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Animals
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Automation, Big Data, and New Technologies in Animal Research
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Automation, Big Data, and New Technologies in Animal Research

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal System and Management".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 22358

Special Issue Editors

Dr. Nuno Franco

E-Mail Website
Guest Editor
i3S—Institute of Health Research and Innovation, University of Porto, 4169-007 Porto, Portugal
Interests: animal welfare; animal ethics; laboratory animal science; experimental design
Dr. Paulin Jirkof

E-Mail Website
Guest Editor
Department of Animals welfare, Zurich University, CH-8057 Zurich, Switzerland
Interests: animal welfare; 3Rs; animal behavior; pain assessment; severity assessment; analgesia; laboratory animals
Special Issues, Collections and Topics in MDPI journals
Dr. Katharina Hohlbaum

E-Mail Website
Guest Editor
Institute of Animal Welfare, Animal Behavior, and Laboratory Animal Science, Department of Veterinary Medicine, Freie Universität Berlin, 14163 Berlin, Germany
Interests: animal welfare; animal behavior; laboratory animals; refinement; severity assessment; facial expression analysis; intelligence

Special Issue Information

Automation technology offers a wide range of possibilities for data collection in research on animals. One of its most important advantages is the possibility of continuous, round-the-clock data collection with minimal disturbance of the animals, with applications that go from biomedical research to precision livestock farming and wildlife research. The automated collection and/or analysis of large datasets gathered from sensors or cameras can offer a comprehensive picture of animal behaviour and physiology, and its interactions with the environment, while avoiding current caveats of operator-based methods, which are typically more labour-demanding, time-consuming, cost-inefficient, and bias-prone. Moreover, automated analysis allows identifying more details that would otherwise remain uncovered.

This Special Issue is interested in both reviews and original research papers on new technological approaches in research on animals, with particular emphasis on automation technology, artificial intelligence, and big data. We invite reports on the development or application of these technologies in laboratory, farm, companion, and wild animals. Examples include the application of new technological approaches to identify behavioural and physiological indicators of positive and negative welfare, as well as monitoring and early signalling of health problems to allow prompt intervention (from timely treatment of common diseases in livestock species to implementation of humane endpoints in animal models of disease).

Dr. Paulin Jirkof
Dr. Nuno Franco
Dr. Katharina Hohlbaum
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Animals is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • animal research
  • automation
  • technology
  • big data
  • precision livestock farming

Published Papers (5 papers)

