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Agriculture
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Application of Intelligent Technologies in Farm Animal Disease, Feeding and...
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Application of Intelligent Technologies in Farm Animal Disease, Feeding and Building Environmental Control

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

Deadline for manuscript submissions: closed (25 May 2026) | Viewed by 11319

Special Issue Editors

Prof. Dr. Liqiang Han

E-Mail Website
Guest Editor
College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450046, China
Interests: cow; goat; veterinary medicine; animal health; disease warning; intelligent diagnosis; image recognition
Dr. Shuqing Han

E-Mail Website
Guest Editor
Agricultural Information Institute, Chinese Academy of Agricultural Sciences(CAAS), Beijing 100081, China
Interests: smart livestock farming; animal health surveillance and early warning; automated phenotyping of livestock; livestock behavior recognition
Dr. Xuemei Nan

E-Mail Website
Guest Editor
Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
Interests: ruminant nutrition; intelligent agricultural equipment; precision agriculture; rumen microbiota; extracellular vesicle

Special Issue Information

Dear Colleagues,

Traditional livestock farming has long faced problems regarding inaccurate environmental control (lagging regulation of temperature, humidity, and harmful gases), a dependence on manual experience for disease prevention and control (leading to high misdiagnosis rates and strong lag), and low feed utilization rates (with waste rates averaging 15%–20%), resulting in low production efficiency, reduced economic benefits, and intensified environmental pollution on farms. The application of intelligent technologies provides technical support that makes it possible to accurately identify the physiological states of individual animals (such as their heart rate, body temperature, and rumination frequency), to regulate breeding environments in real time (by linking ventilation and temperature control systems), and to construct disease warning models.

This Special Issue aims to highlight impactful research and commentary with a focus on using intelligent technologies to improve farm production efficiency and resource utilization, to ensure animal health and welfare, and to build sustainable ecosystems. It will fully embrace inter- and trans-disciplinary studies from multiple disciplines (e.g., animal disease, animal nutrition, environmental sciences, and artificial intelligence).

Research articles will cover a broad range of farm animals including cows, goats, sheep, swine, poultry, and other farmed animals. All types of articles, from original research to opinions and reviews, are welcome.

Prof. Dr. Liqiang Han
Dr. Shuqing Han
Dr. Xuemei Nan
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

  • animal disease
  • animal feeding
  • environmental control
  • smart livestock farming
  • intelligent agricultural equipment
  • animal health surveillance and early warning
  • image recognition
  • livestock behavior recognition

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Published Papers (6 papers)

