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Animals
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Deep Learning and Animal Welfare
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Deep Learning and Animal Welfare

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

Deadline for manuscript submissions: closed (10 July 2021) | Viewed by 21515

Special Issue Editors

Prof. Dr. Christa Thöne-Reineke

E-Mail Website
Guest Editor
Freie Universität Berlin, Institute of Animal Welfare, Animal Behavior and Laboratory Animal Science, Berlin, Germany
Interests: animal welfare; animal behavior; laboratory animals and education
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lars Lewejohann

E-Mail Website
Guest Editor
Freie Universität Berlin, Institute of Animal Welfare, Animal Behavior and Laboratory Animal Science, Berlin, Germany
Interests: animal welfare; refinement; animal behavior; laboratory animals; behavioral neuroscience; science of intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Animal Welfare and Deep Learning is a new interdisciplinary area of research in which modern methods from the field of artificial intelligence are used to create new opportunities for scientifically assessing and evaluating animal welfare. The acquisition and evaluation of large data sets from animal physiology, animal behavior, or clinical data can be significantly facilitated and improved with these new methods. This opens up completely new possibilities for all areas where animals are used, including farm animals, wild animals, pets, and experimental animals. However, it is of great importance to pay attention to the validity and transparency of these methods, and to point out possible limitations. Furthermore, the explanatory power of the methods towards society must be considered in order to achieve acceptance. For this purpose, ethical reflection is indispensable.

This Special Issue is interested in all areas in which this interdisciplinary work takes place in the context of animal welfare. Both reviews and original research papers are welcome. The Issue is interested in reports on the special requirements for single species as well as in comprehensive general findings. Strategies and concepts for the assessment of animal welfare, case studies implementing practical improvement strategies of Deep Learning methods, and ethical considerations are welcome.

Prof. Dr. Christa Thöne-Reineke
Prof. Dr. Lars Lewejohann
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

  • deep learning
  • KI
  • animal welfare
  • ethics
  • big data

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Published Papers (3 papers)

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Research

14 pages, 2696 KiB  
Article
Learn to Train: Improving Training Data for a Neural Network to Detect Pecking Injuries in Turkeys
by Nina Volkmann, Johannes Brünger, Jenny Stracke, Claudius Zelenka, Reinhard Koch, Nicole Kemper and Birgit Spindler
Animals 2021, 11(9), 2655; https://doi.org/10.3390/ani11092655 - 9 Sep 2021
Cited by 7 | Viewed by 2859
Abstract
This study aimed to develop a camera-based system using artificial intelligence for automated detection of pecking injuries in turkeys. Videos were recorded and split into individual images for further processing. Using specifically developed software, the injuries visible on these images were marked by [...] Read more.
This study aimed to develop a camera-based system using artificial intelligence for automated detection of pecking injuries in turkeys. Videos were recorded and split into individual images for further processing. Using specifically developed software, the injuries visible on these images were marked by humans, and a neural network was trained with these annotations. Due to unacceptable agreement between the annotations of humans and the network, several work steps were initiated to improve the training data. First, a costly work step was used to create high-quality annotations (HQA) for which multiple observers evaluated already annotated injuries. Therefore, each labeled detection had to be validated by three observers before it was saved as “finished”, and for each image, all detections had to be verified three times. Then, a network was trained with these HQA to assist observers in annotating more data. Finally, the benefit of the work step generating HQA was tested, and it was shown that the value of the agreement between the annotations of humans and the network could be doubled. Although the system is not yet capable of ensuring adequate detection of pecking injuries, the study demonstrated the importance of such validation steps in order to obtain good training data. Full article
(This article belongs to the Special Issue Deep Learning and Animal Welfare)
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18 pages, 4738 KiB  
Article
Action Recognition Using a Spatial-Temporal Network for Wild Felines
by Liqi Feng, Yaqin Zhao, Yichao Sun, Wenxuan Zhao and Jiaxi Tang
Animals 2021, 11(2), 485; https://doi.org/10.3390/ani11020485 - 12 Feb 2021
Cited by 19 | Viewed by 4515
Abstract
Behavior analysis of wild felines has significance for the protection of a grassland ecological environment. Compared with human action recognition, fewer researchers have focused on feline behavior analysis. This paper proposes a novel two-stream architecture that incorporates spatial and temporal networks for wild [...] Read more.
Behavior analysis of wild felines has significance for the protection of a grassland ecological environment. Compared with human action recognition, fewer researchers have focused on feline behavior analysis. This paper proposes a novel two-stream architecture that incorporates spatial and temporal networks for wild feline action recognition. The spatial portion outlines the object region extracted by Mask region-based convolutional neural network (R-CNN) and builds a Tiny Visual Geometry Group (VGG) network for static action recognition. Compared with VGG16, the Tiny VGG network can reduce the number of network parameters and avoid overfitting. The temporal part presents a novel skeleton-based action recognition model based on the bending angle fluctuation amplitude of the knee joints in a video clip. Due to its temporal features, the model can effectively distinguish between different upright actions, such as standing, ambling, and galloping, particularly when the felines are occluded by objects such as plants, fallen trees, and so on. The experimental results showed that the proposed two-stream network model can effectively outline the wild feline targets in captured images and can significantly improve the performance of wild feline action recognition due to its spatial and temporal features. Full article
(This article belongs to the Special Issue Deep Learning and Animal Welfare)
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24 pages, 63692 KiB  
Article
Automated Bird Counting with Deep Learning for Regional Bird Distribution Mapping
by Hüseyin Gökhan Akçay, Bekir Kabasakal, Duygugül Aksu, Nusret Demir, Melih Öz and Ali Erdoğan
Animals 2020, 10(7), 1207; https://doi.org/10.3390/ani10071207 - 16 Jul 2020
Cited by 50 | Viewed by 12488
Abstract
A challenging problem in the field of avian ecology is deriving information on bird population movement trends. This necessitates the regular counting of birds which is usually not an easily-achievable task. A promising attempt towards solving the bird counting problem in a more [...] Read more.
A challenging problem in the field of avian ecology is deriving information on bird population movement trends. This necessitates the regular counting of birds which is usually not an easily-achievable task. A promising attempt towards solving the bird counting problem in a more consistent and fast way is to predict the number of birds in different regions from their photos. For this purpose, we exploit the ability of computers to learn from past data through deep learning which has been a leading sub-field of AI for image understanding. Our data source is a collection of on-ground photos taken during our long run of birding activity. We employ several state-of-the-art generic object-detection algorithms to learn to detect birds, each being a member of one of the 38 identified species, in natural scenes. The experiments revealed that computer-aided counting outperformed the manual counting with respect to both accuracy and time. As a real-world application of image-based bird counting, we prepared the spatial bird order distribution and species diversity maps of Turkey by utilizing the geographic information system (GIS) technology. Our results suggested that deep learning can assist humans in bird monitoring activities and increase citizen scientists’ participation in large-scale bird surveys. Full article
(This article belongs to the Special Issue Deep Learning and Animal Welfare)
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