Next Article in Journal
A High-Level Control Algorithm Based on sEMG Signalling for an Elbow Joint SMA Exoskeleton
Next Article in Special Issue
fPADnet: Small and Efficient Convolutional Neural Network for Presentation Attack Detection
Previous Article in Journal
High-Efficiency Output Pressure Performance Using Capacitive Micromachined Ultrasonic Transducers with Substrate-Embedded Springs
Previous Article in Special Issue
Staged Incentive and Punishment Mechanism for Mobile Crowd Sensing
 
Search for Articles:
Title / Keyword
Author / Affiliation
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
Journals
Sensors
Volume 18
Issue 8
10.3390/s18082521
sensors-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share announcement question_answer thumb_up
...
textsms
...
Article

A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors

by
Jake Cowton
1,2,*,
Ilias Kyriazakis
2,
Thomas Plötz
3,† and
Jaume Bacardit
1
1
Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
2
Agriculture, School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
3
Open Lab, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
*
Author to whom correspondence should be addressed.
†
Current address: School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA 30318, USA.
Sensors 2018, 18(8), 2521; https://doi.org/10.3390/s18082521
Received: 22 June 2018 / Revised: 25 July 2018 / Accepted: 31 July 2018 / Published: 2 August 2018
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Deep Learning for Sensor Information Fusion)
View Full-Text Download PDF
Browse Figures

Abstract

We designed and evaluated an assumption-free, deep learning-based methodology for animal health monitoring, specifically for the early detection of respiratory disease in growing pigs based on environmental sensor data. Two recurrent neural networks (RNNs), each comprising gated recurrent units (GRUs), were used to create an autoencoder (GRU-AE) into which environmental data, collected from a variety of sensors, was processed to detect anomalies. An autoencoder is a type of network trained to reconstruct the patterns it is fed as input. By training the GRU-AE using environmental data that did not lead to an occurrence of respiratory disease, data that did not fit the pattern of “healthy environmental data” had a greater reconstruction error. All reconstruction errors were labelled as either normal or anomalous using threshold-based anomaly detection optimised with particle swarm optimisation (PSO), from which alerts are raised. The results from the GRU-AE method outperformed state-of-the-art techniques, raising alerts when such predictions deviated from the actual observations. The results show that a change in the environment can result in occurrences of pigs showing symptoms of respiratory disease within 1–7 days, meaning that there is a period of time during which their keepers can act to mitigate the negative effect of respiratory diseases, such as porcine reproductive and respiratory syndrome (PRRS), a common and destructive disease endemic in pigs. View Full-Text
Keywords: anomaly detection; deep learning; sensors; GRU; pig; health; disease anomaly detection; deep learning; sensors; GRU; pig; health; disease
►▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
SciFeed

Share and Cite

MDPI and ACS Style

Cowton, J.; Kyriazakis, I.; Plötz, T.; Bacardit, J. A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors. Sensors 2018, 18, 2521. https://doi.org/10.3390/s18082521

AMA Style

Cowton J, Kyriazakis I, Plötz T, Bacardit J. A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors. Sensors. 2018; 18(8):2521. https://doi.org/10.3390/s18082521

Chicago/Turabian Style

Cowton, Jake, Ilias Kyriazakis, Thomas Plötz, and Jaume Bacardit. 2018. "A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors" Sensors 18, no. 8: 2521. https://doi.org/10.3390/s18082521

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Map by Country/Region

1
Back to TopTop