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Research

Jump to: Review

22 pages, 5475 KiB  
Article
Non-Invasive Assessment of Mild Stress-Induced Hyperthermia by Infrared Thermography in Laboratory Mice
by Urša Blenkuš, Ana Filipa Gerós, Cristiana Carpinteiro, Paulo de Castro Aguiar, I. Anna S. Olsson and Nuno Henrique Franco
Animals 2022, 12(2), 177; https://doi.org/10.3390/ani12020177 - 12 Jan 2022
Cited by 7 | Viewed by 3708
Abstract
Stress-induced hyperthermia (SIH) is a physiological response to acute stressors in mammals, shown as an increase in core body temperature, with redirection of blood flow from the periphery to vital organs. Typical temperature assessment methods for rodents are invasive and can themselves elicit [...] Read more.
Stress-induced hyperthermia (SIH) is a physiological response to acute stressors in mammals, shown as an increase in core body temperature, with redirection of blood flow from the periphery to vital organs. Typical temperature assessment methods for rodents are invasive and can themselves elicit SIH, affecting the readout. Infrared thermography (IRT) is a promising non-invasive alternative, if shown to accurately identify and quantify SIH. We used in-house developed software ThermoLabAnimal 2.0 to automatically detect and segment different body regions, to assess mean body (Tbody) and mean tail (Ttail) surface temperatures by IRT, along with temperature (Tsc) assessed by reading of subcutaneously implanted PIT-tags, during handling-induced stress of pair-housed C57BL/6J and BALB/cByJ mice of both sexes (N = 68). SIH was assessed during 10 days of daily handling (DH) performed twice per day, weekly voluntary interaction tests (VIT) and an elevated plus maze (EPM) at the end. To assess the discrimination value of IRT, we compared SIH between tail-picked and tunnel-handled animals, and between mice receiving an anxiolytic drug or vehicle prior to the EPM. During a 30 to 60 second stress exposure, Tsc and Tbody increased significantly (p < 0.001), while Ttail (p < 0.01) decreased. We did not find handling-related differences. Within each cage, mice tested last consistently showed significantly higher (p < 0.001) Tsc and Tbody and lower (p < 0.001) Ttail than mice tested first, possibly due to higher anticipatory stress in the latter. Diazepam-treated mice showed lower Tbody and Tsc, consistent with reduced anxiety. In conclusion, our results suggest that IRT can identify and quantify stress in mice, either as a stand-alone parameter or complementary to other methods. Full article
(This article belongs to the Special Issue Automation, Big Data, and New Technologies in Animal Research)
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13 pages, 3679 KiB  
Article
An Improved Approach to Automated Measurement of Body Condition Score in Dairy Cows Using a Three-Dimensional Camera System
by Rodrigo I. Albornoz, Khageswor Giri, Murray C. Hannah and William J. Wales
Animals 2022, 12(1), 72; https://doi.org/10.3390/ani12010072 - 29 Dec 2021
Cited by 10 | Viewed by 3317
Abstract
Body condition scoring is a valuable tool used to assess the changes in subcutaneous tissue reserves of dairy cows throughout the lactation resulting from changes to management or nutritional interventions. A subjective visual method is typically used to assign a body condition score [...] Read more.
Body condition scoring is a valuable tool used to assess the changes in subcutaneous tissue reserves of dairy cows throughout the lactation resulting from changes to management or nutritional interventions. A subjective visual method is typically used to assign a body condition score (BCS) to a cow following a standardized scale, but this method is subject to operator bias and is labor intensive, limiting the number of animals that can be scored and frequency of measurement. An automated three-dimensional body condition scoring camera system is commercially available (DeLaval Body Condition Scoring, BCS DeLaval International AB, Tumba, Sweden), but the reliability of the BCS data for research applications is still unknown, as the system’s sensitivity to change in BCS over time within cows has yet to be investigated. The objective of this study was to evaluate the suitability of an automated body condition scoring system for dairy cows for research applications as an alternative to visual body condition scoring. Thirty-two multiparous Holstein-Friesian cows (9 ± 6.8 days in milk) were body condition scored visually by three trained staff weekly and automatically twice each day by the camera for at least 7 consecutive weeks. Measurements were performed in early lactation, when the greatest differences in BCS of a cow over the lactation are normally present, and changes in BCS occur rapidly compared with later stages, allowing for detectable changes in a short timeframe by each method. Two data sets were obtained from the automatic body condition scoring camera: (1) raw daily BCS camera values and (2) a refined data set obtained from the raw daily BCS camera data by fitting a robust smooth loess function to identify and remove outliers. Agreement, precision, and sensitivity properties of the three data sets (visual, raw, and refined camera BCS) were compared in terms of the weekly average for each cow. Sensitivity was estimated as the ratio of response to precision, providing an objective performance criterion for independent comparison of methods. The camera body condition scoring method, using raw or refined camera data, performed better on this criterion compared with the visual method. Sensitivities of the raw BCS camera method, the refined BCS camera method, and the visual BCS method for changes in weekly mean score were 3.6, 6.2, and 1.7, respectively. To detect a change in BCS of an animal, assuming a decline of about 0.2 BCS (1–8 scale) per month, as was observed on average in this experiment, it would take around 44 days with the visual method, 21 days with the raw camera method, or 12 days with the refined camera method. This represents an increased capacity of both camera methods to detect changes in BCS over time compared with the visual method, which improved further when raw camera data were refined as per our proposed method. We recommend the use of the proposed refinement of the camera’s daily BCS data for research applications. Full article
(This article belongs to the Special Issue Automation, Big Data, and New Technologies in Animal Research)
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18 pages, 23005 KiB  
Article
Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification
by Jennifer Salau, Jan Henning Haas, Wolfgang Junge and Georg Thaller
Animals 2021, 11(1), 50; https://doi.org/10.3390/ani11010050 - 29 Dec 2020
Cited by 8 | Viewed by 2544
Abstract
Machine learning methods have become increasingly important in animal science, and the success of an automated application using machine learning often depends on the right choice of method for the respective problem and data set. The recognition of objects in 3D data is [...] Read more.
Machine learning methods have become increasingly important in animal science, and the success of an automated application using machine learning often depends on the right choice of method for the respective problem and data set. The recognition of objects in 3D data is still a widely studied topic and especially challenging when it comes to the partition of objects into predefined segments. In this study, two machine learning approaches were utilized for the recognition of body parts of dairy cows from 3D point clouds, i.e., sets of data points in space. The low cost off-the-shelf depth sensor Microsoft Kinect V1 has been used in various studies related to dairy cows. The 3D data were gathered from a multi-Kinect recording unit which was designed to record Holstein Friesian cows from both sides in free walking from three different camera positions. For the determination of the body parts head, rump, back, legs and udder, five properties of the pixels in the depth maps (row index, column index, depth value, variance, mean curvature) were used as features in the training data set. For each camera positions, a k nearest neighbour classifier and a neural network were trained and compared afterwards. Both methods showed small Hamming losses (between 0.007 and 0.027 for k nearest neighbour (kNN) classification and between 0.045 and 0.079 for neural networks) and could be considered successful regarding the classification of pixel to body parts. However, the kNN classifier was superior, reaching overall accuracies 0.888 to 0.976 varying with the camera position. Precision and recall values associated with individual body parts ranged from 0.84 to 1 and from 0.83 to 1, respectively. Once trained, kNN classification is at runtime prone to higher costs in terms of computational time and memory compared to the neural networks. The cost vs. accuracy ratio for each methodology needs to be taken into account in the decision of which method should be implemented in the application. Full article
(This article belongs to the Special Issue Automation, Big Data, and New Technologies in Animal Research)
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Review

