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Agriculture
Special Issues
Computer Vision Analysis Applied to Farm Animals
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Computer Vision Analysis Applied to Farm Animals

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 580

Special Issue Editors

Dr. Yang Zhao

E-Mail Website
Guest Editor
Department of Animal Science, University of Tennessee, Knoxville, TN 37996, USA
Interests: precision livestock farming; poultry behavior and welfare; poultry environment, airborne transmission; air pollutant monitoring and mitigation
Special Issues, Collections and Topics in MDPI journals
Dr. Beibei Xu

E-Mail Website
Guest Editor
Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14850, USA
Interests: digital agriculture; precision farming; machine learning; deep learning; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of computer vision and image analysis into precision livestock farming has revolutionized agricultural practices, offering innovative solutions for monitoring and managing farm animals. Historically, traditional animal husbandry relied heavily on manual inspection and intervention. However, recent advances in technology have introduced sophisticated image processing techniques that enable real-time, automated analysis. The aim of this Special Issue is to explore cutting-edge research and applications in computer vision and image analysis for precision livestock farming, emphasizing their role in improving animal health, welfare, and overall farm efficiency. We are soliciting papers that present novel methodologies, applications, and case studies in this field. Topics of interest include, but are not limited to, advanced imaging techniques for monitoring animal health, automated behavior analysis, and integration of computer vision systems with other precision livestock farming technologies. This Special Issue seeks to bring together innovative research and practical solutions to address current challenges and future opportunities in livestock farming.

Dr. Yang Zhao
Dr. Beibei Xu
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. Agriculture 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 2600 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

  • computer vision
  • image analysis
  • precision livestock farming
  • animal welfare
  • deep learning
  • behavior monitoring
  • health assessment
  • data fusion
  • imaging technologies

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Published Papers (1 paper)

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Research

13 pages, 2855 KiB  
Article
Research on Video Behavior Detection and Analysis Model for Sow Estrus Cycle Based on Deep Learning
by Kaidong Lei, Bugao Li, Shan Zhong, Hua Yang, Hao Wang, Xiangfang Tang and Benhai Xiong
Agriculture 2025, 15(9), 975; https://doi.org/10.3390/agriculture15090975 (registering DOI) - 30 Apr 2025
Viewed by 57
Abstract
Against the backdrop of precision livestock farming, sow behavior analysis holds significant theoretical and practical value. Traditional production methods face challenges such as low production efficiency, high labor intensity, and increased disease prevention risks. With the rapid advancement of optoelectronic technology and deep [...] Read more.
Against the backdrop of precision livestock farming, sow behavior analysis holds significant theoretical and practical value. Traditional production methods face challenges such as low production efficiency, high labor intensity, and increased disease prevention risks. With the rapid advancement of optoelectronic technology and deep learning, more technologies are being integrated into smart agriculture. Intelligent large-scale pig farming has become an effective means to improve sow quality and productivity, with behavior recognition technology playing a crucial role in intelligent pig farming. Specifically, monitoring sow behavior enables an effective assessment of health conditions and welfare levels, ensuring efficient and healthy sow production. This study constructs a 3D-CNN model based on video data from the sow estrus cycle, achieving analysis of SOB, SOC, SOS, and SOW behaviors. In typical behavior classification, the model attains accuracy, recall, and F1-score values of (1.00, 0.90, 0.95; 0.96, 0.98, 0.97; 1.00, 0.96, 0.98; 0.86, 1.00, 0.93), respectively. Additionally, under conditions of multi-pig interference and non-specifically labeled data, the accuracy, recall, and F1-scores for the semantic recognition of SOB, SOC, SOS, and SOW behaviors based on the 3D-CNN model are (1.00, 0.90, 0.95; 0.89, 0.89, 0.89; 0.91, 1.00, 0.95; 1.00, 1.00, 1.00), respectively. These findings provide key technical support for establishing the classification and semantic recognition of typical sow behaviors during the estrus cycle, while also offering a practical solution for rapid video-based behavior detection and welfare monitoring in precision livestock farming. Full article
(This article belongs to the Special Issue Computer Vision Analysis Applied to Farm Animals)
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