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Animals
Special Issues
Machine Learning and Statistics Applied to Livestock—Omics Data
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Machine Learning and Statistics Applied to Livestock—Omics Data

A special issue of Animals (ISSN 2076-2615).

Deadline for manuscript submissions: 31 July 2024 | Viewed by 2702

Special Issue Editors

Dr. Marco Milanesi

E-Mail Website
Guest Editor
Department for Innovation in Biological, Agro-Food and Forest Systems, Università Della Tuscia, Via S. Camillo de Lellis snc, 01100 Viterbo, Italy
Interests: animal breeding and genetics; livestock genomic; animal biodiversity; bioinformatics; transcriptomic
Dr. Daniele Pietrucci

E-Mail Website
Guest Editor
Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, IBIOM, CNR, 70126 Bari, Italy
Interests: metagenomics; transcriptomics in human and animal health; machine learning applied to biological data
Prof. Dr. Giovanni Chillemi

E-Mail Website
Guest Editor
Department for Innovation in Biological, Agro-food and Forest systems (DIBAF), University of Tuscia, Via San Camillo de Lellis, 01100 Viterbo, Italy
Interests: animal genomics; transcriptomics; bioinformatics; in silico protein modeling; ancient DNA

Special Issue Information

Dear Colleagues,

Nowadays, the data collected in the livestock sector (phenotypes, omics data, etc.) could be considered as “big data”. For this reason, “classical” statistical methods and pipelines may no longer be sufficient to dissect the biology behind the data.

The aim of this Special Issue is to develop new pipelines and methods and to identify alternative uses for existing methods, in particular for machine learning (ML) and artificial intelligence (AI) algorithms. The results could be compared with classical statistical methods. Omics data, including genomic (WGS, SNP array, etc.), transcriptomic and metagenomic, among others, will be explored along with connected metadata from the bred animals or the hosted microorganism (in the feces, gut, rumen, etc.). The idea is to apply the aforementioned methods to different kind of analyses, for example GWAS, genomic selection and biodiversity, in general modelling and prediction.

Dr. Marco Milanesi
Dr. Daniele Pietrucci
Prof. Dr. Giovanni Chillemi
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
  • big data
  • genomic
  • transcriptomic
  • whole genome sequencing
  • SNP data
  • modelling
  • prediction

Published Papers (1 paper)

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Research

18 pages, 1917 KiB  
Article
Towards Early Poultry Health Prediction through Non-Invasive and Computer Vision-Based Dropping Classification
by Arnas Nakrosis, Agne Paulauskaite-Taraseviciene, Vidas Raudonis, Ignas Narusis, Valentas Gruzauskas, Romas Gruzauskas and Ingrida Lagzdinyte-Budnike
Animals 2023, 13(19), 3041; https://doi.org/10.3390/ani13193041 - 27 Sep 2023
Cited by 4 | Viewed by 1896
Abstract
The use of artificial intelligence techniques with advanced computer vision techniques offers great potential for non-invasive health assessments in the poultry industry. Evaluating the condition of poultry by monitoring their droppings can be highly valuable as significant changes in consistency and color can [...] Read more.
The use of artificial intelligence techniques with advanced computer vision techniques offers great potential for non-invasive health assessments in the poultry industry. Evaluating the condition of poultry by monitoring their droppings can be highly valuable as significant changes in consistency and color can be indicators of serious and infectious diseases. While most studies have prioritized the classification of droppings into two categories (normal and abnormal), with some relevant studies dealing with up to five categories, this investigation goes a step further by employing image processing algorithms to categorize droppings into six classes, based on visual information indicating some level of abnormality. To ensure a diverse dataset, data were collected in three different poultry farms in Lithuania by capturing droppings on different types of litter. With the implementation of deep learning, the object detection rate reached 92.41% accuracy. A range of machine learning algorithms, including different deep learning architectures, has been explored and, based on the obtained results, we have proposed a comprehensive solution by combining different models for segmentation and classification purposes. The results revealed that the segmentation task achieved the highest accuracy of 0.88 in terms of the Dice coefficient employing the K-means algorithm. Meanwhile, YOLOv5 demonstrated the highest classification accuracy, achieving an ACC of 91.78%. Full article
(This article belongs to the Special Issue Machine Learning and Statistics Applied to Livestock—Omics Data)
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