New Techniques and Technologies Applicable to Animal Production

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1203

Special Issue Editors


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Guest Editor
Instituto de Investigación e Innovación Agroalimentario y Agroambiental (CIAGRO), Universidad Miguel Hernández de Elche, Ctra de Beniel Km 3.2, 03312 Orihuela, Spain
Interests: animal genetics; animal physiology; animal biotechnology; welfare; metabolome; selection

E-Mail Website
Guest Editor
Instituto de Investigación e Innovación Agroalimentario y Agroambiental (CIAGRO), Universidad Miguel Hernández de Elche, Ctra de Beniel Km 3.2, 03312 Orihuela, Spain
Interests: animal genetics; animal physiology; embryo development; microbiome; metabolome; selection; welfare
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Centro de Investigación e Innovación Agroalimentariay Agroambiental (CIAGRO-UMH), Universidad Miguel Hernández de Elche, Ctra. Beniel km 3.2, 03312 Alicante, Spain
Interests: animal breeding; animal physiology; animal genetics; animal biotechnology; embryos; embryo cryopreservation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The contemporary landscape of animal production is marked by a critical juncture, driven by the imperative to enhance operational efficiency, ecological sustainability, and animal welfare standards. Novel methodologies and technological innovations are either being developed within the agricultural sciences or adapted from disparate scientific domains, fundamentally transforming the productive capacity of livestock systems. Ranging from precision livestock management, integrating sensor networks and real-time data analytics, to advancements in genomics and biotechnology directed towards genetic enhancement and animal health optimization, the sector is undergoing substantive technological evolution. The advent and integration of artificial intelligence (AI) and the Internet of Things (IoT) are further optimizing resource allocation and the continuous monitoring of livestock populations. These technologies are also being applied to animal nutrition through the formulation of bespoke diets and the incorporation of innovative feed additives. Furthermore, strategies aimed at the attenuation of environmental impact are being refined via the principles of circular economy and advanced waste management protocols.

This Special Issue seeks to provide a comprehensive overview of the tools and knowledge that are preparing the sector for a more productive, responsible, and sustainable future.

Dr. Iván Agea
Dr. María-José Argente
Prof. Dr. María-Luz García
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 250 words) can be sent to the Editorial Office for assessment.

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

  • efficiency
  • sustainability
  • animal welfare
  • precision livestock farming
  • artificial intelligence

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Published Papers (1 paper)

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Research

22 pages, 5101 KB  
Article
Application of Supervised Machine Learning Techniques and Digital Image Analysis for Predicting Live Weight in Anadolu-T Broilers
by Erdem Küçüktopçu, Bilal Cemek, Didem Yıldırım, Halis Simsek, Kadir Erensoy and Musa Sarıca
Animals 2026, 16(1), 68; https://doi.org/10.3390/ani16010068 - 25 Dec 2025
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Abstract
Accurate estimation of live weight is essential for efficient management and precision control in poultry production. This study evaluated the potential of supervised machine learning (ML) algorithms and digital image analysis for non-invasive prediction of live weight in Anadolu-T broilers, a locally developed [...] Read more.
Accurate estimation of live weight is essential for efficient management and precision control in poultry production. This study evaluated the potential of supervised machine learning (ML) algorithms and digital image analysis for non-invasive prediction of live weight in Anadolu-T broilers, a locally developed genotype in Türkiye. A total of 4200 records were collected from 100 broilers (50 males and 50 females) over 42 days, including daily measurements of back length, back width, and live weight. Five ML algorithms—Random Forest (RF), k-Nearest Neighbors (KNN), Support Vector Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR)—were trained and validated to estimate live weight based on morphometric traits. Among all algorithms, KNN achieved the highest accuracy (R2 = 0.982, RMSE = 111.509 g, MAPE = 8.205%), followed by RF and XGB, which also produced stable and reliable predictions. The image-based models using log-transformed regression between body surface pixel area and live weight yielded similar accuracy (R2 = 0.989, RMSE = 101.197 g, MAPE = 7.266%), confirming that projected surface area can effectively represent growth progression. The results demonstrate that integrating ML algorithms with digital imaging offers a practical, cost-effective, and non-invasive approach for real-time broiler weight estimation. This approach supports the advancement of precision poultry farming through automated, data-driven growth monitoring. Full article
(This article belongs to the Special Issue New Techniques and Technologies Applicable to Animal Production)
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