Livestock feeding behaviour: A review on automated systems for ruminant monitoring

Feeding behaviour; Machine learning; Precision livestock farming; Review; Sensor data; 'current; Animal agriculture; Animal husbandry; Automated monitoring systems; Automated systems; Feeding behavior; Machine-learning; Research areas; Sensors data; Control and Systems Engineering; Food Science; Agronomy and Crop Science; Soil Science

Abstract :

[en] Livestock feeding behaviour is an influential research area in animal husbandry and agriculture. In recent years, there has been a growing interest in automated systems for monitoring the behaviour of ruminants. Current automated monitoring systems mainly use motion, acoustic, pressure and image sensors to collect and analyse patterns related to ingestive behaviour, foraging activities and daily intake. The performance evaluation of existing methods is a complex task and direct comparisons between studies is difficult. Several factors prevent a direct comparison, starting from the diversity of data and performance metrics used in the experiments. This review on the analysis of the feeding behaviour of ruminants emphasise the relationship between sensing methodologies, signal processing, and computational intelligence methods. It assesses the main sensing methodologies and the main techniques to analyse the signals associated with feeding behaviour, evaluating their use in different settings and situations. It also highlights the potential of the valuable information provided by automated monitoring systems to expand knowledge in the field, positively impacting production systems and research. The paper closes by discussing future engineering challenges and opportunities in livestock feeding behaviour monitoring.

Disciplines :

Animal production & animal husbandry

Author, co-author :

Chelotti, José ; Université de Liège - ULiège > Département GxABT > Animal Sciences (AS) ; Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Argentina

Martinez-Rau, Luciano S. ; Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Argentina ; Department of Computer and Electrical Engineering, Mid Sweden University, Sundsvall, Sweden

Ferrero, Mariano; Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Argentina

Vignolo, Leandro D. ; Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Argentina

Galli, Julio R. ; Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, Argentina ; Instituto de Investigaciones en Ciencias Agrarias de Rosario, IICAR, UNR-CONICET, Argentina

Planisich, Alejandra M. ; Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, Argentina

Rufiner, H. Leonardo ; Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Argentina ; Laboratorio de Cibernética, Facultad de Ingeniería, Universidad Nacional de Entre Ríos, Argentina

Giovanini, Leonardo L.; Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Argentina

Language :

English

Title :

Livestock feeding behaviour: A review on automated systems for ruminant monitoring

Publication date :

October 2024

Journal title :

Biosystems Engineering

ISSN :

1537-5110

eISSN :

1537-5129

Publisher :

Academic Press

Volume :

246

Pages :

150 - 177

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://api.elsevier.com/content/article/PII:S1537511024001752?httpAccept=text/xml

Funders :

UNL - Universidad Nacional del Litoral
UNR - Universidad Nacional de Rosario
ASaCTeI - Agencia Santafesina de Ciencia, Tecnología e Innovación
CONICET - Consejo Nacional de Investigaciones Científicas y Técnicas
Knowledge Foundation

Funding text :

This work has been funded by Universidad Nacional del Litoral [CAID 50620190100080LI, 50620190100151LI]; Universidad Nacional de Rosario [projects 2013-AGR216, 2016-AGR266, 80020180300053UR]; Agencia Santafesina de Ciencia, Tecnolog\u00EDa e Innovaci\u00F3n (ASACTEI) [project IO-2018\u201300082]; Consejo Nacional de Investigaciones Cient\u00EDficas y T\u00E9cnicas (CONICET) [project 2017-PUE sinc(i)]; Knowledge Foundation [grant number NIIT 20180170]. Support program for National Universities 2023. FONDAGRO. Secretary of Agriculture, Livestock and Fisheries of the Argentine Nation. The authors would like to thank the dedication and perceptive help of Campo Experimental J. Villarino Dairy Farm staff for their assistance and support during the completion of this study.

Available on ORBi :

since 21 August 2024

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