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Animals
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Animal Medical Engineering—І: Signal Acquisition and Analysis of Livestock Animals
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Animal Medical Engineering—І: Signal Acquisition and Analysis of Livestock Animals

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 20739

Special Issue Editor

Prof. Dr. Hyeon Tae Kim

E-Mail Website1 Website2
Guest Editor
Department of Bio-Systems Engineering, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Republic of Korea
Interests: agricultural environmental engineering; smart farming; machine learning; deep learning; animal welfare; pattern analysis; plant science; food testing; food security; sustainable production and energy use; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, livestock production has had rapid development in developing countries. However, because of knowledge gaps, the keeping of livestock is still mainly free-range and the methods of animal husbandry and the monitoring strategies of most of the farms are conventional, which has resulted in lower unit yield and efficiency but higher feed costs, operational cost and so on. There is a need, for both economic and quality assurance reasons, for an efficient and cost-effective method for identifying and tracking livestock, and for the monitoring of the processing of that livestock. The quality of livestock data depends, to a great extent, on the data collection system adopted. Multiple sources and methods of data acquisition exist, which however need to be integrated into a functional farm management system. Precision livestock farming (PLF) systems have the potential to acquire data concerning the biological signals such as identification, monitoring, behavior pattern changes etc. Therefore, this Special Issue aims to gather current research from across the world that will drive animal behavior and welfare towards the new paradigm of livestock signal acquisition systems that is essential for production efficiency improvement. Near future, the extension of the current issue will be named as “Animal medical engineering-ІІ”, intend to use the data acquisition methodologies and analysis to improve the welfare of the livestock animals.

We are seeking original research papers about how technology can be used to acquire the signals of livestock animals for enhancing animal welfare. In addition, we are seeking technology that includes the development of models or algorithms for use in PLF systems such as bio signal analysis, machine learning, pattern recognition, etc. Moreover, topics may be related to smart farming, biological signal acquisition methodologies and analysis of the environmental impact on animal welfare.

Dr. Hyeon Tae Kim
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

  • Bio signal analysis
  • Machine learning
  • Animal welfare
  • Pattern analysis
  • Animal health

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Published Papers (3 papers)

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Research

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20 pages, 6536 KiB  
Article
Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations
by Anil Bhujel, Elanchezhian Arulmozhi, Byeong-Eun Moon and Hyeon-Tae Kim
Animals 2021, 11(11), 3089; https://doi.org/10.3390/ani11113089 - 29 Oct 2021
Cited by 27 | Viewed by 4059
Abstract
Pig behavior is an integral part of health and welfare management, as pigs usually reflect their inner emotions through behavior change. The livestock environment plays a key role in pigs’ health and wellbeing. A poor farm environment increases the toxic GHGs, which might [...] Read more.
Pig behavior is an integral part of health and welfare management, as pigs usually reflect their inner emotions through behavior change. The livestock environment plays a key role in pigs’ health and wellbeing. A poor farm environment increases the toxic GHGs, which might deteriorate pigs’ health and welfare. In this study a computer-vision-based automatic monitoring and tracking model was proposed to detect pigs’ short-term physical activities in the compromised environment. The ventilators of the livestock barn were closed for an hour, three times in a day (07:00–08:00, 13:00–14:00, and 20:00–21:00) to create a compromised environment, which increases the GHGs level significantly. The corresponding pig activities were observed before, during, and after an hour of the treatment. Two widely used object detection models (YOLOv4 and Faster R-CNN) were trained and compared their performances in terms of pig localization and posture detection. The YOLOv4, which outperformed the Faster R-CNN model, was coupled with a Deep-SORT tracking algorithm to detect and track the pig activities. The results revealed that the pigs became more inactive with the increase in GHG concentration, reducing their standing and walking activities. Moreover, the pigs shortened their sternal-lying posture, increasing the lateral lying posture duration at higher GHG concentration. The high detection accuracy (mAP: 98.67%) and tracking accuracy (MOTA: 93.86% and MOTP: 82.41%) signify the models’ efficacy in the monitoring and tracking of pigs’ physical activities non-invasively. Full article
(This article belongs to the Special Issue Animal Medical Engineering—І: Signal Acquisition and Analysis of Livestock Animals)
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11 pages, 3256 KiB  
Article
Breathing Pattern Analysis in Cattle Using Infrared Thermography and Computer Vision
by Sueun Kim and Yuichi Hidaka
Animals 2021, 11(1), 207; https://doi.org/10.3390/ani11010207 - 16 Jan 2021
Cited by 20 | Viewed by 4937
Abstract
Breathing patterns can be considered a vital sign providing health information. Infrared thermography is used to evaluate breathing patterns because it is non-invasive. Our study used not only sequence temperature data but also RGB images to gain breathing patterns in cattle. Mask R-CNN [...] Read more.
Breathing patterns can be considered a vital sign providing health information. Infrared thermography is used to evaluate breathing patterns because it is non-invasive. Our study used not only sequence temperature data but also RGB images to gain breathing patterns in cattle. Mask R-CNN was used to detect the ROI (region of interest, nose) in the cattle RGB images. Mask segmentation from the ROI detection was applied to the corresponding temperature data. Finally, to visualize the breathing pattern, we calculated the temperature values in the ROI by averaging all temperature values in the ROI. The results in this study show 76% accuracy with Mask R-CNN in detecting cattle noses. With respect to the temperature calculation methods, the averaging method showed the most appropriate breathing pattern compared to other methods (maximum temperature in the ROI and integrating all temperature values in the ROI). Finally, we compared the breathing pattern from the averaging method and that from the thermal image observation and found them to be highly correlated (R2 = 0.91). This method is not labor-intensive, can handle big data, and is accurate. In addition, we expect that the characteristics of the method might enable the analysis of temperature data from various angles. Full article
(This article belongs to the Special Issue Animal Medical Engineering—І: Signal Acquisition and Analysis of Livestock Animals)
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Review

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23 pages, 2613 KiB  
Review
The Application of Cameras in Precision Pig Farming: An Overview for Swine-Keeping Professionals
by Elanchezhian Arulmozhi, Anil Bhujel, Byeong-Eun Moon and Hyeon-Tae Kim
Animals 2021, 11(8), 2343; https://doi.org/10.3390/ani11082343 - 9 Aug 2021
Cited by 45 | Viewed by 10492
Abstract
Pork is the meat with the second-largest overall consumption, and chicken, pork, and beef together account for 92% of global meat production. Therefore, it is necessary to adopt more progressive methodologies such as precision livestock farming (PLF) rather than conventional methods to improve [...] Read more.
Pork is the meat with the second-largest overall consumption, and chicken, pork, and beef together account for 92% of global meat production. Therefore, it is necessary to adopt more progressive methodologies such as precision livestock farming (PLF) rather than conventional methods to improve production. In recent years, image-based studies have become an efficient solution in various fields such as navigation for unmanned vehicles, human–machine-based systems, agricultural surveying, livestock, etc. So far, several studies have been conducted to identify, track, and classify the behaviors of pigs and achieve early detection of disease, using 2D/3D cameras. This review describes the state of the art in 3D imaging systems (i.e., depth sensors and time-of-flight cameras), along with 2D cameras, for effectively identifying pig behaviors and presents automated approaches for the monitoring and investigation of pigs’ feeding, drinking, lying, locomotion, aggressive, and reproductive behaviors. Full article
(This article belongs to the Special Issue Animal Medical Engineering—І: Signal Acquisition and Analysis of Livestock Animals)
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