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Improving Livestock Productivity and Sustainability: Use of Sensor Data and Livestock Behavior

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 18927

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Dr. María Dolores Fernández Rodríguez

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Department of Agroforestry Engineering, University of Santiago de Compostela, 27002 Lugo, Spain
Interests: sustainable animal production; smart farming; environmental and animal variables modeling and control; smart farming; agriculture monitoring; animal behavior; agriculture emissions
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Manuel Ramiro Rodríguez Rodríguez

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Department of Agroforestry Engineering, University of Santiago de Compostela, 27002 Lugo, Spain
Interests: environmental and animal variables modeling and control; sustainability and energy efficiency; smart farming; agriculture monitoring; precision farming; livestock management; animal behavior; agriculture emissions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Animal production is a basic activity for the goal of supplying food to a growing global population. This activity involves multiple aspects in terms of productivity, animal health and welfare, and the environment. Within this complex system, farms must find a balance between sustainability and productivity. Respecting the environment is a requirement to ensure the future of a production that must also be economically profitable, while providing dignified jobs in adequate working conditions. Furthermore, sectorial legislation is becoming stricter, and consumers are increasingly demanding, which requires products to come from animals that have lived in optimal conditions. In order to produce with welfare conditions, it is extremely useful to have access to reliable and objective data regarding animals and the environment in which they are housed. The relevant topics for this Special Issue include new information and communication technologies, equipment for data measurement, acquisition and management, and any sensors or equipment that obtain objective information, needed in order to optimize animal production while respecting animal welfare and the environment.

Dr. María Dolores Fernández Rodríguez
Prof. Dr. Manuel Ramiro Rodríguez Rodríguez
Guest Editors

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Published Papers (11 papers)

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Research

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14 pages, 5746 KiB

Open AccessArticle

Deep Learning-Based Automated Approach for Determination of Pig Carcass Traits

by Jiacheng Wei, Yan Wu, Xi Tang, Jinxiu Liu, Yani Huang, Zhenfang Wu, Xinyun Li and Zhiyan Zhang

Animals 2024, 14(16), 2421; https://doi.org/10.3390/ani14162421 - 21 Aug 2024

Viewed by 686

Abstract

Pig carcass traits are among the most economically significant characteristics and are crucial for genetic selection in breeding and enhancing the economic efficiency. Standardized and automated carcass phenotyping can greatly enhance the measurement efficiency and accuracy, thereby facilitating the selection and breeding of superior pig carcasses. In this study, we utilized phenotypic images and data from 3912 pigs to propose a deep learning-based approach for the automated determination of pig carcass phenotypic traits. Using the YOLOv8 algorithm, our carcass length determination model achieves an average accuracy of 99% on the test set. Additionally, our backfat segmentation model, YOLOV8n-seg, demonstrates robust segmentation performance, with a Mean IoU of 89.10. An analysis of the data distribution comparing manual and model-derived measurements revealed that differences in the carcass straight length are primarily concentrated between −2 cm and 4 cm, while differences in the carcass diagonal length are concentrated between −3 cm and 2 cm. To validate the method, we compared model measurements with manually obtained data, achieving coefficients of determination (R²) of 0.9164 for the carcass straight length, 0.9325 for the carcass diagonal length, and 0.7137 for the backfat thickness, indicating high reliability. Our findings provide valuable insights into automating carcass phenotype determination and grading in pig production. Full article

(This article belongs to the Special Issue Improving Livestock Productivity and Sustainability: Use of Sensor Data and Livestock Behavior)

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22 pages, 4997 KiB

Open AccessArticle

A Sheep Identification Method Based on Three-Dimensional Sheep Face Reconstruction and Feature Point Matching

by Jing Xue, Zhanfeng Hou, Chuanzhong Xuan, Yanhua Ma, Quan Sun, Xiwen Zhang and Liang Zhong

