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Sensors 2018, 18(8), 2521; https://doi.org/10.3390/s18082521

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

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
Current address: School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA 30318, USA.
*
Author to whom correspondence should be addressed.
Received: 22 June 2018 / Revised: 25 July 2018 / Accepted: 31 July 2018 / Published: 2 August 2018
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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
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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.

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