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Animals
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Technology and Engineering Solutions in Livestock Farming
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Technology and Engineering Solutions in Livestock Farming

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 December 2021) | Viewed by 24253

Special Issue Editor

Dr. Abozar Nasirahmadi

E-Mail Website
Guest Editor
Department of Agricultural and Biosystems Engineering, University of Kassel, 37213 Witzenhausen, Germany
Interests: machine vision; artificial intelligence; precision livestock farming; robotics in agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the increase in world population and market demand for meat and milk products, the scale of animal husbandry must increase. Therefore, addressing the issue of animal health and welfare becomes more essential for the farm owners as well as scientists. Precision livestock farming, as a management tool of livestock production for monitoring of animal health and behavior, and evaluating the environmental impact on livestock production and technology solutions to improve animal welfare has been increasingly utilized in recent years to support both commercial and research stakeholders in addressing these challenges. In this context, innovative technologies and techniques as well as machine learning and statistical models make it possible for a deeper understanding of precision livestock management systems which have led to the improvement of animal welfare and performance as well as sustainability. There has been some rapid advancements in sensor and low-cost technologies application in animal monitoring, data processing and optimization, disease, behavior and stress prediction in animal farming, indoor and outdoor reliable optical and non-optical based sensors application.

Therefore, the object of this Special Issue is to promote a deeper understanding of the latest findings in precision livestock farming research, engineering, and management solutions in all fields of livestock farming. We invite original research and review articles that cover a broad range of topics in livestock farming. The intention of this Special Issue is to focus on the most recent techniques in the research areas that include (but are not limited to):

  • Technology application (e.g., camera, microphone, accelerometer, temperature, air quality sensors, etc.) in assessment/monitoring of behaviors, health and welfare of animals
  • Application of artificial intelligence, machine learning, big data and statistical models in livestock farming
  • Modeling and/or simulation of livestock barn and/or environmental conditions
  • Investigation/analysis as well as modeling of nutritional status of animals
  • Sensor fusion and signal processing
  • Engineering-based methodology to develop advanced farm management systems
  • Assessment of economic and environmental aspects associated with livestock farming management

Dr. Abozar Nasirahmadi
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

  • livestock management
  • artificial intelligence
  • machine learning
  • sensors
  • image and signal processing
  • animal health and welfare
  • big data
  • digital technology