Animals 2024, 14(13), 1923; https://doi.org/10.3390/ani14131923 - 29 Jun 2024

Viewed by 953

Abstract

As the sheep industry rapidly moves towards modernization, digitization, and intelligence, there is a need to build breeding farms integrated with big data. By collecting individual information on sheep, precision breeding can be conducted to improve breeding efficiency, reduce costs, and promote healthy breeding practices. In this context, the accurate identification of individual sheep is essential for establishing digitized sheep farms and precision animal husbandry. Currently, scholars utilize deep learning technology to construct recognition models, learning the biological features of sheep faces to achieve accurate identification. However, existing research methods are limited to pattern recognition at the image level, leading to a lack of diversity in recognition methods. Therefore, this study focuses on the small-tailed Han sheep and develops a sheep face recognition method based on three-dimensional reconstruction technology and feature point matching, aiming to enrich the theoretical research of sheep face recognition technology. The specific recognition approach is as follows: full-angle sheep face images of experimental sheep are collected, and corresponding three-dimensional sheep face models are generated using three-dimensional reconstruction technology, further obtaining three-dimensional sheep face images from three different perspectives. Additionally, this study developed a sheep face orientation recognition algorithm called the sheep face orientation recognition algorithm (SFORA). The SFORA incorporates the ECA mechanism to further enhance recognition performance. Ultimately, the SFORA has a model size of only 5.3 MB, with accuracy and F1 score reaching 99.6% and 99.5%, respectively. During the recognition task, the SFORA is first used for sheep face orientation recognition, followed by matching the recognition image with the corresponding three-dimensional sheep face image based on the established SuperGlue feature-matching algorithm, ultimately outputting the recognition result. Experimental results indicate that when the confidence threshold is set to 0.4, SuperGlue achieves the best matching performance, with matching accuracies for the front, left, and right faces reaching 96.0%, 94.2%, and 96.3%, respectively. This study enriches the theoretical research on sheep face recognition technology and provides technical support. Full article

(This article belongs to the Special Issue Improving Livestock Productivity and Sustainability: Use of Sensor Data and Livestock Behavior)

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15 pages, 4835 KiB

Open AccessArticle

Analysis and Comparison of New-Born Calf Standing and Lying Time Based on Deep Learning

by Wenju Zhang, Yaowu Wang, Leifeng Guo, Greg Falzon, Paul Kwan, Zhongming Jin, Yongfeng Li and Wensheng Wang

Animals 2024, 14(9), 1324; https://doi.org/10.3390/ani14091324 - 29 Apr 2024

Viewed by 1047

Abstract

Standing and lying are the fundamental behaviours of quadrupedal animals, and the ratio of their durations is a significant indicator of calf health. In this study, we proposed a computer vision method for non-invasively monitoring of calves’ behaviours. Cameras were deployed at four viewpoints to monitor six calves on six consecutive days. YOLOv8n was trained to detect standing and lying calves. Daily behavioural budget was then summarised and analysed based on automatic inference on untrained data. The results show a mean average precision of 0.995 and an average inference speed of 333 frames per second. The maximum error in the estimated daily standing and lying time for a total of 8 calf-days is less than 14 min. Calves with diarrhoea had about 2 h more daily lying time (p < 0.002), 2.65 more daily lying bouts (p < 0.049), and 4.3 min less daily lying bout duration (p = 0.5) compared to healthy calves. The proposed method can help in understanding calves’ health status based on automatically measured standing and lying time, thereby improving their welfare and management on the farm. Full article

(This article belongs to the Special Issue Improving Livestock Productivity and Sustainability: Use of Sensor Data and Livestock Behavior)

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20 pages, 6292 KiB

Open AccessArticle

Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data

by Shuwen Hu, Antonio Reverter, Reza Arablouei, Greg Bishop-Hurley, Jody McNally, Flavio Alvarenga and Aaron Ingham

Animals 2024, 14(2), 301; https://doi.org/10.3390/ani14020301 - 18 Jan 2024

Cited by 2 | Viewed by 2050

Abstract

In this study, we equip two breeds of cattle located in tropical and temperate climates with smart ear tags containing triaxial accelerometers to measure their activity levels across different time periods. We produce activity profiles when measured by each of four statistical features, the mean, median, standard deviation, and median absolute deviation of the Euclidean norm of either unfiltered or high-pass-filtered accelerometer readings over five-minute windows. We then aggregate the values from the 5 min windows into hourly or daily (24 h) totals to produce activity profiles for animals kept in each of the test environments. To gain a better understanding of the variation between the peak and nadir activity levels within a 24 h period, we divide each day into multiple equal-length intervals, which can range from 2 to 96 intervals. We then calculate a statistical measure, called daily differential activity (DDA), by computing the differences in feature values for each interval pair. Our findings demonstrate that patterns within the activity profile are more clearly visualised from readings that have been subject to high-pass filtering and that the median of the acceleration vector norm is the most reliable feature for characterising activity and calculating the DDA measure. The underlying causes for these differences remain elusive and is likely attributable to environmental factors, cattle breeds, or management practices. Activity profiles produced from the standard deviation (a feature routinely applied to the quantification of activity level) showed less uniformity between animals and larger variation in values overall. Assessing activity using ear tag accelerometers holds promise for monitoring animal health and welfare. However, optimal results may only be attainable when true diurnal patterns are detected and accounted for. Full article