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Research

Jump to: Review

14 pages, 2334 KB  
Article
Pressure Drop Across Animal Occupied Zone of Dairy Barns Under Multiple Scenarios
by Qianying Yi, El Hadj Moustapha Doumbia, Ali Alaei, David Janke, Thomas Amon and Sabrina Hempel
Agriculture 2026, 16(1), 79; https://doi.org/10.3390/agriculture16010079 - 29 Dec 2025
Viewed by 446
Abstract
In naturally ventilated dairy barns, many questions regarding airflow, indoor air quality, and emissions are still unanswered, often resulting in inaccurate environmental control of the housing. Particularly, limited understanding of the implications of the constantly changing outdoor weather conditions in interaction with the [...] Read more.
In naturally ventilated dairy barns, many questions regarding airflow, indoor air quality, and emissions are still unanswered, often resulting in inaccurate environmental control of the housing. Particularly, limited understanding of the implications of the constantly changing outdoor weather conditions in interaction with the building design and the role of the characteristics of the animals’ movement inside the building enhances uncertainties in the estimation of airflows within and across the barns. Computational fluid dynamics (CFD) have been used in the past to better understand the dynamics of barn climate, but the models are typically too slow to be used for real-time prediction and control. We investigated the effect of animal characteristics (i.e., animal location, orientation, body posture, and dimensions) on the pressure drop in the animal occupied zone considering inlet wind speed from 0.1 m s−1 to 5 m s−1 and wind direction of 0° and 90° in a CFD model. The cow position in general had little impact on the pressure drop at low wind speeds, but became relevant at higher wind speeds. Cows distributed in a more organized alignment showed less airflow resistance, and, therefore, a lower pressure drop and higher air velocities. Moreover, the cow breed affected the pressure drop, with higher withers resulting in a higher pressure drop and air resistance. In contrast, the effects of cow lying–standing ratio on the pressure drop and airflow resistance coefficients were negligible for both investigated wind directions. Our study aims to provide guidance for optimizing parametrizations of the animal occupied zone in order to enhance the speed of simulations without significant loss in model accuracy. In addition, the conclusions drawn from our study may support the adaption of building design and herd management to improve the effectiveness of ventilation concepts of naturally ventilated dairy barns. Full article
(This article belongs to the Special Issue Application of Intelligent Technologies in Farm Animal Disease, Feeding and Building Environmental Control)
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20 pages, 1872 KB  
Article
Aspirin Eugenol Ester Ameliorates Hypothalamic Neuroinflammation and Improves Growth Performance in High-Density-Raised Broilers
by Dongying Bai, Yi Zhang, Xiaodie Zhao, Yanli Wang, Bo Zheng, Xueqing Xiao, Wenrui Zhen, Fangshen Guo, Yushu Zhang, Bingkun Zhang and Yanbo Ma
Agriculture 2026, 16(1), 38; https://doi.org/10.3390/agriculture16010038 - 23 Dec 2025
Viewed by 550
Abstract
High-stocking-density (HD) environments can trigger systemic inflammatory responses, consequently impairing broiler growth. Given the broad anti-inflammatory properties of aspirin eugenol ester (AEE), this study investigated the effects of AEE supplementation on growth performance, immune organ indices, serum immunoglobulin levels, and hypothalamic inflammation-related markers [...] Read more.
High-stocking-density (HD) environments can trigger systemic inflammatory responses, consequently impairing broiler growth. Given the broad anti-inflammatory properties of aspirin eugenol ester (AEE), this study investigated the effects of AEE supplementation on growth performance, immune organ indices, serum immunoglobulin levels, and hypothalamic inflammation-related markers in HD broilers. A total of 528 one-day-old male Arbor Acres (AA) broilers were randomly assigned to four groups: ND, HD, ND-AEE, and HD-AEE (ND, 14 birds/m2; HD, 22 birds/m2), with six replicate cages per treatment group over a 42-day experimental period. The results revealed that AEE significantly improved the growth performance of HD broilers. Immune organ indices, serum immunoglobulin levels, and the expression of spleen inflammatory factors was associated with the organismal inflammatory response, which manifested primarily during the late growth phase. On Day 35, AEE significantly suppressed (p < 0.05) the relative mRNA expression of p21-activated kinase 1 (PAK1) in the hypothalamus of HD broilers. On Day 42, AEE significantly reduced the relative mRNA expression of PAK1, p38 mitogen-activated protein kinase (p38MAPK), cyclooxygenase-2 (COX-2), prostaglandin E synthase 1 (mPGES-1), and interleukin-1β (IL-1β) (p < 0.05), while significantly elevating the relative mRNA expression of growth hormone-releasing hormone (GHRH) (p < 0.05). Collectively, these findings demonstrate that AEE mitigates high-density rearing-induced hypothalamic inflammation and is associated with downregulated mRNA expression of PAK1 and its downstream targets in the p38MAPK/COX-2 axis. This gene expression profile correlates with improved growth and immune function in high-density-stressed broilers, suggesting a potential regulatory link that requires further validation at the protein and functional levels. Full article
(This article belongs to the Special Issue Application of Intelligent Technologies in Farm Animal Disease, Feeding and Building Environmental Control)
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15 pages, 3327 KB  
Article
Distinguishing True from False Estrus in Hanwoo Cows Using Neck-Mounted IMU Sensors: Quantifying Behavioral Differences to Reduce False Positives
by Seong-Jin Kim, Xue-Cheng Jin, Rajaraman Bharanidharan and Na-Yeon Kim
Agriculture 2025, 15(21), 2307; https://doi.org/10.3390/agriculture15212307 - 5 Nov 2025
Cited by 3 | Viewed by 1290
Abstract
This study aimed to characterize behavioral differences between true estrus (TE) and false estrus (FE) in cows using neck-mounted six-axis inertial measurement unit sensors to reduce false positives in automated detection systems. A retrospective analysis was conducted on 1464 validated estrus alerts from [...] Read more.
