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Animals
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Artificial Intelligence (AI) Applied to Animal Health and Welfare
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Artificial Intelligence (AI) Applied to Animal Health and Welfare

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Veterinary Clinical Studies".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 14284

Special Issue Editor

Dr. Pablo Valdes-Donoso

E-Mail Website
Guest Editor
Faculty of Veterinary Medicine, Department of Clinical Sciences, University of Montreal, Saint-Hyacinthe, QC, Canada
Interests: animal health economics; One Health; food sustainability; artificial intelligence

Special Issue Information

Dear Colleagues,

Improvements in computing power, storage capacity, and use of big data have accelerated the research and development of Artificial intelligence (AI) in multiple disciplines. In animal sciences, AI incipient studies move forward in several areas. For example, the relationships between real-time sensor data and production coupled with animal health and welfare status are at the heart of current research on precision livestock farming. The elaboration of machine learning and deep learning models that could predict diseases, detect abnormal images, or identify disease-related sounds are part of current investigations that aim to support animal and public health professionals.

The aim of this Special Issue is to present recent research that fills gaps of current literature on how AI may improve animal health and welfare. Further, manuscripts that use novel machine learning approaches to address control strategies and impacts of animal diseases, as well as those that shed light on the intersection between AI and One Health, are welcome in this issue.

Dr. Pablo Valdes-Donoso
Guest Editor

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

  • artificial intelligence
  • machine learning
  • animal big data
  • animal health
  • animal welfare
  • veterinary sciences
  • animal disease prediction
  • image interpretation
  • disease-related sounds
  • one health

Published Papers (6 papers)

