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Advancing Animal Welfare: Precision Livestock Farming Technologies for Monitoring and Preventing Abnormal Behavior

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

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 18111

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Special Issue Editors

Dr. Hao Li

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Guest Editor

Department of Agricultural Structure Environment Engineering, College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100193, China
Interests: livestock environmental engineering; indoor environment and ventilation; precise ventilation of livestock houses; CFD; environmental control system and strategy
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaoshuai Wang

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Guest Editor

College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Interests: ventilation design and environmental control in livestock building; assessment of animal heat stress; precision livestock farming
Special Issues, Collections and Topics in MDPI journals

Dr. Qianying Yi

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Guest Editor

Department of Engineering for Livestock Management, Leibniz Institute for Agricultural Engineering and Bioeconomy, Potsdam, Germany
Interests: design and control of natural ventilation system; Investigation of air movement inside and around buildings; determination of ventilation rate of naturally ventilated livestock buildings; modelling and reducing emissions in livestock buildings
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The welfare of animals has been a concern for centuries. However, it is only in recent decades that researchers have begun to focus on the monitoring and prevention of abnormal animal behavior. With various advances in technology, there are now more opportunities to detect and prevent abnormal animal behavior, which can have a significant impact on animal welfare.
This Special Issue aims to highlight the latest innovations in monitoring and preventing abnormal animal behavior. We hope to showcase cutting-edge research that is advancing our understanding of animal behavior and welfare. The scope of this Special Issue covers a wide range of topics, from the use of CFD for animal environment optimization to the development of wearable sensors for animal behavior tracking.
The articles in this Special Issue will cover a broad range of topics related to the monitoring and prevention of abnormal animal behavior, e.g., precision indoor positioning, environment control, the automatic monitoring of animals, behavior and stress, remote disease diagnosis, and environmental hazard emission control.
We are soliciting papers that showcase innovative research on the monitoring and prevention of abnormal animal behavior. We are particularly interested in papers that demonstrate the use of new technologies and equipment, as well as those that explore the practical implications of research. We welcome original research articles, review articles, and case studies that provide valuable insights into this important topic.

Dr. Hao Li
Dr. Xiaoshuai Wang
Dr. Qianying Yi
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 monthly 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
environmental control
airflow distribution
computational fluid dynamics
animal stress
animal behavior
environmental hazard emission
IoT
deep learning
sensors
computer vision
big data
modelling

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Published Papers (8 papers)

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Research

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18 pages, 6815 KiB

Open AccessArticle

Research on Predictive Control Method of Pigsty Environment Based on Fuzzy Control

by Fengwu Zhu, Yuqing Zhang, Weijian Zhang, Tianshi Gao, Suyu Wang and Lina Zhou

Agriculture 2024, 14(7), 1004; https://doi.org/10.3390/agriculture14071004 - 26 Jun 2024

Viewed by 1056

Abstract

At present, most of the environmental control systems of pigsties use direct control methods; when factors, such as temperature and humidity, exceed the set threshold value, the corresponding actuator is turned on for regulation. However, such methods have problems such as low control accuracy, high energy consumption, and untimeliness. In order to save on energy consumption and improve control accuracy, this paper takes the predicted value, set value, and current detection value of the internal environment of a pigsty as input, and combines fuzzy control and direct control methods to realize the predictive control of the pigsty environment. The test results show that, compared with the direct control method, the fuzzy predictive control method can make fluctuations in the internal temperature and humidity of the pigsty less close to the set threshold value, while the ammonia concentration hardly exceeds the set threshold value. The results show that predictive control can more accurately control the internal environment of the pigsty and reduce energy costs by about 20%. Therefore, this method can provide scientific and effective environmental control methods for agricultural production processes, such as livestock breeding and greenhouse cultivation, in semi-confined spaces. Full article

(This article belongs to the Special Issue Advancing Animal Welfare: Precision Livestock Farming Technologies for Monitoring and Preventing Abnormal Behavior)

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20 pages, 4260 KiB

Open AccessArticle

Development of a Decision Support System for Animal Health Management Using Geo-Information Technology: A Novel Approach to Precision Livestock Management

by Sudhanshu S. Panda, Thomas H. Terrill, Aftab Siddique, Ajit K. Mahapatra, Eric R. Morgan, Andres A. Pech-Cervantes and Jan A. Van Wyk

