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Agriculture
Special Issues
Precision Livestock Farming and Artificial Intelligence for Sustainable Livestock Systems
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Precision Livestock Farming and Artificial Intelligence for Sustainable Livestock Systems

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 849

Special Issue Editors

Dr. Javier Plaza

E-Mail Website
Guest Editor
Faculty of Agricultural and Environmental Sciences, University of Salamanca, Avda. Filiberto Villalobos 119, 37007 Salamanca, Spain
Interests: precision livestock farming; animal welfare; sensors; sustainable livestock systems; geotechnologies; animal physiology; reproduction; artificial intelligence
Prof. Dr. Carlos Palacios Riocerezo

E-Mail Website
Guest Editor
Faculty of Agricultural and Environmental Sciences, University of Salamanca, Avda. Filiberto Villalobos 119, 37007 Salamanca, Spain
Interests: precision livestock farming; animal welfare; sensors; sustainable livestock systems; animal physiology; reproduction

Special Issue Information

Dear Colleagues,

Livestock farming is undergoing a significant transformation driven by the ongoing agricultural and livestock technological revolution. In this evolving landscape, Precision Livestock Farming (PLF) has emerged as a groundbreaking paradigm, leveraging advanced sensors, actuators, and data-driven methodologies to enhance farm management and decision-making.

One of the key aspects of PLF is the real-time monitoring of animal behavior, physiology, welfare, and productivity, which plays a crucial role in developing more sustainable livestock systems based on environmental, social, and economic perspectives. The integration of sensor-based technologies, such as wearable sensors, image analysis, and bioacoustics monitoring, allows for the continuous collection of vast amounts of data, which requires advanced computational techniques to process, interpret, and apply this information effectively.

In this context, Artificial Intelligence (AI) is revolutionizing PLF by providing robust tools for data analysis, predictive modeling, and automated decision-making. Machine learning algorithms, deep learning techniques, and computer vision applications enable unprecedented levels of precision in livestock monitoring. These advancements facilitate early disease detection, stress assessment, automated feeding systems, and individualized animal care strategies, ultimately leading to more efficient, resilient, and sustainable livestock production.

This Special Issue aims to bring together innovative research at the intersection of PLF, AI, and data analytics, highlighting the latest innovations in sensor technology, real-time data processing, and smart decision-making frameworks. By fostering interdisciplinary collaboration, this collection of studies will contribute to the development of next-generation livestock farming systems that balance productivity with ethical and environmental considerations.

Dr. Javier Plaza
Prof. Dr. Carlos Palacios Riocerezo
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. Agriculture 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 2600 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
  • artificial intelligence
  • automatization
  • deep learning
  • machine learning
  • sensors
  • computer vision
  • sustainable livestock systems
  • animal welfare
  • animal behavior

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Published Papers (2 papers)

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Research

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19 pages, 6537 KiB  
Article
Multimodal Feature-Driven Deep Learning for the Prediction of Duck Body Dimensions and Weight
by Wenbo Xiao, Qiannan Han, Gang Shu, Guiping Liang, Hongyan Zhang, Song Wang, Zhihao Xu, Weican Wan, Chuang Li, Guitao Jiang and Yi Xiao
Agriculture 2025, 15(10), 1021; https://doi.org/10.3390/agriculture15101021 - 8 May 2025
Viewed by 530
Abstract
Accurate body dimension and weight measurements are critical for optimizing poultry management, health assessment, and economic efficiency. This study introduces an innovative deep learning-based model leveraging multimodal data—2D RGB images from different views, depth images, and 3D point clouds—for the non-invasive estimation of [...] Read more.
Accurate body dimension and weight measurements are critical for optimizing poultry management, health assessment, and economic efficiency. This study introduces an innovative deep learning-based model leveraging multimodal data—2D RGB images from different views, depth images, and 3D point clouds—for the non-invasive estimation of duck body dimensions and weight. A dataset of 1023 Linwu ducks, comprising over 5000 samples with diverse postures and conditions, was collected to support model training. The proposed method innovatively employs PointNet++ to extract key feature points from point clouds, extracts and computes corresponding 3D geometric features, and fuses them with multi-view convolutional 2D features. A Transformer encoder is then utilized to capture long-range dependencies and refine feature interactions, thereby enhancing prediction robustness. The model achieved a mean absolute percentage error (MAPE) of 5.73% and an R2 of 0.953 across seven morphometric parameters describing body dimensions, and an MAPE of 10.49% with an R2 of 0.952 for body weight, indicating robust and consistent predictive performance across both structural and mass-related phenotypes. Unlike conventional manual measurements, the proposed model enables high-precision estimation while eliminating the necessity for physical handling, thereby reducing animal stress and broadening its application scope. This study marks the first application of deep learning techniques to poultry body dimension and weight estimation, providing a valuable reference for the intelligent and precise management of the livestock industry with far-reaching practical significance. Full article
(This article belongs to the Special Issue Precision Livestock Farming and Artificial Intelligence for Sustainable Livestock Systems)
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Review

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38 pages, 672 KiB  
Review
Digital Transition as a Driver for Sustainable Tailor-Made Farm Management: An Up-to-Date Overview on Precision Livestock Farming
by Caterina Losacco, Gianluca Pugliese, Lucrezia Forte, Vincenzo Tufarelli, Aristide Maggiolino and Pasquale De Palo
Agriculture 2025, 15(13), 1383; https://doi.org/10.3390/agriculture15131383 (registering DOI) - 27 Jun 2025
Abstract
The increasing integration of sensing devices with smart technologies, deep learning algorithms, and robotics is profoundly transforming the agricultural sector in the context of Farming 4.0. These technological advancements constitute critical enablers for the development of customized, data-driven farming systems, offering potential solutions [...] Read more.
The increasing integration of sensing devices with smart technologies, deep learning algorithms, and robotics is profoundly transforming the agricultural sector in the context of Farming 4.0. These technological advancements constitute critical enablers for the development of customized, data-driven farming systems, offering potential solutions to the challenges of agricultural intensification while addressing societal concerns associated with the emerging paradigm of “farming by numbers”. The Precision Livestock Farming (PLF) systems enable the continuous, real-time, and individual sensing of livestock in order to detect subtle change in animals’ status and permit timely corrective actions. In addition, smart technology implementation within the housing environment leads the whole farming sector towards enhanced business rentability and food security as well as increased animal health and welfare conditions. Looking to the future, the collection, processing, and analysis of data with advanced statistic methods provide valuable information useful to design predictive models and foster the insight on animal welfare, environmental sustainability, farming productivity, and profitability. This review highlights the significant potential of implementing advanced sensing systems in livestock farming, examining the scientific foundations of PLF and analyzing the main technological applications driving the transition from traditional practices to more modern and efficient farming models. Full article
(This article belongs to the Special Issue Precision Livestock Farming and Artificial Intelligence for Sustainable Livestock Systems)
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