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Advancements in Robotics and Automation for Precision Livestock and Poultry...
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Advancements in Robotics and Automation for Precision Livestock and Poultry Farming: Prospects and Innovations

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

Deadline for manuscript submissions: 30 October 2026 | Viewed by 3893

Editor

Dr. Guoming Li

E-Mail Website
Guest Editor
Department of Poultry Science and Institute for Artificial Intelligence, University of Georgia, Athens, GA, USA
Interests: precision livestock farming; animal welfare and behavior; smart sensing; applied artificial intelligence; poultry robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Livestock and poultry farming is facing great challenges such as reduced labor, increased labor costs, and increasing public expectations of welfare-friendly products. Robotics and automation have been progressively developed and adopted into animal farming to support precision management, productivity improvement, welfare assessment, behavior monitoring, digital phenotyping, and early intervention. Consequenly, automated solutions provide effective means of improving production efficiency and enhancing animal welfare, as well as increasing the sustainability of animal systems.

The aim of this Special Issue is to invite scholars to solve critical problems in different phases of vertically integrated animal production systems via innovative solutions of robotics and automation. Both original research articles and review papers are welcome.

Dr. Guoming Li
Guest Editor

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-anonymized 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
  • robotics
  • automation
  • path planning
  • soft robotics
  • algorithms
  • sensors
  • end effector
  • collaborative robot
  • kinematics

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Published Papers (4 papers)