Agriculture 2024, 14(5), 696; https://doi.org/10.3390/agriculture14050696 - 28 Apr 2024

Viewed by 1731

Abstract

Livestock management is challenging for resource-poor (R-P) farmers due to unavailability of quality feed, limited professional advice, and rumor-spreading about animal health condition in a herd. This research seeks to improve animal health in southern Africa by promoting sericea lespedeza (Lespedeza cuneata), a nutraceutical forage legume. An automated geospatial model for precision agriculture (PA) can identify suitable locations for its cultivation. Additionally, a novel approach of radio-frequency identifier (RFID) supported telemetry technology can track animal movement, and the analyses of data using artificial intelligence can determine sickness of small ruminants. This RFID-based system is being connected to a smartphone app (under construction) to alert farmers of potential livestock health issues in real time so they can take immediate corrective measures. An accompanying Decision Support System (DSS) site is being developed for R-P farmers to obtain all possible support on livestock production, including the designed PA and RFID-based DSS. Full article

(This article belongs to the Special Issue Advancing Animal Welfare: Precision Livestock Farming Technologies for Monitoring and Preventing Abnormal Behavior)

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15 pages, 4491 KiB

Open AccessArticle

A Non-Contact and Fast Estimating Method for Respiration Rate of Cows Using Machine Vision

by Xiaoshuai Wang, Binghong Chen, Ruimin Yang, Kai Liu, Kaixuan Cuan and Mengbing Cao

Agriculture 2024, 14(1), 40; https://doi.org/10.3390/agriculture14010040 - 25 Dec 2023

Cited by 7 | Viewed by 3153

Abstract

Detecting respiration rate (RR) is a promising and practical heat stress diagnostic method for cows, with significant potential benefits for dairy operations in monitoring thermal conditions and managing cooling treatments. Currently, the optical flow method is widely employed for automatic video-based RR estimation. However, the optical flow-based approach for RR estimation can be time-consuming and susceptible to interference from various unrelated cow movements, such as rising, lying down, and body shaking. The aim of this study was to propose a novel optical flow-based algorithm for remotely and rapidly detecting the respiration rate of cows in free stalls. To accomplish this, we initially collected 250 sixty-second video episodes from a commercial dairy farm, which included some episodes with interfering motions. We manually observed the respiration rate for each episode, considering it as the ground truth RR. The analysis revealed that certain cow movements, including posture changes and body shaking, introduced noise that compromises the precision of RR detection. To address this issue, we implemented noise filters, with the Butterworth filter proving highly effective in mitigating noise resulting from cow movements. The selection of the region of interest was found to have a substantial impact on the accuracy of RR detection. Opting for the central region was recommended for optimal results. The comparison between the RR estimated by the modified cow respiration rate (MCRR) algorithm and the ground truth RR showed a good agreement with a mean absolute relative error of 7.6 ± 8.9% and a Pearson correlation coefficient of 0.86. Additionally, the results also indicated that reducing the original frame rate from 25 to 5 frames per second and adjusting the image pixel size from 630 × 450 to 79 × 57 pixels notably reduced computational time from 39.8 to 2.8 s, albeit with a slight increase in mean absolute relative error to 8.0 ± 9.0%. Full article

(This article belongs to the Special Issue Advancing Animal Welfare: Precision Livestock Farming Technologies for Monitoring and Preventing Abnormal Behavior)

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18 pages, 7015 KiB

Open AccessArticle

Assessment of Airflow Patterns Induced by a Retractable Baffle to Mitigate Heat Stress in a Large-Scale Mechanically Ventilated Barn

by Seunghyeon Jung, Hanwook Chung and Christopher Y. Choi

Agriculture 2023, 13(10), 1910; https://doi.org/10.3390/agriculture13101910 - 29 Sep 2023

Cited by 1 | Viewed by 1305

Abstract

In large-scale dairy farming, heat stress remains a primary concern, and cross-ventilated barns have become increasingly prevalent in order to tackle this issue. Such barns employ energy-intensive electrical fans to enhance airflow and regulate temperature. To optimize this system, air baffles are often placed above the animal-occupied zones (AOZ) to direct airflow toward the cows. Although previous studies have suggested that baffles can substantially amplify the system’s cooling effect, the comprehensive impact of baffles on airflow patterns in a full-scale barn is less understood. Traditional measurement techniques, involving physical sensors, are both technically demanding and costly. Moreover, they often fall short in accounting for the dynamic microenvironmental changes induced by fluctuating weather, animal movement, and operational machinery. This study leverages computational fluid dynamics (CFD) to model airflow behaviors within a cross-ventilated barn, specifically examining the influence of a retractable baffle. CFD not only offers a reliable and efficient method for simulations but also allows for accurate assessments by validating the generated data against empirical observations. The results verify that, when properly configured, air baffles can significantly enhance airflow at cows in large barns. Additionally, the study establishes the reliability of CFD for designing large-scale dairy barns. Full article

