ArticleSensors (Basel, Switzerland)2018
A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 65 citations in OpenAlex.
- Review of applications of deep learning in veterinary diagnostics and animal health.Frontiers in veterinary science · 2025Pooled it
- XGBoost-based early warning framework for farm-level PED risk using meteorological and herd management variables.iScience · 2026Article
- Machine Learning-Based Human Detection Using Active Non-Line-of-Sight Laser Sensing.Sensors (Basel, Switzerland) · 2026Article
- From machine learning to digital twin integration for livestock production and research.Frontiers in veterinary science · 2026Review
- Research on a Novel Unsupervised-Learning-Based Pipeline Leak Detection Method Based on Temporal Kolmogorov-Arnold Network with Autoencoder Integration.Sensors (Basel, Switzerland) · 2025Article
- Leveraging productivity indicators for anomaly detection in swine breeding herds with unsupervised learning.Frontiers in veterinary science · 2025Article
- Meat quality and safety issues during high temperatures and cutting-edge technologies to mitigate the scenario.Journal of animal science and technology · 2024Review
- Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter.Sensors (Basel, Switzerland) · 2023Article
- Farmers' Perspectives of the Benefits and Risks in Precision Livestock Farming in the EU Pig and Poultry Sectors.Animals : an open access journal from MDPI · 2023Review
- GRU-Based Denoising Autoencoder for Detection and Clustering of Unknown Single and Concurrent Faults during System Integration Testing of Automotive Software Systems.Sensors (Basel, Switzerland) · 2023Article
- Innovative applications of artificial intelligence in zoonotic disease management.Science in One Health · 2023Review
- ASAS-NANP symposium: mathematical modeling in animal nutrition: limitations and potential next steps for modeling and modelers in the animal sciences.Journal of animal science · 2022Review
- Unsupervised Deep Anomaly Detection for Medical Images Using an Improved Adversarial Autoencoder.Journal of digital imaging · 2022Article
- Abnormal ECG detection based on an adversarial autoencoder.Frontiers in physiology · 2022Article
- Article
- The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic.Information systems frontiers : a journal of research and innovation · 2021Article
- Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks.JMIR mHealth and uHealth · 2020Article
- A critical reflection on intensive pork production with an emphasis on animal health and welfare.Journal of animal science · 2020Article
- Combination of Sensor Data and Health Monitoring for Early Detection of Subclinical Ketosis in Dairy Cows.Sensors (Basel, Switzerland) · 2020Article
- A Novel Framework with High Diagnostic Sensitivity for Lung Cancer Detection by Electronic Nose.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.