(This article belongs to the Special Issue Improving Livestock Productivity and Sustainability: Use of Sensor Data and Livestock Behavior)

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14 pages, 1677 KiB

Open AccessArticle

Determination of Behavioral Changes Associated with Bovine Respiratory Disease in Australian Feedlots

by Brad J. White, Dan R. Goehl, Joe P. McMeniman, Tony Batterham, Calvin W. Booker and Christopher McMullen

Animals 2023, 13(23), 3692; https://doi.org/10.3390/ani13233692 - 29 Nov 2023

Viewed by 1124

Abstract

Accurately identifying bovine respiratory disease is challenging in feedlots, and previous studies suggest behavioral monitoring is important. The study objective was to describe individual differences in physical activity (distance traveled), feeding/watering patterns (proximity to feed and water), and social behavior (average cattle within 3 m) when associated with health status in commercially raised beef cattle during the first 28 days on feed. Data from a previous Australian feedlot study monitoring cattle behavior and associated health outcomes were analyzed. Health status categories were generated for all cattle, and each animal was categorized as known healthy (HLTH), known diseased (SICK), or intermediate/uncertain (INTR). The INTR animals were excluded from the final analysis. Key findings included: differentiation in activity between SICK (n = 138) and HLTH (n = 1508) cattle dependent on time of day, SICK cattle spending more time in water and feeding zones early in the feeding phase (<6 days on feed), SICK cattle spending more time in the water and feeding zone during the overnight hours, and SICK cattle spending more time in groups early in the feeding phase but more time in isolation after the first week on feed. Results illustrate behavioral data were associated with important health outcomes. Full article

(This article belongs to the Special Issue Improving Livestock Productivity and Sustainability: Use of Sensor Data and Livestock Behavior)

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18 pages, 5959 KiB

Open AccessArticle

Monitoring the Effect of Weed Encroachment on Cattle Behavior in Grazing Systems Using GPS Tracking Collars

by Igor L. Bretas, Jose C. B. Dubeux, Jr., Priscila J. R. Cruz, Luana M. D. Queiroz, Martin Ruiz-Moreno, Colt Knight, Scott Flynn, Sam Ingram, Jose D. Pereira Neto, Kenneth T. Oduor, Daniele R. S. Loures, Sabina F. Novo, Kevin R. Trumpp, Javier P. Acuña and Marilia A. Bernardini

Animals 2023, 13(21), 3353; https://doi.org/10.3390/ani13213353 - 28 Oct 2023

Cited by 2 | Viewed by 2243

Abstract

Weed encroachment on grasslands can negatively affect herbage allowance and animal behavior, impacting livestock production. We used low-cost GPS collars fitted to twenty-four Angus crossbred steers to evaluate the effects of different levels of weed encroachment on animal activities and spatial distribution. The experiment was established with a randomized complete block design, with three treatments and four blocks. The treatments were paddocks free of weeds (weed-free), paddocks with weeds established in alternated strips (weed-strips), and paddocks with weeds spread throughout the entire area (weed-infested). Animals in weed-infested paddocks had reduced resting time and increased grazing time, distance traveled, and rate of travel (p < 0.05) compared to animals in weed-free paddocks. The spatial distribution of the animals was consistently greater in weed-free paddocks than in weed-strips or weed-infested areas. The effects of weed encroachment on animal activities were minimized after weed senescence at the end of the growing season. Pasture weed encroachment affected cattle behavior and their spatial distribution across the pasture, potentially impacting animal welfare. Further long-term studies are encouraged to evaluate the impacts of weed encroachment on animal performance and to quantify the effects of behavioral changes on animal energy balance. Full article

(This article belongs to the Special Issue Improving Livestock Productivity and Sustainability: Use of Sensor Data and Livestock Behavior)

