Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
2.7
Journals
Animals
Special Issues
Real-Time Sensors and Their Applications in Smart Animal Agriculture
share announcement

Real-Time Sensors and Their Applications in Smart Animal Agriculture

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 839

Special Issue Editor

Dr. Isabella C. F. S. Condotta

E-Mail Website
Guest Editor
Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Interests: precision management of animals; precision livestock farming; artificial intelligence; machine learning; computer vision; automation

Special Issue Information

Dear Colleagues,

The integration of real-time sensors into animal agriculture is revolutionizing livestock management and enhancing productivity, animal welfare, and environmental sustainability. These advanced sensor technologies, including wearable devices, smart cameras, and environmental sensors, enable continuous monitoring of animal behavior, health, and well-being. By providing real-time data on vital signs, movement patterns, feeding behavior, and environmental conditions, sensors empower farmers to make more informed, timely, and precise management decisions.

This Special Issue will explore the development, application, and impact of real-time sensor technologies in smart animal agriculture. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: the use of sensors for health monitoring, behavior tracking, precision feeding, and environmental control. Additionally, we encourage studies that address the challenges and opportunities of integrating sensor data with AI and machine learning systems to enhance decision-making processes.

We invite you to share your latest research and findings on real-time sensors and their applications in advancing smart animal agriculture.

I look forward to receiving your contributions.

Dr. Isabella C. F. S. Condotta
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

  • precision livestock farming
  • animal monitoring sensors
  • real-time data analytics
  • smart farming technologies
  • sensor-based animal management

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 11556 KiB  
Article
Enhanced Methodology and Experimental Research for Caged Chicken Counting Based on YOLOv8
by Zhenlong Wu, Jikang Yang, Hengyuan Zhang and Cheng Fang
Animals 2025, 15(6), 853; https://doi.org/10.3390/ani15060853 - 16 Mar 2025
Viewed by 522
Abstract
Accurately counting chickens in densely packed cages is a major challenge in large-scale poultry farms. Traditional manual counting methods are labor-intensive, costly, and prone to errors due to worker fatigue. Furthermore, current deep learning models often struggle with accuracy in caged environments because [...] Read more.
Accurately counting chickens in densely packed cages is a major challenge in large-scale poultry farms. Traditional manual counting methods are labor-intensive, costly, and prone to errors due to worker fatigue. Furthermore, current deep learning models often struggle with accuracy in caged environments because they are not well-equipped to handle occlusions. In response, we propose the You Only Look Once-Chicken Counting Algorithm (YOLO-CCA). YOLO-CCA improves the YOLOv8-small model by integrating the CoordAttention mechanism and the Reversible Column Networks backbone. This enhancement improved the YOLOv8-small model’s F1 score to 96.7% (+3%) and average precision50:95 to 80.6% (+2.8%). Additionally, we developed a threshold-based continuous frame inspection method that records the maximum number of chickens per cage with corresponding timestamps. The data are stored in a cloud database for reliable tracking during robotic inspections. The experiments were conducted in an actual poultry farming environment, involving 80 cages with a total of 493 chickens, and showed that YOLO-CCA raised the chicken recognition rate to 90.9% (+13.2%). When deployed on a Jetson AGX Orin industrial computer using TensorRT, the detection speed increased to 90.9 FPS (+57.6 FPS), although the recognition rate slightly decreased to 93.2% (−2.9%). In summary, YOLO-CCA reduces labor costs, improves counting efficiency, and supports intelligent poultry farming transformation. Full article
(This article belongs to the Special Issue Real-Time Sensors and Their Applications in Smart Animal Agriculture)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop