Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

The RcMYB21 transcription factor activates flavonol biosynthesis by targeting RcFLS in Chinese raspberry.

BMC plant biology·2026

Same author

Pregnancy-related adverse events and congenital disorders associated with aspirin: A real-world pharmacovigilance analysis of the FDA adverse event reporting system.

Medicine·2026

Same author

Cross-species hybrid vesicles enable synergistic immunochemotherapy through enhanced targeted drug delivery and immune activation.

Journal of nanobiotechnology·2026

Same author

Multifunctional copper complex-mediated self-blocking plasmid delivery of PD-L1 for tumor chemoimmunotherapy.

Colloids and surfaces. B, Biointerfaces·2026

Same author

Lower limb joint kinematics in older adults during steady-state walking in the home setting.

Gait & posture·2026

Same author

Analysis of Photothermal Conversion Behaviors in Graphene-Polymer Nanocomposites.

Polymers·2026

Search research articles

Related Experiment Video

Updated: Jan 16, 2026

The Use of an Automated System GreenFeed to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

The Use of an Automated System GreenFeed to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

Published on: September 7, 2015

Real-Time Auto-Monitoring of Livestock: Quantitative Framework and Challenges.

Sarah Brocklehurst¹, Zhou Fang¹, Adam Butler¹

¹Biomathematics and Statistics Scotland (BioSS), Edinburgh EH9 3FD, UK.

Sensors (Basel, Switzerland)

|September 27, 2025

Summary

This summary is machine-generated.

Automated sensors generate valuable data for real-time decision-making in livestock farming. Further development and fair comparison of quantitative methods are needed for effective application on farms.

Keywords:

decision-making latent variable modelling livestock machine learning neural networks prediction prediction validation sensors statistical modelling time series

More Related Videos

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

Related Experiment Videos

Last Updated: Jan 16, 2026

The Use of an Automated System GreenFeed to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

The Use of an Automated System GreenFeed to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

Published on: September 7, 2015

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

Area of Science:

Agricultural technology
Data science in animal agriculture

Background:

Automated sensor use is rapidly increasing across diverse fields, including agriculture.
Livestock farming increasingly utilizes sensor data for monitoring and decision-making.
Quantitative methods like machine learning are proposed for real-time data analysis.

Purpose of the Study:

To highlight the need for developing and validating quantitative methods for real-time decision-making in livestock farming.
To address challenges in applying quantitative approaches dynamically on farms.
To outline approaches for fair and robust evaluation of method performance.

Main Methods:

Review of existing literature on quantitative methods for sensor data analysis.
Discussion of practical challenges in real-time, dynamic application of methods on farms.
Proposal of frameworks for comparative performance evaluation of algorithms.

Main Results:

Current literature lacks sufficient development and validation of quantitative methods for practical farm application.
Significant challenges exist in implementing real-time, dynamic quantitative analysis on livestock farms.
There is a critical need for standardized, fair comparisons of method performance.

Conclusions:

Further research is essential to develop and validate quantitative methods for sensor data in livestock farming.
Practical feasibility and robust performance evaluation are key to successful implementation.
Addressing these gaps will enhance the utility of empirical data for optimizing farm management.