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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14 pages, 1863 KiB  
Article
Estrus Prediction Models for Dairy Gyr Heifers
by Valesca Vilela Andrade, Priscila Arrigucci Bernardes, Rogério Ribeiro Vicentini, André Penido Oliveira, Renata Veroneze, Aska Ujita, João Alberto Negrão and Lenira El Faro
Animals 2021, 11(11), 3103; https://doi.org/10.3390/ani11113103 - 30 Oct 2021
Cited by 2 | Viewed by 2496
Abstract
Technological devices are increasingly present in livestock activities, such as identifying the reproductive status of cows. For this, predictive models must be accurate and usable in the productive context. The aims of this study were to evaluate estrus-associated changes in reticulo-rumen temperature (RRT) [...] Read more.
Technological devices are increasingly present in livestock activities, such as identifying the reproductive status of cows. For this, predictive models must be accurate and usable in the productive context. The aims of this study were to evaluate estrus-associated changes in reticulo-rumen temperature (RRT) and activity (ACT) in Dairy Gyr heifers provided by reticulo-rumen boluses and to test the ability of different models for estrus prediction. The RRT and ACT of 45 heifers submitted to estrus synchronization were recorded using reticulo-rumen boluses. The means of RRT and ACT at different time intervals were compared between the day before and the day of estrus manifestation. An analysis of variance of RRT and ACT was performed using mixed models. A second approach employed logistic regression, random forest, and linear discriminant analysis models using RRT, ACT, time of day, and the temperature-humidity index (THI) as predictors. There was an increase in RRT and ACT at estrus (p < 0.05) compared to the same period on the day before and on the day after estrus. The random forest model provided the best performance values with a sensitivity of 51.69% and specificity of 93.1%. The present results suggest that RRT and ACT contribute to the identification of estrus in Dairy Gyr heifers. Full article
(This article belongs to the Special Issue Technology and Engineering Solutions in Livestock Farming)
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12 pages, 3122 KiB  
Article
Evaluation of Wearable Cameras for Monitoring and Analyzing Calf Behavior: A Preliminary Study
by Tomoko Saitoh and Yuko Kato
Animals 2021, 11(9), 2622; https://doi.org/10.3390/ani11092622 - 7 Sep 2021
Cited by 4 | Viewed by 3349
Abstract
Understanding cattle behavior is important for discerning their health and management status. However, manual observations of cattle are time-consuming and labor-intensive. Moreover, during manual observations, the presence or position of a human observer may alter the normal behavior of the cattle. Wearable cameras [...] Read more.
Understanding cattle behavior is important for discerning their health and management status. However, manual observations of cattle are time-consuming and labor-intensive. Moreover, during manual observations, the presence or position of a human observer may alter the normal behavior of the cattle. Wearable cameras are small and lightweight; therefore, they do not disturb cattle behavior when attached to their bodies. Thus, this study aimed to evaluate the suitability of wearable cameras for monitoring and analyzing cattle behavior. From December 18 to 27, 2017, this study used four 2-month-old, group-housed Holstein calves at the Field Science Center of the Obihiro University of Agriculture and Veterinary Medicine, Japan. Calf behavior was recorded every 30 s using a wearable camera (HX-A1H, Panasonic, Japan) from 10:00 to 15:30 and observed directly from 11:00 to 12:00 and 14:00 to 15:00. In addition, the same observer viewed the camera recordings corresponding to the direct observation periods, and the results were compared. The correlation coefficients of all behavioral data from direct and wearable camera video observations were significant (p < 0.01). We conclude that wearable cameras are suitable for observing calf behavior, particularly their posture (standing or lying), as well as their ruminating and feeding behaviors. Full article
(This article belongs to the Special Issue Technology and Engineering Solutions in Livestock Farming)
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16 pages, 5019 KiB  
Article
Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering
by Dae-Hyun Jung, Na Yeon Kim, Sang Ho Moon, Changho Jhin, Hak-Jin Kim, Jung-Seok Yang, Hyoung Seok Kim, Taek Sung Lee, Ju Young Lee and Soo Hyun Park
Animals 2021, 11(2), 357; https://doi.org/10.3390/ani11020357 - 1 Feb 2021
Cited by 58 | Viewed by 8928
Abstract
The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning [...] Read more.
The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle. Full article
(This article belongs to the Special Issue Technology and Engineering Solutions in Livestock Farming)
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Review

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16 pages, 971 KiB  
Review
Digital Phenotyping in Livestock Farming
by Suresh Neethirajan and Bas Kemp
Animals 2021, 11(7), 2009; https://doi.org/10.3390/ani11072009 - 5 Jul 2021
Cited by 22 | Viewed by 6410
Abstract
Currently, large volumes of data are being collected on farms using multimodal sensor technologies. These sensors measure the activity, housing conditions, feed intake, and health of farm animals. With traditional methods, the data from farm animals and their environment can be collected intermittently. [...] Read more.
Currently, large volumes of data are being collected on farms using multimodal sensor technologies. These sensors measure the activity, housing conditions, feed intake, and health of farm animals. With traditional methods, the data from farm animals and their environment can be collected intermittently. However, with the advancement of wearable and non-invasive sensing tools, these measurements can be made in real-time for continuous quantitation relating to clinical biomarkers, resilience indicators, and behavioral predictors. The digital phenotyping of humans has drawn enormous attention recently due to its medical significance, but much research is still needed for the digital phenotyping of farm animals. Implications from human studies show great promise for the application of digital phenotyping technology in modern livestock farming, but these technologies must be directly applied to animals to understand their true capacities. Due to species-specific traits, certain technologies required to assess phenotypes need to be tailored efficiently and accurately. Such devices allow for the collection of information that can better inform farmers on aspects of animal welfare and production that need improvement. By explicitly addressing farm animals’ individual physiological and mental (affective states) needs, sensor-based digital phenotyping has the potential to serve as an effective intervention platform. Future research is warranted for the design and development of digital phenotyping technology platforms that create shared data standards, metrics, and repositories. Full article
(This article belongs to the Special Issue Technology and Engineering Solutions in Livestock Farming)
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