(This article belongs to the Special Issue Advancing Animal Welfare: Precision Livestock Farming Technologies for Monitoring and Preventing Abnormal Behavior)

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23 pages, 1780 KiB

Open AccessArticle

The Impact of Calf Rearing with Foster Cows on Calf Health, Welfare, and Veal Quality in Dairy Farms

by Paweł Solarczyk, Tomasz Sakowski, Marcin Gołębiewski, Jan Slósarz, Grzegorz Grodkowski, Kinga Grodkowska, Luisa Biondi, Massimiliano Lanza, Antonio Natalello and Kamila Puppel

Agriculture 2023, 13(9), 1829; https://doi.org/10.3390/agriculture13091829 - 18 Sep 2023

Cited by 3 | Viewed by 2036

Abstract

This study assessed the impact of different calf rearing systems on calf health, behavior, meat quality, and oxidative stability. The study involved two groups of bull calves: conventionally penned calves (control, fed with use of automatic feeders) and calves reared alongside foster cows (experimental). The presence of foster cows was found to have a significant positive influence on calf health. Calves raised with foster cows experienced lower rates of diarrhea, delayed instances of coughing, and a reduced occurrence of rhinitis compared to conventionally reared calves. Behavioral observations revealed differences in sucking and licking behaviors between the two groups. Calves with foster cows displayed more consistent patterns of these behaviors, while conventionally reared calves exhibited greater variability. Additionally, the experimental group consistently achieved higher daily weight gains, suggesting the potential for larger and more valuable carcasses at slaughter. Importantly, there were no significant differences in the quality of veal between the two rearing groups. This included fatty acid composition, color attributes, and myoglobin levels, indicating consistent meat quality. In summary, this research highlights the advantages of rearing systems that prioritize calf health and behavior, emphasizing maternal care and natural behaviors. Such systems hold promise for improving calf welfare and enhancing the sustainability of the meat production industry. The integration of foster cows into dairy farming practices emerges as a practical and effective approach, particularly for the rearing of bull calves. Full article

(This article belongs to the Special Issue Advancing Animal Welfare: Precision Livestock Farming Technologies for Monitoring and Preventing Abnormal Behavior)

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14 pages, 7627 KiB

Open AccessArticle

Study of Pose Estimation Based on Spatio-Temporal Characteristics of Cow Skeleton

by Yongfeng Wei, Hanmeng Zhang, Caili Gong, Dong Wang, Ming Ye and Yupu Jia

Agriculture 2023, 13(8), 1535; https://doi.org/10.3390/agriculture13081535 - 1 Aug 2023

Cited by 4 | Viewed by 2108

Abstract

The pose of cows reflects their body condition, and the information contained in the skeleton can provide data support for lameness, estrus, milk yield, and contraction behavior detection. This paper presents an algorithm for automatically detecting the condition of cows in a real farm environment based on skeleton spatio-temporal features. The cow skeleton is obtained by matching Partial Confidence Maps (PCMs) and Partial Affinity Fields (PAFs). The effectiveness of skeleton extraction was validated by testing 780 images for three different poses (standing, walking, and lying). The results indicate that the Average Precision of Keypoints (APK) for the pelvis is highest in the standing and lying poses, achieving 89.52% and 90.13%, respectively. For walking, the highest APK for the legs was 88.52%, while the back APK was the lowest across all poses. To estimate the pose, a Multi-Scale Temporal Convolutional Network (MS-TCN) was constructed, and comparative experiments were conducted to compare different attention mechanisms and activation functions. Among the tested models, the CMS-TCN with Coord Attention and Gaussian Error Linear Unit (GELU) activation functions achieved precision, recall, and F1 scores of 94.71%, 86.99%, and 90.69%, respectively. This method demonstrates a relatively high detection rate, making it a valuable reference for animal pose estimation in precision livestock farming. Full article