This study aimed to characterize behavioral differences between true estrus (TE) and false estrus (FE) in cows using neck-mounted six-axis inertial measurement unit sensors to reduce false positives in automated detection systems. A retrospective analysis was conducted on 1464 validated estrus alerts from 414 Hanwoo cows across 13 commercial farms in South Korea. Alerts were classified as TE (625 alerts) or FE (839 alerts) based on comprehensive validation criteria, including standing heat observation, artificial insemination records, ovulation confirmation, and pregnancy outcomes. Mounting activity, rumination time, and lying time were analyzed. True estrus exhibited significantly higher (p < 0.0001) total number of mounts and maximum mounting duration compared to FE over the entire observation period. Notably, the maximum number of mounts per hour was higher (p < 0.0001) in FE before alert generation but higher (p < 0.0001) in TE afterward, with FE declining rapidly. Coefficients of variation for rumination and lying time were significantly higher (p < 0.0001) in TE than in FE, indicating greater behavioral disruption. These findings revealed that secondary behavioral signs exhibit distinct quantitative and temporal patterns between TE and FE, suggesting potential criteria that could be integrated into automated detection algorithms to reduce false-positive rates. Full article
(This article belongs to the Special Issue Application of Intelligent Technologies in Farm Animal Disease, Feeding and Building Environmental Control)
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20 pages, 13462 KB  
Article
An AI-Based System for Monitoring Laying Hen Behavior Using Computer Vision for Small-Scale Poultry Farms
by Jill Italiya, Ahmed Abdelmoamen Ahmed, Ahmed A. A. Abdel-Wareth and Jayant Lohakare
Agriculture 2025, 15(18), 1963; https://doi.org/10.3390/agriculture15181963 - 17 Sep 2025
Cited by 7 | Viewed by 4389
Abstract
Small-scale poultry farms often lack access to advanced monitoring tools and rely heavily on manual observation, which is time-consuming, inconsistent, and insufficient for precise flock management. Feeding and drinking behaviors are critical, as they serve as early indicators of health and environmental issues. [...] Read more.
Small-scale poultry farms often lack access to advanced monitoring tools and rely heavily on manual observation, which is time-consuming, inconsistent, and insufficient for precise flock management. Feeding and drinking behaviors are critical, as they serve as early indicators of health and environmental issues. With global poultry production expanding, raising over 70 billion hens annually, there is an urgent need for intelligent, low-cost systems that can continuously and accurately monitor bird behavior in resource-limited farm settings. This paper presents the development of a computer vision-based chicken behavior monitoring system, specifically designed for small barn environments where at most 10–15 chickens are housed at any time. The developed system consists of an object detection model, created on top of the YOLOv8 model, trained with an imagery dataset of laying hen, feeder, and waterer objects. Although chickens are visually indistinguishable, the system processes each detection per frame using bounding boxes and movement-based approximation identification rather than continuous identity tracking. The approach simplifies the tracking process without losing valuable behavior insights. Over 700 frames were annotated manually for high-quality labeled data, with different lighting, hen positions, and interaction angles with dispensers. The images were annotated in YOLO format and used for training the detection model for 100 epochs, resulting in a model having an average mean average precision (mAP@0.5) metric value of 91.5% and a detection accuracy of over 92%. The proposed system offers an efficient, low-cost solution for monitoring chicken feeding and drinking behaviors in small-scale farms, supporting improved management and early health detection. Full article
(This article belongs to the Special Issue Application of Intelligent Technologies in Farm Animal Disease, Feeding and Building Environmental Control)
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18 pages, 3131 KB  
Article
An Improved Model for Online Detection of Early Lameness in Dairy Cows Using Wearable Sensors: Towards Enhanced Efficiency and Practical Implementation
by Xiaofei Dai, Guodong Cheng, Lu Yang, Yali Wang, Zhongkun Li, Shuqing Han and Jifang Liu
Agriculture 2025, 15(15), 1643; https://doi.org/10.3390/agriculture15151643 - 30 Jul 2025
Cited by 2 | Viewed by 2288
Abstract
This study proposed an online early lameness detection method for dairy cow health management to overcome the inability of wearable sensor-based methods for online detection and low sensitivity to early lameness. Wearable IMU sensors collected acceleration data in stationary and moving states; a [...] Read more.
This study proposed an online early lameness detection method for dairy cow health management to overcome the inability of wearable sensor-based methods for online detection and low sensitivity to early lameness. Wearable IMU sensors collected acceleration data in stationary and moving states; a threshold discrimination module using variance of motion-direction acceleration was designed to distinguish states within 2 s, enabling rapid data screening. For moving-state windowed data, the InceptionTime network was modified with YOLOConv1D and SeparableConv1D modules plus Dropout, which significantly reduced model parameters and helped mitigate overfitting risk, enhancing generalization on the test set. Typical gait features were fused with deep features automatically learned by the network, enabling accurate discrimination among healthy, mild (early) lameness, and severe lameness. Results showed that the online detection model achieved 80.6% dairy cow health status detection accuracy with 0.8 ms single-decision latency. The recall and F1 score for lameness, including early and severe cases, reached 89.11% and 88.93%, demonstrating potential for early and progressive lameness detection. This study improves lameness detection efficiency and validates the feasibility and practical value of wearable sensor-based gait analysis for dairy cow health management, providing new approaches and technical support for monitoring and early intervention on large-scale farms. Full article
(This article belongs to the Special Issue Application of Intelligent Technologies in Farm Animal Disease, Feeding and Building Environmental Control)
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Review