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20 pages, 1404 KiB  
Review
Intelligent Perception-Based Cattle Lameness Detection and Behaviour Recognition: A Review
by Yongliang Qiao, He Kong, Cameron Clark, Sabrina Lomax, Daobilige Su, Stuart Eiffert and Salah Sukkarieh
Animals 2021, 11(11), 3033; https://doi.org/10.3390/ani11113033 - 22 Oct 2021
Cited by 26 | Viewed by 5026
Abstract
The growing world population has increased the demand for animal-sourced protein. However, animal farming productivity is faced with challenges from traditional farming practices, socioeconomic status, and climate change. In recent years, smart sensors, big data, and deep learning have been applied to animal [...] Read more.
The growing world population has increased the demand for animal-sourced protein. However, animal farming productivity is faced with challenges from traditional farming practices, socioeconomic status, and climate change. In recent years, smart sensors, big data, and deep learning have been applied to animal welfare measurement and livestock farming applications, including behaviour recognition and health monitoring. In order to facilitate research in this area, this review summarises and analyses some main techniques used in smart livestock farming, focusing on those related to cattle lameness detection and behaviour recognition. In this study, more than 100 relevant papers on cattle lameness detection and behaviour recognition have been evaluated and discussed. Based on a review and a comparison of recent technologies and methods, we anticipate that intelligent perception for cattle behaviour and welfare monitoring will develop towards standardisation, a larger scale, and intelligence, combined with Internet of things (IoT) and deep learning technologies. In addition, the key challenges and opportunities of future research are also highlighted and discussed. Full article
(This article belongs to the Special Issue Automation, Big Data, and New Technologies in Animal Research)
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31 pages, 1892 KiB  
Review
Infrared Thermography in the Study of Animals’ Emotional Responses: A Critical Review
by Tiziano Travain and Paola Valsecchi
Animals 2021, 11(9), 2510; https://doi.org/10.3390/ani11092510 - 26 Aug 2021
Cited by 33 | Viewed by 5500
Abstract
Whether animals have emotions was historically a long-lasting question but, today, nobody disputes that they do. However, how to assess them and how to guarantee animals their welfare have become important research topics in the last 20 years. Infrared thermography (IRT) is a [...] Read more.
Whether animals have emotions was historically a long-lasting question but, today, nobody disputes that they do. However, how to assess them and how to guarantee animals their welfare have become important research topics in the last 20 years. Infrared thermography (IRT) is a method to record the electromagnetic radiation emitted by bodies. It can indirectly assess sympathetic and parasympathetic activity via the modification of temperature of different body areas, caused by different phenomena such as stress-induced hyperthermia or variation in blood flow. Compared to other emotional activation assessment methods, IRT has the advantage of being noninvasive, allowing use without the risk of influencing animals’ behavior or physiological responses. This review describes general principles of IRT functioning, as well as its applications in studies regarding emotional reactions of domestic animals, with a brief section dedicated to the experiments on wildlife; it analyzes potentialities and possible flaws, confronting the results obtained in different taxa, and discusses further opportunities for IRT in studies about animal emotions. Full article
(This article belongs to the Special Issue Automation, Big Data, and New Technologies in Animal Research)
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