Moving towards Intelligence: Advances and Perspectives on Smart Poultry Farming

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Poultry".

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 1840

Special Issue Editors

Department of Animal Science, The University of Tennessee, Knoxville, TN, USA
Interests: animal smart sensoring; robotics; behavior monitoring; welfare assessment; airborne transmission of pathogens; and environment management
Special Issues, Collections and Topics in MDPI journals
Department of Poultry Science, University of Georgia, Athens, GA, USA
Interests: precision livestock farming; animal welfare and behavior; smart sensing; applied artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Department of Agricultural Structure and Bioenvironmental Engineering, China Agricultural University, Beijing, China
Interests: precision poultry farming; animal welfare; smart sensing; air pollutants dispersion

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit your manuscript(s) to the Special Issue entitled “Moving towards Intelligence: Advances and Perspectives on Smart Poultry Farming”.

We are facing ever-growing demands for protein resources. To meet such demands, global poultry meat and egg production has soared in the past decades and is projected to keep growing in the decades to come. While poultry production makes critical contributions to food and nutrition security, it uses substantial natural and human resources and has significant impacts on society, the economy, public health, and the environment. Although the extent of these impacts may vary among continents and countries due to differences in production practices, climate conditions, and social structures and preferences, the global poultry industry, as a whole, should strive to keep improving its sustainability and efficiency in the resource usage. Smart Poultry Farming (SPF) features applications of intelligent and automated sensing technologies and computer tools for sustainable and efficient poultry production, and it offers solutions to the poultry industry to address challenges in terms of poultry management, the environment, nutrition, automation and robotics, health, welfare assessment, behaviour monitoring, waste management, etc.

This Special Issue aims to collect research and review papers addressing the sustainability and efficiency of poultry production and exploring the aforementioned areas through applications of SPF solutions in poultry meat and egg production. 

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Poultry environment;
  • Poultry nutrition;
  • Poultry welfare and behaviour;
  • Poultry robotics;
  • Poultry data management;
  • Poultry genotype and phenotype;
  • Sensing technologies for poultry application;
  • The IoT;
  • Artificial intelligence;
  • Waste management;
  • The social impact of smart poultry farming.

We look forward to receiving your contributions.

Dr. Yang Zhao
Dr. Lilong Chai
Dr. Guoming Li
Dr. Xiao Yang
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. 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

  • artificial intelligence
  • poultry production
  • animal welfare
  • sustainability
  • smart poultry farming
  • precision poultry farming

Published Papers (1 paper)

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Research

16 pages, 2684 KiB  
Article
Tracking and Characterizing Spatiotemporal and Three-Dimensional Locomotive Behaviors of Individual Broilers in the Three-Point Gait-Scoring System
by Guoming Li, Richard S. Gates, Meaghan M. Meyer and Elizabeth A. Bobeck
Animals 2023, 13(4), 717; https://doi.org/10.3390/ani13040717 - 17 Feb 2023
Cited by 4 | Viewed by 1439
Abstract
Gait scoring is a useful measure for evaluating broiler production efficiency, welfare status, bone quality, and physiology. The research objective was to track and characterize spatiotemporal and three-dimensional locomotive behaviors of individual broilers with known gait scores by jointly using deep-learning algorithms, depth [...] Read more.
Gait scoring is a useful measure for evaluating broiler production efficiency, welfare status, bone quality, and physiology. The research objective was to track and characterize spatiotemporal and three-dimensional locomotive behaviors of individual broilers with known gait scores by jointly using deep-learning algorithms, depth sensing, and image processing. Ross 708 broilers were placed on a platform specifically designed for gait scoring and manually categorized into one of three numerical scores. Normal and depth cameras were installed on the ceiling to capture top-view videos and images. Four birds from each of the three gait-score categories were randomly selected out of 70 total birds scored for video analysis. Bird moving trajectories and 16 locomotive-behavior metrics were extracted and analyzed via the developed deep-learning models. The trained model gained 100% accuracy and 3.62 ± 2.71 mm root-mean-square error for tracking and estimating a key point on the broiler back, indicating precise recognition performance. Broilers with lower gait scores (less difficulty walking) exhibited more obvious lateral body oscillation patterns, moved significantly or numerically faster, and covered more distance in each movement event than those with higher gait scores. In conclusion, the proposed method had acceptable performance for tracking broilers and was found to be a useful tool for characterizing individual broiler gait scores by differentiating between selected spatiotemporal and three-dimensional locomotive behaviors. Full article
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