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Research

22 pages, 6595 KB  
Article
CVIWM: A Tightly Coupled State Estimation Method for Poultry House Inspection Robots in Structurally Degraded Environments
by Hongfeng Deng, Canhuan Lu, Jiacheng Jiang, Cheng Fang and Tiemin Zhang
Animals 2026, 16(12), 1780; https://doi.org/10.3390/ani16121780 - 9 Jun 2026
Viewed by 274
Abstract
Accurate positioning is essential for inspection robots in caged chicken houses, where long straight corridors, sparse textures, and repetitive structures challenge conventional methods. This paper proposes CVIWM (Coupled Visual-Inertial-Wheel Odometry with Markers), a tightly coupled state estimation method that fuses visual, inertial measurement [...] Read more.
Accurate positioning is essential for inspection robots in caged chicken houses, where long straight corridors, sparse textures, and repetitive structures challenge conventional methods. This paper proposes CVIWM (Coupled Visual-Inertial-Wheel Odometry with Markers), a tightly coupled state estimation method that fuses visual, inertial measurement unit (IMU), wheel odometry (WO), and fiducial marker observations within a factor graph optimization framework. Wheel odometry preintegration suppresses IMU horizontal drift and provides absolute scale, while sparse AprilTag markers (10 m spacing) periodically reset accumulated errors. Experiments in an 80 m corridor of a commercial caged chicken house at 0.116 m/s and 0.232 m/s showed that CVIWM achieves average positioning errors of 2.402 cm and 3.253 cm. This high precision ensured reliable image acquisition (image shift <83 pixels), enabling 95.7% dead hen detection and 98.9% egg detection accuracy. CVIWM offers a low-cost, easy-to-deploy, high-accuracy solution for automated poultry house inspection, supporting smart livestock farming. Full article
(This article belongs to the Special Issue Advancements in Robotics and Automation for Precision Livestock and Poultry Farming: Prospects and Innovations)
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27 pages, 16043 KB  
Article
Ultra-Wideband Radar-Based Sensing Poultry Litter Moisture Content Monitoring System
by Haotang Li, Zhenyu Qi, Tanvir Ahmed, Shungeng Zhang, Sen He, Zi Wang and Guoming Li
Animals 2026, 16(9), 1382; https://doi.org/10.3390/ani16091382 - 30 Apr 2026
Viewed by 866
Abstract
High litter moisture content (LMC) in poultry houses is a primary driver of footpad dermatitis, elevated ammonia emissions, and bacterial proliferation. These conditions directly compromise broiler welfare and productivity. Existing monitoring methods, including oven-drying, contact-based sensors, and near-infrared spectroscopy, suffer from invasiveness, single-point [...] Read more.
High litter moisture content (LMC) in poultry houses is a primary driver of footpad dermatitis, elevated ammonia emissions, and bacterial proliferation. These conditions directly compromise broiler welfare and productivity. Existing monitoring methods, including oven-drying, contact-based sensors, and near-infrared spectroscopy, suffer from invasiveness, single-point limitation, or surface-only measurement. This study investigates ultra-wideband (UWB) impulse radar as a non-contact sensing modality for estimating the LMC of cedar wood shaving bedding under controlled laboratory conditions. A four-phase experimental program was conducted. Phases 1–3 characterized signal–moisture relationships across 0–50% LMC, manure simulant contamination, and bedding structural changes (loose, compacted, caked). Phase 4 tested whether UWB radar can estimate litter LMC when a stationary broiler body obstructs the beam under combined contamination and structural conditions. A progressive feature engineering approach and an SVC-gated mixture-of-experts regression architecture were used to address each confounding factor. Full technical details are provided in the Methods Section. Under clean conditions, the baseline model achieved R2=0.97 and RMSE = 2.48% LMC. Under combined realistic conditions (manure contamination, caked bedding, centered carcass), the full pipeline achieved R2=0.91 and RMSE = 4.53% LMC, with 98.8% bird detection accuracy from the radar signal alone. These laboratory findings suggest that the UWB radar can sense litter moisture through a stationary broiler body. The results support its potential as the sensing core of a non-contact monitoring system for precision poultry farming. Full article
(This article belongs to the Special Issue Advancements in Robotics and Automation for Precision Livestock and Poultry Farming: Prospects and Innovations)
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26 pages, 3215 KB  
Article
A Conformer-Based Time–Frequency Decoupling Network for Pig Vocalization Behavior Classification
by Jianping Wang, Yuqing Liu, Siao Geng, Feng Wei, Haoyu Wu, Yuzhen Song, Yingying Lv, Shugang Li and Qian Li
Animals 2026, 16(9), 1337; https://doi.org/10.3390/ani16091337 - 27 Apr 2026
Viewed by 800
Abstract
Continuous monitoring of pig behavior is essential for timely health management and welfare assessment in commercial production systems. Although vision-based methods have been widely studied, their practical application in commercial barns is often limited by variable lighting, frequent occlusion, and high stocking density. [...] Read more.
Continuous monitoring of pig behavior is essential for timely health management and welfare assessment in commercial production systems. Although vision-based methods have been widely studied, their practical application in commercial barns is often limited by variable lighting, frequent occlusion, and high stocking density. Acoustic sensing offers a non-contact alternative that is independent of lighting conditions; however, reliable behavior classification from pig vocalizations remains challenging in commercial environments because of background noise and temporal variability in sound patterns. In this study, an attention-guided acoustic framework, termed ATF-Conformer, was developed for pig vocalization classification under farm conditions. A five-class vocalization dataset was collected from finishing Landrace pigs and multiparous sows on a commercial farm, including cough, scream, estrus, feeding, and normal behavior sounds. The proposed framework combined spectrogram denoising with interactive attention to enhance behavior-related acoustic information, while a time-frequency-decoupled Conformer encoder was introduced to improve feature representation under noisy conditions. Final classification was performed using mask-based temporal pooling with an additive angular margin Softmax objective. In five-fold grouped cross-validation, ATF-Conformer achieved an accuracy of 97.34% ± 0.42 and outperformed several existing acoustic models across multiple evaluation metrics. A similar accuracy of 97.38% was obtained on an independent test set, indicating stable performance across datasets. These results suggest that the proposed method can support continuous, non-invasive pig vocalization-based behavior monitoring and may assist farm owners or workers in pen-level screening of frequent cough or abnormal vocal events, thereby supporting targeted on-site inspection in precision livestock farming. Full article
(This article belongs to the Special Issue Advancements in Robotics and Automation for Precision Livestock and Poultry Farming: Prospects and Innovations)
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16 pages, 2516 KB  
Article
A Novel Lightweight Deep Learning Model for Boar Sperm Head Detection in Microscopic Images: YOLO11_SRP
by Mingchao Pan, Lin Gao, Zhendong Zhu, Yingqi Li and Mingkang Gao
Animals 2026, 16(2), 258; https://doi.org/10.3390/ani16020258 - 15 Jan 2026
Viewed by 1101
Abstract
Accurate and quantitative detection of boar sperm heads is essential for breeding selection and reproductive management. Manual microscopic counting is time-consuming, labor-intensive, and prone to subjective bias, while existing computer-based algorithms often struggle to recognize sperm cells accurately when they overlap or move [...] Read more.
Accurate and quantitative detection of boar sperm heads is essential for breeding selection and reproductive management. Manual microscopic counting is time-consuming, labor-intensive, and prone to subjective bias, while existing computer-based algorithms often struggle to recognize sperm cells accurately when they overlap or move rapidly in high-magnification microscopic images. This study proposes a lightweight boar sperm detection model, YOLO11_SRP, designed to improve small-object recognition in complex microscopic scenarios. The model integrates a lightweight StarNet backbone, a rectangular self-calibration module for enhanced spatial feature modeling, and an additional low-level detection layer optimized for tiny targets. We evaluated the model on a boar sperm microscopic image dataset and compared it with the standard YOLO11s framework. The results show that YOLO11_SRP achieves an mAP@0.5 of 91.9%, representing a 13.9% improvement over YOLO11s, while simultaneously reducing parameters by 39% and computational cost by 14.1%. These findings demonstrate that YOLO11_SRP provides efficient and accurate sperm detection, supporting the development of efficient and reliable automated sperm analysis pipelines, in which sperm head detection serves as a fundamental preprocessing step. Full article
(This article belongs to the Special Issue Advancements in Robotics and Automation for Precision Livestock and Poultry Farming: Prospects and Innovations)
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