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20 pages, 3981 KiB

Open AccessArticle

Unsupervised Domain Adaptation for Mitigating Sensor Variability and Interspecies Heterogeneity in Animal Activity Recognition

by Seong-Ho Ahn, Seeun Kim and Dong-Hwa Jeong

Animals 2023, 13(20), 3276; https://doi.org/10.3390/ani13203276 - 20 Oct 2023

Cited by 1 | Viewed by 1758

Abstract

Animal activity recognition (AAR) using wearable sensor data has gained significant attention due to its applications in monitoring and understanding animal behavior. However, two major challenges hinder the development of robust AAR models: domain variability and the difficulty of obtaining labeled datasets. To address this issue, this study intensively investigates the impact of unsupervised domain adaptation (UDA) for AAR. We compared three distinct types of UDA techniques: minimizing divergence-based, adversarial-based, and reconstruction-based approaches. By leveraging UDA, AAR classifiers enable the model to learn domain-invariant features, allowing classifiers trained on the source domain to perform well on the target domain without labels. We evaluated the effectiveness of UDA techniques using dog movement sensor data and additional data from horses. The application of UDA across sensor positions (neck and back), sizes (middle-sized and large-sized), and gender (female and male) within the dog data, as well as across species (dog and horses), exhibits significant improvements in the classification performance and reduced the domain discrepancy. The results highlight the potential of UDA to mitigate the domain shift and enhance AAR in various settings and for different animal species, providing valuable insights for practical applications in real-world scenarios where labeled data is scarce. Full article

(This article belongs to the Special Issue Improving Livestock Productivity and Sustainability: Use of Sensor Data and Livestock Behavior)

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14 pages, 1478 KiB

Open AccessArticle

Modelling of Animal Activity, Illuminance, and Noise on a Weaned Piglet Farm

by Maria D. Fernández, Roberto Besteiro, Tamara Arango and Manuel R. Rodríguez

Animals 2023, 13(20), 3257; https://doi.org/10.3390/ani13203257 - 19 Oct 2023

Viewed by 1237

Abstract

Measuring animal activity and its evolution in real time is useful for animal welfare assessment. In addition, illuminance and noise level are two factors that can improve our understanding of animal activity. This study aims to establish relationships between animal activity as measured by passive infrared sensors, and both illuminance and noise level on a conventional weaned piglet farm. First, regression models were applied, and then cosine models with three harmonics were developed using least squares with a Generalized Reduced Gradient Nonlinear method. Finally, all the models were validated. Linear models showed positive correlations, with values between 0.40 and 0.56. Cosine models drew clear patterns of daily animal activity, illuminance and noise level with two peaks, one in the morning and one in the afternoon, coinciding with human activity inside the building, with a preference for inactivity at night-time and around midday. Cosine model fitting revealed strong correlations, both in the measurement and validation periods, for animal activity (R = 0.97 and 0.92), illuminance (R = 0.95 and 0.91) and noise level (R = 0.99 and 0.92). The developed models could be easily implemented in animal welfare monitoring systems and could provide useful information about animal activity through continuous monitoring of illuminance or noise levels. Full article

(This article belongs to the Special Issue Improving Livestock Productivity and Sustainability: Use of Sensor Data and Livestock Behavior)

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13 pages, 3817 KiB

Open AccessArticle

Evaluating the Activity of Pigs with Radio-Frequency Identification and Virtual Walking Distances

by Anita Kapun, Felix Adrion and Eva Gallmann

Animals 2023, 13(19), 3112; https://doi.org/10.3390/ani13193112 - 6 Oct 2023

Viewed by 1217

Abstract

Monitoring the activity of animals can help with assessing their health status. We monitored the walking activity of fattening pigs using a UHF-RFID system. Four hundred fattening pigs with UHF-RFID ear tags were recorded by RFID antennas at the troughs, playing devices and drinkers during the fattening period. A minimum walking distance, or virtual walking distance, was determined for each pig per day by calculating the distances between two consecutive reading areas. This automatically calculated value was used as an activity measure and not only showed differences between the pigs but also between different fattening stages. The longer the fattening periods lasted, the less walking activity was detected. The virtual walking distance ranged between 281 m on average in the first fattening stage and about 141 m in the last fattening stage in a restricted environment. The findings are similar to other studies considering walking distances of fattening pigs, but are far less labor-intensive and time-consuming than direct observations. Full article

(This article belongs to the Special Issue Improving Livestock Productivity and Sustainability: Use of Sensor Data and Livestock Behavior)

