sensor technique (level I), data interpretation (Level II), integration of information ( Level III), and decision making (Level IV). Relevant information from the articles was systematically reviewed and discussed. We identified 132 articles published in the past 10 years, describing a total of 151 sensor systems. Most sensor systems were aimed at mastitis and reproduction, followed by locomotion and metabolic disorders. The far majority of the articles were at level II (data interpretation) presenting research on (novel) algorithms to detect disease. A large number of different statistical or machine-learning or deep-learning models were described and evaluated, amongst others random forests. Level II systems applied statistical analysis or machine-learning/deep-learning models (e.g., random forests, You Only Look Once (YOLO), Support Vector Machine (SVM), or Convolutional Neural Network (CNN)). These algorithms did use a wide range of sensor data. Only few articles aimed at level III research, integration of information and decision support. This review highlights the need for sensor systems research to be driven by real-world requirements for on-farm decision making. To move from proof-of-concept toward practical, future research must integrate sensor outputs with herd records and financial models, validate systems across multiple farms and at higher data frequencies, and embed economic evaluation alongside sensitivity and specificity metrics. Addressing these technical, integration, and economic challenges is essential before sensor systems can fully support automated, value-driven health management on commercial dairy farms.

Authors

Institutions

Publication Details

Journal
Animal Bioscience
Published
2026-03-11
DOI
https://doi.org/10.5713/ab.250895
Primary Topic
Animal Behavior and Welfare Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Application of precision livestock farming: Challenges and opportunities

H. Hogeveen, Muhammad Ikhsan Sani, Mariska van der Voort, Bedir Tekinerdogan
Animal Bioscience
Animal Behavior and Welfare Studies
article

Application of precision livestock farming: Challenges and opportunities

H. Hogeveen, Muhammad Ikhsan Sani, Mariska van der Voort, Bedir Tekinerdogan
article en

Abstract

This systematic literature review aims to evaluate advancements in sensor systems for detecting health conditions in dairy cows especially on mastitis, fertility, locomotion, and metabolic disorders. Relevant articles published between 2014 and 2024 were identified from Scopus. Each article was categorized by health condition and assigned to one of four development levels: sensor technique (level I), data interpretation (Level II), integration of information ( Level III), and decision making (Level IV). Relevant information from the articles was systematically reviewed and discussed. We identified 132 articles published in the past 10 years, describing a total of 151 sensor systems. Most sensor systems were aimed at mastitis and reproduction, followed by locomotion and metabolic disorders. The far majority of the articles were at level II (data interpretation) presenting research on (novel) algorithms to detect disease. A large number of different statistical or machine-learning or deep-learning models were described and evaluated, amongst others random forests. Level II systems applied statistical analysis or machine-learning/deep-learning models (e.g., random forests, You Only Look Once (YOLO), Support Vector Machine (SVM), or Convolutional Neural Network (CNN)). These algorithms did use a wide range of sensor data. Only few articles aimed at level III research, integration of information and decision support. This review highlights the need for sensor systems research to be driven by real-world requirements for on-farm decision making. To move from proof-of-concept toward practical, future research must integrate sensor outputs with herd records and financial models, validate systems across multiple farms and at higher data frequencies, and embed economic evaluation alongside sensitivity and specificity metrics. Addressing these technical, integration, and economic challenges is essential before sensor systems can fully support automated, value-driven health management on commercial dairy farms.

Animal Bioscience
Wageningen University & Research (NL)
Openalex Percentile: Top 14%
Animal Behavior and Welfare Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.