Precision Farming Technologies for Monitoring Livestock and Poultry

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Livestock Farming Technology".

Deadline for manuscript submissions: 1 April 2025 | Viewed by 1630

Special Issue Editors


E-Mail Website
Guest Editor
Biological and Agricultural Engineering, University of Arkansas, Fayetteville, AR, USA
Interests: precision livestock farming; precision poultry; artificial intelligence; computer vision; machine learning; sensors; animal behavior and welfare

E-Mail Website
Guest Editor
Biological and Agricultural Engineering, University of Arkansas, Fayetteville, AR, USA
Interests: precision livestock farming; artificial intelligence; machine vision systems; imaging system; smart food manufacturing; robotics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Poultry Science, University of Georgia, Athens, GA, USA
Interests: precision poultry and livestock farming; climate-smart farming; computer vision; machine learning; sensors; big data; animal behavior and welfare; animal environment; sustainable agriculture
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Animal Science, Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
Interests: precision livestock management; animal housing and environmental control; applied data analysis in agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of precision livestock and poultry farming has rapidly evolved and transformed management practices by integrating state-of-the-art sensing technologies and computer tools to offer innovative farming solutions. Recent advancements in sensors, IoT, computer vision, machine learning, and robotics have revolutionized traditional practices by enabling continuous, objective, and automated monitoring of animal health, behavior, and welfare. These technologies enhance production efficiency, support sustainable farming practices, and improve animal welfare.

We invite you to contribute to this Special Issue on “Precision Farming Technologies for Monitoring Livestock and Poultry” which aims to gather cutting-edge research and comprehensive reviews on applying these technologies in livestock and poultry management. We are pleased to invite researchers from around the world to contribute original research and review papers. Showcasing these advancements and improvements over time provides the transformative impact of precision technologies on the poultry and livestock industry.

Topics of interest include, but are not limited to:

  • Precision poultry management;
  • Precision livestock farming;
  • Sensor utilization;
  • Data management;
  • Robotics applications;
  • Simulations of robotic systems;
  • Smart farming solutions for optimizing resource use;
  • IoT implementations;
  • Artificial intelligence;
  • Computer vision;
  • Machine learning;
  • Precision health monitoring and disease detection;
  • Welfare and behavior monitoring;
  • Nutrient management systems for precision feeding;
  • Precision breeding techniques.

Dr. Ramesh Bahadur Bist
Dr. Dongyi Wang
Dr. Lilong Chai
Dr. Yijie Xiong
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. AgriEngineering is an international peer-reviewed open access quarterly 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 1600 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
  • precision poultry farming
  • machine learning
  • deep learning
  • computer vision
  • robotics
  • wearable devices
  • IoT
  • sensors
  • big data

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 polices can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

15 pages, 2347 KiB  
Article
A Machine Vision System for Monitoring Wild Birds on Poultry Farms to Prevent Avian Influenza
by Xiao Yang, Ramesh Bahadur Bist, Sachin Subedi, Zihao Wu, Tianming Liu, Bidur Paneru and Lilong Chai
AgriEngineering 2024, 6(4), 3704-3718; https://doi.org/10.3390/agriengineering6040211 - 9 Oct 2024
Viewed by 1281
Abstract
The epidemic of avian influenza outbreaks, especially high-pathogenicity avian influenza (HPAI), which causes respiratory disease and death, is a disaster in poultry. The outbreak of HPAI in 2014–2015 caused the loss of 60 million chickens and turkeys. The most recent HPAI outbreak, ongoing [...] Read more.
The epidemic of avian influenza outbreaks, especially high-pathogenicity avian influenza (HPAI), which causes respiratory disease and death, is a disaster in poultry. The outbreak of HPAI in 2014–2015 caused the loss of 60 million chickens and turkeys. The most recent HPAI outbreak, ongoing since 2021, has led to the loss of over 50 million chickens so far in the US and Canada. Farm biosecurity management practices have been used to prevent the spread of the virus. However, existing practices related to controlling the transmission of the virus through wild birds, especially waterfowl, are limited. For instance, ducks were considered hosts of avian influenza viruses in many past outbreaks. The objectives of this study were to develop a machine vision framework for tracking wild birds and test the performance of deep learning models in the detection of wild birds on poultry farms. A deep learning framework based on computer vision was designed and applied to the monitoring of wild birds. A night vision camera was used to collect data on wild bird near poultry farms. In the data, there were two main wild birds: the gadwall and brown thrasher. More than 6000 pictures were extracted through random video selection and applied in the training and testing processes. An overall precision of 0.95 ([email protected]) was reached by the model. The model is capable of automatic and real-time detection of wild birds. Missed detection mainly came from occlusion because the wild birds tended to hide in grass. Future research could be focused on applying the model to alert to the risk of wild birds and combining it with unmanned aerial vehicles to drive out detected wild birds. Full article
(This article belongs to the Special Issue Precision Farming Technologies for Monitoring Livestock and Poultry)
Show Figures

Figure 1

Back to TopTop