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Review

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23 pages, 1594 KiB

Open AccessReview

A Literature Review of Modeling Approaches Applied to Data Collected in Automatic Milking Systems

by Laura Ozella, Karina Brotto Rebuli, Claudio Forte and Mario Giacobini

Animals 2023, 13(12), 1916; https://doi.org/10.3390/ani13121916 - 8 Jun 2023

Cited by 4 | Viewed by 1961

Abstract

Automatic milking systems (AMS) have played a pioneering role in the advancement of Precision Livestock Farming, revolutionizing the dairy farming industry on a global scale. This review specifically targets papers that focus on the use of modeling approaches within the context of AMS. We conducted a thorough review of 60 articles that specifically address the topics of cows’ health, production, and behavior/management Machine Learning (ML) emerged as the most widely used method, being present in 63% of the studies, followed by statistical analysis (14%), fuzzy algorithms (9%), deterministic models (7%), and detection algorithms (7%). A significant majority of the reviewed studies (82%) primarily focused on the detection of cows’ health, with a specific emphasis on mastitis, while only 11% evaluated milk production. Accurate forecasting of dairy cow milk yield and understanding the deviation between expected and observed milk yields of individual cows can offer significant benefits in dairy cow management. Likewise, the study of cows’ behavior and herd management in AMSs is under-explored (7%). Despite the growing utilization of machine learning (ML) techniques in the field of dairy cow management, there remains a lack of a robust methodology for their application. Specifically, we found a substantial disparity in adequately balancing the positive and negative classes within health prediction models. Full article

(This article belongs to the Special Issue Improving Livestock Productivity and Sustainability: Use of Sensor Data and Livestock Behavior)

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15 pages, 814 KiB

Open AccessReview

When Everything Becomes Bigger: Big Data for Big Poultry Production

by Giovanni Franzo, Matteo Legnardi, Giulia Faustini, Claudia Maria Tucciarone and Mattia Cecchinato

Animals 2023, 13(11), 1804; https://doi.org/10.3390/ani13111804 - 30 May 2023

Cited by 13 | Viewed by 3461

Abstract

In future decades, the demand for poultry meat and eggs is predicted to considerably increase in pace with human population growth. Although this expansion clearly represents a remarkable opportunity for the sector, it conceals a multitude of challenges. Pollution and land erosion, competition for limited resources between animal and human nutrition, animal welfare concerns, limitations on the use of growth promoters and antimicrobial agents, and increasing risks and effects of animal infectious diseases and zoonoses are several topics that have received attention from authorities and the public. The increase in poultry production must be achieved mainly through optimization and increased efficiency. The increasing ability to generate large amounts of data (“big data”) is pervasive in both modern society and the farming industry. Information accessibility—coupled with the availability of tools and computational power to store, share, integrate, and analyze data with automatic and flexible algorithms—offers an unprecedented opportunity to develop tools to maximize farm profitability, reduce socio-environmental impacts, and increase animal and human health and welfare. A detailed description of all topics and applications of big data analysis in poultry farming would be infeasible. Therefore, the present work briefly reviews the application of sensor technologies, such as optical, acoustic, and wearable sensors, as well as infrared thermal imaging and optical flow, to poultry farming. The principles and benefits of advanced statistical techniques, such as machine learning and deep learning, and their use in developing effective and reliable classification and prediction models to benefit the farming system, are also discussed. Finally, recent progress in pathogen genome sequencing and analysis is discussed, highlighting practical applications in epidemiological tracking, and reconstruction of microorganisms’ population dynamics, evolution, and spread. The benefits of the objective evaluation of the effectiveness of applied control strategies are also considered. Although human-artificial intelligence collaborations in the livestock sector can be frightening because they require farmers and employees in the sector to adapt to new roles, challenges, and competencies—and because several unknowns, limitations, and open-ended questions are inevitable—their overall benefits appear to be far greater than their drawbacks. As more farms and companies connect to technology, artificial intelligence (AI) and sensing technologies will begin to play a greater role in identifying patterns and solutions to pressing problems in modern animal farming, thus providing remarkable production-based and commercial advantages. Moreover, the combination of diverse sources and types of data will also become fundamental for the development of predictive models able to anticipate, rather than merely detect, disease occurrence. The increasing availability of sensors, infrastructures, and tools for big data collection, storage, sharing, and analysis—together with the use of open standards and integration with pathogen molecular epidemiology—have the potential to address the major challenge of producing higher-quality, more healthful food on a larger scale in a more sustainable manner, thereby protecting ecosystems, preserving natural resources, and improving animal and human welfare and health. Full article

(This article belongs to the Special Issue Improving Livestock Productivity and Sustainability: Use of Sensor Data and Livestock Behavior)

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