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Animals
Special Issues
Animal Production in the Artificial Intelligence Era: Advances and Applications
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Animal Production in the Artificial Intelligence Era: Advances and Applications

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

Deadline for manuscript submissions: 15 November 2024 | Viewed by 2179

Special Issue Editors

Dr. Saleh Shahinfar

E-Mail Website
Guest Editor
Precision Farming Consultant, Melbourne, VIC, Australia
Interests: artificial intelligence; machine learning; fuzzy logic; precision farming; decision support systems; phenomics
Dr. Sajjad Toghiani

E-Mail Website
Guest Editor
Agricultural Research Service, Animal Genomics and Improvement Laboratory, USDA, Beltsville, MD, USA
Interests: quantitative genetics; genomic selection; statistical genetics; population genomics; genomic inbreeding; genetic adaptation; genotype imputation
Prof. Dr. Cedric Gondro

E-Mail Website
Guest Editor
Department of Animal Science, College of Agriculture and Natural Resources, Michigan State University, East Lansing, MI, USA
Interests: quantitative genetics; animal breeding; computational genomics; evolutionary computation; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Romdhane Rekaya

E-Mail Website
Guest Editor
1. Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
2. Department of Statistics, University of Georgia, Athens, GA, USA
3. Institute of Bioinformatics, University of Georgia, Athens, GA, USA
4. Institute of Integrative and Precision Agriculture, University of Georgia, Athens, GA, USA
Interests: quantitative genetics; genomics; animal breeding; Bayesian inference; large scale data analysis; precision agriculture

Special Issue Information

Dear Colleagues,

The third decade of the 21st century has ushered in an unprecedented AI revolution. We are witnessing the transformation of industries and societies through groundbreaking advancements in artificial intelligence. Animal production has a tremendous opportunity to capitalize on this trend as it already has a long history of developing and adopting advanced data analytics techniques and counts with a very talented scientific community and forward-thinking practitioners. Applications of AI in animal production include, are but not limited to, measuring, monitoring, and managing animal production systems to enhance profitability, production, and health event forecasting, real time prediction, and decision support tools. To accomplish that, the integration of sensors, hyper-spectral, MIR/NMR, imaging, and omics data to phenomics data for the prediction of performance, health, welfare, and behavior, reproduction or even breeding values, and selection decisions are inevitable and bring new challenges and opportunities to the field.

Our aim with this Special Issue is to invite the precision animal farming community to present their latest cutting-edge research in artificial intelligence applied to animal production. Original research, reviews, or commentary papers with a focus on advancements of AI in real-world animal production applications, health and welfare, reproduction, genetics, and breeding programs are especially encouraged to be submitted to this Special Issue.

Dr. Saleh Shahinfar
Dr. Sajjad Toghiani
Prof. Dr. Cedric Gondro
Prof. Dr. Romdhane Rekaya
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
  • machine learning
  • deep learning
  • forecasting (prediction)
  • animal production
  • animal health and welfare
  • breeding and genetics
  • reproduction
  • high-throughput phenotyping and big data
  • sensors
  • computer vision

Published Papers (2 papers)

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Research

23 pages, 19155 KiB  
Article
Open-Set Recognition of Individual Cows Based on Spatial Feature Transformation and Metric Learning
by Buyu Wang, Xia Li, Xiaoping An, Weijun Duan, Yuan Wang, Dian Wang and Jingwei Qi
Animals 2024, 14(8), 1175; https://doi.org/10.3390/ani14081175 - 14 Apr 2024
Viewed by 518
Abstract
The automated recognition of individual cows is foundational for implementing intelligent farming. Traditional methods of individual cow recognition from an overhead perspective primarily rely on singular back features and perform poorly for cows with diverse orientation distributions and partial body visibility in the [...] Read more.
The automated recognition of individual cows is foundational for implementing intelligent farming. Traditional methods of individual cow recognition from an overhead perspective primarily rely on singular back features and perform poorly for cows with diverse orientation distributions and partial body visibility in the frame. This study proposes an open-set method for individual cow recognition based on spatial feature transformation and metric learning to address these issues. Initially, a spatial transformation deep feature extraction module, ResSTN, which incorporates preprocessing techniques, was designed to effectively address the low recognition rate caused by the diverse orientation distribution of individual cows. Subsequently, by constructing an open-set recognition framework that integrates three attention mechanisms, four loss functions, and four distance metric methods and exploring the impact of each component on recognition performance, this study achieves refined and optimized model configurations. Lastly, introducing moderate cropping and random occlusion strategies during the data-loading phase enhances the model’s ability to recognize partially visible individuals. The method proposed in this study achieves a recognition accuracy of 94.58% in open-set scenarios for individual cows in overhead images, with an average accuracy improvement of 2.98 percentage points for cows with diverse orientation distributions, and also demonstrates an improved recognition performance for partially visible and randomly occluded individual cows. This validates the effectiveness of the proposed method in open-set recognition, showing significant potential for application in precision cattle farming management. Full article
(This article belongs to the Special Issue Animal Production in the Artificial Intelligence Era: Advances and Applications)
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21 pages, 6076 KiB  
Article
In Vivo Prediction of Breast Muscle Weight in Broiler Chickens Using X-ray Images Based on Deep Learning and Machine Learning
by Rui Zhu, Jiayao Li, Junyan Yang, Ruizhi Sun and Kun Yu
Animals 2024, 14(4), 628; https://doi.org/10.3390/ani14040628 - 16 Feb 2024
Viewed by 704
Abstract
Accurately estimating the breast muscle weight of broilers is important for poultry production. However, existing related methods are plagued by cumbersome processes and limited automation. To address these issues, this study proposed an efficient method for predicting the breast muscle weight of broilers. [...] Read more.
Accurately estimating the breast muscle weight of broilers is important for poultry production. However, existing related methods are plagued by cumbersome processes and limited automation. To address these issues, this study proposed an efficient method for predicting the breast muscle weight of broilers. First, because existing deep learning models struggle to strike a balance between accuracy and memory consumption, this study designed a multistage attention enhancement fusion segmentation network (MAEFNet) to automatically acquire pectoral muscle mask images from X-ray images. MAEFNet employs the pruned MobileNetV3 as the encoder to efficiently capture features and adopts a novel decoder to enhance and fuse the effective features at various stages. Next, the selected shape features were automatically extracted from the mask images. Finally, these features, including live weight, were input to the SVR (Support Vector Regression) model to predict breast muscle weight. MAEFNet achieved the highest intersection over union (96.35%) with the lowest parameter count (1.51 M) compared to the other segmentation models. The SVR model performed best (R2 = 0.8810) compared to the other prediction models in the five-fold cross-validation. The research findings can be applied to broiler production and breeding, reducing measurement costs, and enhancing breeding efficiency. Full article
(This article belongs to the Special Issue Animal Production in the Artificial Intelligence Era: Advances and Applications)
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