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Research

13 pages, 2627 KiB  
Article
Applying Sequential Pattern Mining to Investigate the Temporal Relationships between Commonly Occurring Internal Medicine Diseases and Intervals for the Risk of Concurrent Disease in Canine Patients
by Suk-Jun Lee and Jung-Hyun Kim
Animals 2023, 13(21), 3359; https://doi.org/10.3390/ani13213359 - 29 Oct 2023
Viewed by 909
Abstract
Sequential pattern mining (SPM) is a data mining technique used for identifying common association rules in multiple sequential datasets and patterns in ordered events. In this study, we aimed to identify the relationships between commonly occurring internal medicine diseases in canine patients. We [...] Read more.
Sequential pattern mining (SPM) is a data mining technique used for identifying common association rules in multiple sequential datasets and patterns in ordered events. In this study, we aimed to identify the relationships between commonly occurring internal medicine diseases in canine patients. We obtained medical records of dogs referred to the Konkuk University Veterinary Medicine Teaching Hospital. The data used for SPM included comorbidities and intervals between the diagnoses of internal medicine diseases. Additionally, we estimated the 3-year risk of developing an additional disease after the initial diagnosis of a commonly occurring veterinary internal medicine disease using logistic regression. We identified 547 canine patients diagnosed with ≥ 1 internal medicine disease. The SPM-based analysis assessed comorbidities and intervals for each of the five most common internal medical diseases, including hyperadrenocorticism, myxomatous mitral valve disease, canine atopic dermatitis, chronic kidney disease, and chronic pancreatitis. The highest values of the association rule were 3.01%, 6.02%, 3.9%, 4.1%, and 4.84%, and the shortest intervals were 1.64, 13.14, 5.37, 17.02, and 1.7 days, respectively. This study proposes that SPM is an effective technique for identifying common associations and temporal relationships between internal medicine diseases, and can be used to assess the probability of additional admission due to the development of the subsequent disease that may be diagnosed in canine patients. The results of this study will help veterinarians suggest appropriate preventive measures or other medical treatments for canine patients with medical conditions that have not yet been diagnosed, but are likely to develop in the short term. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) Applied to Animal Health and Welfare)
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24 pages, 12490 KiB  
Article
A Fine-Grained Image Classification Approach for Dog Feces Using MC-SCMNet under Complex Backgrounds
by Jinyu Liang, Weiwei Cai, Zhuonong Xu, Guoxiong Zhou, Johnny Li and Zuofu Xiang
Animals 2023, 13(10), 1660; https://doi.org/10.3390/ani13101660 - 17 May 2023
Cited by 1 | Viewed by 1963
Abstract
In a natural environment, factors such as weathering and sun exposure will degrade the characteristics of dog feces; disturbances such as decaying wood and dirt are likely to make false detections; the recognition distinctions between different kinds of feces are slight. To address [...] Read more.
In a natural environment, factors such as weathering and sun exposure will degrade the characteristics of dog feces; disturbances such as decaying wood and dirt are likely to make false detections; the recognition distinctions between different kinds of feces are slight. To address these issues, this paper proposes a fine-grained image classification approach for dog feces using MC-SCMNet under complex backgrounds. First, a multi-scale attention down-sampling module (MADM) is proposed. It carefully retrieves tiny feces feature information. Second, a coordinate location attention mechanism (CLAM) is proposed. It inhibits the entry of disturbance information into the network’s feature layer. Then, an SCM-Block containing MADM and CLAM is proposed. We utilized the block to construct a new backbone network to increase the efficiency of fecal feature fusion in dogs. Throughout the network, we decrease the number of parameters using depthwise separable convolution (DSC). In conclusion, MC-SCMNet outperforms all other models in terms of accuracy. On our self-built DFML dataset, it achieves an average identification accuracy of 88.27% and an F1 value of 88.91%. The results of the experiments demonstrate that it is more appropriate for dog fecal identification and maintains stable results even in complex backgrounds, which may be applied to dog gastrointestinal health checks. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) Applied to Animal Health and Welfare)
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9 pages, 962 KiB  
Article
Automated Wound Image Segmentation: Transfer Learning from Human to Pet via Active Semi-Supervised Learning
by Daniele Buschi, Nico Curti, Veronica Cola, Gianluca Carlini, Claudia Sala, Daniele Dall’Olio, Gastone Castellani, Elisa Pizzi, Sara Del Magno, Armando Foglia, Massimo Giunti, Luciano Pisoni and Enrico Giampieri
Animals 2023, 13(6), 956; https://doi.org/10.3390/ani13060956 - 07 Mar 2023
Cited by 1 | Viewed by 1948
Abstract
Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications regarding wound management for pets. Precise and efficient wound assessment is helpful to improve diagnosis and to increase the effectiveness [...] Read more.
Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications regarding wound management for pets. Precise and efficient wound assessment is helpful to improve diagnosis and to increase the effectiveness of treatment plans for chronic wounds. In this work, we introduced a novel pipeline for the segmentation of pet wound images. Starting from a model pre-trained on human-based wound images, we applied a combination of transfer learning (TL) and active semi-supervised learning (ASSL) to automatically label a large dataset. Additionally, we provided a guideline for future applications of TL+ASSL training strategy on image datasets. We compared the effectiveness of the proposed training strategy, monitoring the performance of an EfficientNet-b3 U-Net model against the lighter solution provided by a MobileNet-v2 U-Net model. We obtained 80% of correctly segmented images after five rounds of ASSL training. The EfficientNet-b3 U-Net model significantly outperformed the MobileNet-v2 one. We proved that the number of available samples is a key factor for the correct usage of ASSL training. The proposed approach is a viable solution to reduce the time required for the generation of a segmentation dataset. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) Applied to Animal Health and Welfare)
