ArticleAnimals : an open access journal from MDPI2020
Remotely Sensed Imagery for Early Detection of Respiratory Disease in Pigs: A Pilot Study.
Article in Animals : an open access journal from MDPI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 40 citations in OpenAlex.
- Pilot Room-Level Acoustic and Physiological Monitoring of Respiratory Disturbance in Pigs Following ExperimentalVeterinary sciences · 2026Article
- Revolutionizing pig farming: Japan's technological innovations and environmental strategies for sustainability.Journal of advanced veterinary and animal research · 2025Article
- Article
- Thermal imaging and computer vision technologies for the enhancement of pig husbandry: a review.Journal of animal science and technology · 2024Review
- Artificial Intelligence for Automatic Monitoring of Respiratory Health Conditions in Smart Swine Farming.Animals : an open access journal from MDPI · 2023Review
- Evaluation of Precision Livestock Technology and Human Scoring of Nursery Pigs in a Controlled Immune Challenge Experiment.Animals : an open access journal from MDPI · 2023Article
- The impact of stress and anesthesia on animal models of infectious disease.Frontiers in veterinary science · 2023Review
- Livestock Identification Using Deep Learning for Traceability.Sensors (Basel, Switzerland) · 2022Article
- The Research Progress of Vision-Based Artificial Intelligence in Smart Pig Farming.Sensors (Basel, Switzerland) · 2022Review
- Thermography for disease detection in livestock: A scoping review.Frontiers in veterinary science · 2022Article
- Porcine respiratory disease complex: Dynamics of polymicrobial infections and management strategies after the introduction of the African swine fever.Frontiers in veterinary science · 2022Review
- Thermoregulation mechanisms and perspectives for validating thermal windows in pigs with hypothermia and hyperthermia: An overview.Frontiers in veterinary science · 2022Review
- Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence.Sensors (Basel, Switzerland) · 2021Article
- Dynamic Variations in Infrared Skin Temperature of Weaned Pigs Experimentally Inoculated with the African Swine Fever Virus: A Pilot Study.Veterinary sciences · 2021Article
- Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Respiratory diseases are a major problem in the pig industry worldwide. Due to the impact of these diseases, the early identification of infected herds is essential. Computer vision technology, using RGB (red, green and blue) and thermal infrared imagery, can assist the early detection of changes in animal physiology related to these and other diseases. This pilot study aimed to identify whether these techniques are a useful tool to detect early changes of eye and ear-base temperature, heart rate and respiration rate in pigs that were challenged with
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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.