(This article belongs to the Special Issue Advancing Animal Welfare: Precision Livestock Farming Technologies for Monitoring and Preventing Abnormal Behavior)

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25 pages, 7138 KiB

Open AccessArticle

Design of a Closed Piggery Environmental Monitoring and Control System Based on a Track Inspection Robot

by Yuhao Li, Chengguo Fu, Hui Yang, Haibo Li, Rongxian Zhang, Yaqi Zhang and Zhankui Wang

Agriculture 2023, 13(8), 1501; https://doi.org/10.3390/agriculture13081501 - 27 Jul 2023

Cited by 7 | Viewed by 2861

Abstract

To improve environmental quality in enclosed piggeries, a monitoring and control system was designed based on a track inspection robot. The system includes a track mobile monitoring platform, an environmental control system, and a monitor terminal. The track mobile monitoring platform consists of three main components: a single-track motion device, a main box containing electronic components, and an environmental sampling device. It is capable of detecting various environmental parameters such as temperature, humidity, NH₃ concentration, CO₂ concentration, light intensity, H₂S concentration, dust concentration, and wind speed at different heights below the track. Additionally, it can control on-site environmental control equipment such as lighting systems, ventilation systems, temperature control systems, and manure cleaning systems. The networked terminal devices enable real-time monitoring of field equipment operating status. An adaptive fuzzy PID control algorithm is embedded in the system to regulate the temperature of the piggery. Field tests conducted on a closed nursery piggery revealed that the system effectively controlled the maximum temperature range within 2 °C. The concentrations of CO₂, NH₃, and PM2.5 were maintained at a maximum of 1092 mg∙m⁻³, 16.8 mg∙m⁻³, and 35 μg∙m⁻³, respectively. The light intensity ranged from 51 to 57 Lux, while the wind speed remained stable at approximately 0.35 m∙s⁻¹. The H₂S concentration was significantly lower than the standard value, and the lowest relative humidity recorded was 18% RH at high temperatures. Regular humidification is required in closed piggeries and other breeding places when the system does not trigger the wet curtain humidification and cooling function, as the relative humidity is lower than the standard value. By controlling the temperature, the system combined with a humidification device can meet environmental requirements. The control method is simple and effective, with a wide range of applications, and holds great potential in the field of agricultural environmental control. Full article

(This article belongs to the Special Issue Advancing Animal Welfare: Precision Livestock Farming Technologies for Monitoring and Preventing Abnormal Behavior)

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Review

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16 pages, 822 KiB

Open AccessReview

Environmental Factor Detection and Analysis Technologies in Livestock and Poultry Houses: A Review

by Fei Qi, Xuedong Zhao, Zhengxiang Shi, Hao Li and Wanying Zhao

Agriculture 2023, 13(8), 1489; https://doi.org/10.3390/agriculture13081489 - 27 Jul 2023

Cited by 8 | Viewed by 2771

Abstract

The environment in livestock and poultry houses plays an important role in the growth and reproduction of livestock and poultry. In order to obtain the environmental conditions of livestock and poultry houses in a timely and reliable manner, and eliminate adverse environmental factors, scholars have been exploring various methods to obtain and predict environmental factors. This paper reviewed the literature from the last 10 years, specifically focusing on technologies for detecting environmental factors in livestock and poultry houses, which can be mainly divided into three categories: research on the environmental monitoring and control of livestock and poultry houses based on detection equipment and wireless sensor technology; research on the distribution and regularity of environmental factors in livestock and poultry houses based on a mathematical model; research on the environmental simulation and detection of livestock and poultry houses based on computer technology. The current testing methods have their advantages and disadvantages. When studying environmental factors, researchers should choose the most appropriate method for data acquisition according to the actual situation. The proposed recommendations for achieving this goal are as follows: (1) The control of environmental factors should be combined with the physiological response of livestock and poultry. The needs of animals should be considered; (2) Novel approaches need to be developed to integrate energy requirements into the environmental regulation of livestock and poultry houses; (3) It is necessary to research and develop control models and strategies that can predict the environment in the houses, and the transient simulation method should be further explored; (4) Improve environmental detection and control accuracy through the coupling of different technologies. Full article

(This article belongs to the Special Issue Advancing Animal Welfare: Precision Livestock Farming Technologies for Monitoring and Preventing Abnormal Behavior)

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