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16 pages, 2500 KiB  
Article
PolarBearVidID: A Video-Based Re-Identification Benchmark Dataset for Polar Bears
by Matthias Zuerl, Richard Dirauf, Franz Koeferl, Nils Steinlein, Jonas Sueskind, Dario Zanca, Ingrid Brehm, Lorenzo von Fersen and Bjoern Eskofier
Animals 2023, 13(5), 801; https://doi.org/10.3390/ani13050801 - 23 Feb 2023
Cited by 4 | Viewed by 2815
Abstract
Automated monitoring systems have become increasingly important for zoological institutions in the study of their animals’ behavior. One crucial processing step for such a system is the re-identification of individuals when using multiple cameras. Deep learning approaches have become the standard methodology for [...] Read more.
Automated monitoring systems have become increasingly important for zoological institutions in the study of their animals’ behavior. One crucial processing step for such a system is the re-identification of individuals when using multiple cameras. Deep learning approaches have become the standard methodology for this task. Especially video-based methods promise to achieve a good performance in re-identification, as they can leverage the movement of an animal as an additional feature. This is especially important for applications in zoos, where one has to overcome specific challenges such as changing lighting conditions, occlusions or low image resolutions. However, large amounts of labeled data are needed to train such a deep learning model. We provide an extensively annotated dataset including 13 individual polar bears shown in 1431 sequences, which is an equivalent of 138,363 images. PolarBearVidID is the first video-based re-identification dataset for a non-human species to date. Unlike typical human benchmark re-identification datasets, the polar bears were filmed in a range of unconstrained poses and lighting conditions. Additionally, a video-based re-identification approach is trained and tested on this dataset. The results show that the animals can be identified with a rank-1 accuracy of 96.6%. We thereby show that the movement of individual animals is a characteristic feature and it can be utilized for re-identification. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) Applied to Animal Health and Welfare)
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15 pages, 2486 KiB  
Article
Association between Rumination Times Detected by an Ear Tag-Based Accelerometer System and Rumen Physiology in Dairy Cows
by Anne Simoni, Andrew Hancock, Christian Wunderlich, Marcus Klawitter, Thomas Breuer, Felix König, Karina Weimar, Marc Drillich and Michael Iwersen
Animals 2023, 13(4), 759; https://doi.org/10.3390/ani13040759 - 20 Feb 2023
Cited by 2 | Viewed by 2239
Abstract
Monitoring rumination activity is considered a useful indicator for the early detection of diseases and metabolic disorders. Accelerometer-based sensor systems provide health alerts based on individual thresholds of rumination times in dairy cows. Detailed knowledge of the relationship between sensor-based rumination times and [...] Read more.
Monitoring rumination activity is considered a useful indicator for the early detection of diseases and metabolic disorders. Accelerometer-based sensor systems provide health alerts based on individual thresholds of rumination times in dairy cows. Detailed knowledge of the relationship between sensor-based rumination times and rumen physiology would help detect conspicuous animals and evaluate the treatment’s success. This study aimed to investigate the association between sensor-based health alerts and rumen fluid characteristics in Holstein-Friesian cows at different stages of lactation. Rumen fluid was collected via a stomach tube from 63 pairs of cows with and without health alerts (ALRT vs NALRT). Pairs were matched based on the day of lactation, the number of lactations, and health criteria. Rumen fluid was collected during and after health alerts. The parameters of color, odor, consistency, pH, redox potential, sedimentation flotation time, and the number of protozoa were examined. Results showed differences between both groups in odor, rumen pH, sedimentation flotation time, and protozoan count at the first rumen fluid collection. Within the groups, greater variations in rumen fluid parameters were found for ALRT cows compared to NALRT cows. The interaction between health alert and stage of lactation did not affect the rumen fluid parameters. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) Applied to Animal Health and Welfare)
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9 pages, 3139 KiB  
Communication
Assessment of Ventral Tail Base Surface Temperature for the Early Detection of Japanese Black Calves with Fever
by Yosuke Sasaki, Yoshihiro Iki, Tomoaki Anan, Jun Hayashi and Mizuho Uematsu
Animals 2023, 13(3), 469; https://doi.org/10.3390/ani13030469 - 29 Jan 2023
Viewed by 1422
Abstract
The objective in the present study was to assess the ventral tail base surface temperature (ST) for the early detection of Japanese Black calves with fever. This study collected data from a backgrounding operation in Miyazaki, Japan, that included 153 calves aged 3–4 [...] Read more.
The objective in the present study was to assess the ventral tail base surface temperature (ST) for the early detection of Japanese Black calves with fever. This study collected data from a backgrounding operation in Miyazaki, Japan, that included 153 calves aged 3–4 months. A wearable wireless ST sensor was attached to the surface of the ventral tail base of each calf at its introduction to the farm. The ventral tail base ST was measured every 10 min for one month. The present study conducted an experiment to detect calves with fever using the estimated residual ST (rST), calculated as the estimated rST minus the mean estimated rST for the same time on the previous 3 days, which was obtained using machine learning algorithms. Fever was defined as an increase of ≥1.0 °C for the estimated rST of a calf for 4 consecutive hours. The machine learning algorithm that applied was a random forest, and 15 features were included. The variable importance scores that represented the most important predictors for the detection of calves with fever were the minimum and maximum values during the last 3 h and the difference between the current value and 24- and 48-h minimum. For this prediction model, accuracy, precision, and sensitivity were 98.8%, 72.1%, and 88.1%, respectively. The present study indicated that the early detection of calves with fever can be predicted by monitoring the ventral tail base ST using a wearable wireless sensor. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) Applied to Animal Health and Welfare)
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