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39 pages, 1232 KB  
Review
Advancements in Intelligent Monitoring Technologies for Behavioral, Physiological, and Biomarker Analysis in Cattle Health: A Review
by Tianyu Li, Ruirui Zhang, Hui Zhao, Linhuan Zhang, Gang Xu, Tongchuan Yi and Weijia Wang
Agriculture 2026, 16(1), 39; https://doi.org/10.3390/agriculture16010039 - 23 Dec 2025
Cited by 2 | Viewed by 1478
Abstract
With the large-scale and intensive development of cattle farming, traditional health monitoring is incompetent for both dairy and beef cattle in commercial and research settings due to high labor costs and poor real-time performance, making intelligent technologies a core solution. This review innovatively [...] Read more.
With the large-scale and intensive development of cattle farming, traditional health monitoring is incompetent for both dairy and beef cattle in commercial and research settings due to high labor costs and poor real-time performance, making intelligent technologies a core solution. This review innovatively integrates three core dimensions—behavioral detection, physiological parameter monitoring, and in vitro substance analysis—filling the gap of single-dimensional summaries and systematically combining technical performance with key deployment considerations (cost, durability, environmental adaptability). Studies show that the detection accuracy of key health indicators generally exceeds 85%, but most technologies face common challenges including animal stress, environmental interference, and complex calibration. Future research should prioritize multimodal data fusion, low-cost sensor development, and anti-interference algorithm optimization. This review provides comprehensive technical references for smart livestock farming, facilitating efficient and sustainable cattle health management. Full article
(This article belongs to the Special Issue Application of Intelligent Technologies in Farm Animal Disease, Feeding and Building Environmental Control)
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