Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.3
3.6
Journals
Agriculture
Special Issues
Precision Livestock Farming and Artificial Intelligence for Sustainable Livestock Systems
share announcement

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 "Artificial Intelligence and Digital Agriculture".

Deadline for manuscript submissions: closed (20 September 2025) | Viewed by 12120

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 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. 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

20 pages, 15574 KB  
Article
Temporal Encoding Strategies for YOLO-Based Detection of Honeybee Trophallaxis Behavior in Precision Livestock Systems
by Gabriela Vdoviak and Tomyslav Sledevič
Agriculture 2025, 15(22), 2338; https://doi.org/10.3390/agriculture15222338 - 11 Nov 2025
Cited by 3 | Viewed by 1365
Abstract
Trophallaxis, a fundamental social behavior observed among honeybees, involves the redistribution of food and chemical signals. The automation of its detection under field-realistic conditions poses a significant challenge due to the presence of crowding, occlusions, and brief, fine-scale motions. In this study, we [...] Read more.
Trophallaxis, a fundamental social behavior observed among honeybees, involves the redistribution of food and chemical signals. The automation of its detection under field-realistic conditions poses a significant challenge due to the presence of crowding, occlusions, and brief, fine-scale motions. In this study, we propose a markerless, deep learning-based approach that injects short- and mid-range temporal features into single-frame You Only Look Once (YOLO) detectors via temporal-to-RGB encodings. A new dataset for trophallaxis detection, captured under diverse illumination and density conditions, has been released. On an NVIDIA RTX 4080 graphics processing unit (GPU), temporal-to-RGB inputs consistently outperformed RGB-only baselines across YOLO families. The YOLOv8m model improved from 84.7% mean average precision (mAP50) with RGB inputs to 91.9% with stacked-grayscale encoding and to 95.5% with temporally encoded motion and averaging over a 1 s window (TEMA-1s). Similar improvements were observed for larger models, with best mAP50 values approaching 94–95%. On an NVIDIA Jetson AGX Orin embedded platform, TensorRT-optimized YOLO models sustained real-time throughput, reaching 30 frames per second (fps) for small and 23–25 fps for medium models with temporal-to-RGB inputs. The results showed that the TEMA-1s encoded YOLOv8m model has achieved the highest mAP50 of 95.5% with real-time inference on both workstation and edge hardware. These findings indicate that temporal-to-RGB encodings provide an accurate and computationally efficient solution for markerless trophallaxis detection in field-realistic conditions. This approach can be further extended to multi-behavior recognition or integration of additional sensing modalities in precision beekeeping. Full article
(This article belongs to the Special Issue Precision Livestock Farming and Artificial Intelligence for Sustainable Livestock Systems)
►▼ Show Figures

Figure 1

19 pages, 6537 KB  
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
Cited by 5 | Viewed by 2231
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)
►▼ Show Figures

Figure 1

Review

Jump to: Research

34 pages, 3372 KB  
Review
Death Detection and Removal in High-Density Animal Farming: Technologies, Integration, Challenges, and Prospects
by Yutong Han, Liangju Wang, Wei Jiang and Hongying Wang
Agriculture 2025, 15(21), 2249; https://doi.org/10.3390/agriculture15212249 - 28 Oct 2025
Cited by 2 | Viewed by 1509
Abstract
In high-density commercial farms, the timely detection and removal of dead bodies are essential to maintain the well-being of animals and ensure farm productivity. This review systematically synthesizes 128 published studies, 52 of which are highly related to the death detecting topic, covering [...] Read more.
In high-density commercial farms, the timely detection and removal of dead bodies are essential to maintain the well-being of animals and ensure farm productivity. This review systematically synthesizes 128 published studies, 52 of which are highly related to the death detecting topic, covering diverse animal species and farming scenarios. The review systematically synthesizes existing research on death detection methods, dead body removal systems, and their integration. The death detection process is divided into three key stages: data acquisition, dataset establishment, and data processing. Inspection systems are categorized into fixed and mobile inspection systems, enabling autonomous imaging for death detection. Regarding death removal systems, current research predominantly focuses on hardware design for poultry and aquaculture, but real-farm validation remains limited. Key focuses for future development include enhancing the robustness and adaptability of detection models with high-quality datasets, brainstorming for more feasible designs of removal systems to enhance adaptability to diverse farm conditions, and improving the integration of inspection systems with removal systems to conduct fully automated detection-removal operations. Ultimately, the successful application of these technologies will reduce labor dependence, enhance biosecurity, and support sustainable, high-density large-scale animal farming while ensuring both satisfying production and the welfare of animals. Full article
(This article belongs to the Special Issue Precision Livestock Farming and Artificial Intelligence for Sustainable Livestock Systems)
►▼ Show Figures

Figure 1

36 pages, 744 KB  
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 - 27 Jun 2025
Cited by 10 | Viewed by 5671
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop