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Precision Livestock Farming: New Techniques for Monitoring the Behaviour and...
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Precision Livestock Farming: New Techniques for Monitoring the Behaviour and Welfare of Farm Animal

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2670

Special Issue Editor

Prof. Dr. Danilo Florentino Pereira

E-Mail Website
Guest Editor
Department of Management, Development and Technology, School of Science and Engineering, São Paulo State University (UNESP), Av. Domingos da Costa Lopes, 780., Tupã 17602-496, SP, Brazil
Interests: poultry farming; heat stress; animal welfare assessment; image processing; computer vision; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Welfare is an inherent quality of animals that is difficult to understand. If animals are well, they will be in good health, and will express natural behaviors and their full genetic potential in production. Over the years, industrial production systems have guaranteed adequate food and thermal environment, safety and health for animals; however, they have neglected important conditions for well-being, such as space to express natural behaviors.

Monitoring animal behavior is an arduous task for humans. By monitoring behavior, we have important information about the welfare of animals. The increased capacity and processing speed of modern computers has made it possible to analyze large volumes of varied and disorganized data, develop complex multivariable models, and implement autonomous monitoring systems that record and analyze signals from sensors installed on the farm. All this expanded capacity to study animals in the production environment has made it possible to expand our knowledge about the behavior and well-being of production animals.

I invite you to submit original research articles, reviews and case studies on new animal monitoring systems and new data analysis techniques that contribute to increasing our knowledge about farm animals and enable a more accurate assessment of welfare.

Prof. Dr. Danilo Florentino Pereira
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 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. 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
  • automation
  • big data
  • environmental control
  • image analysis
  • instrumentation
  • Internet of Things (IoT)
  • machine learning
  • remote monitoring
  • smart housing

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Published Papers (3 papers)

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Research

27 pages, 23565 KiB  
Article
CAMLLA-YOLOv8n: Cow Behavior Recognition Based on Improved YOLOv8n
by Qingxiang Jia, Jucheng Yang, Shujie Han, Zihan Du and Jianzheng Liu
Animals 2024, 14(20), 3033; https://doi.org/10.3390/ani14203033 - 19 Oct 2024
Viewed by 695
Abstract
Cow behavior carries important health information. The timely and accurate detection of standing, grazing, lying, estrus, licking, fighting, and other behaviors is crucial for individual cow monitoring and understanding of their health status. In this study, a model called CAMLLA-YOLOv8n is proposed for [...] Read more.
Cow behavior carries important health information. The timely and accurate detection of standing, grazing, lying, estrus, licking, fighting, and other behaviors is crucial for individual cow monitoring and understanding of their health status. In this study, a model called CAMLLA-YOLOv8n is proposed for Holstein cow behavior recognition. We use a hybrid data augmentation method to provide the model with rich Holstein cow behavior features and improve the YOLOV8n model to optimize the Holstein cow behavior detection results under challenging conditions. Specifically, we integrate the Coordinate Attention mechanism into the C2f module to form the C2f-CA module, which strengthens the expression of inter-channel feature information, enabling the model to more accurately identify and understand the spatial relationship between different Holstein cows’ positions, thereby improving the sensitivity to key areas and the ability to filter background interference. Secondly, the MLLAttention mechanism is introduced in the P3, P4, and P5 layers of the Neck part of the model to better cope with the challenges of Holstein cow behavior recognition caused by large-scale changes. In addition, we also innovatively improve the SPPF module to form the SPPF-GPE module, which optimizes small target recognition by combining global average pooling and global maximum pooling processing and enhances the model’s ability to capture the key parts of Holstein cow behavior in the environment. Given the limitations of traditional IoU loss in cow behavior detection, we replace CIoU loss with Shape–IoU loss, focusing on the shape and scale features of the Bounding Box, thereby improving the matching degree between the Prediction Box and the Ground Truth Box. In order to verify the effectiveness of the proposed CAMLLA-YOLOv8n algorithm, we conducted experiments on a self-constructed dataset containing 23,073 Holstein cow behavior instances. The experimental results show that, compared with models such as YOLOv3-tiny, YOLOv5n, YOLOv5s, YOLOv7-tiny, YOLOv8n, and YOLOv8s, the improved CAMLLA-YOLOv8n model achieved increases in Precision of 8.79%, 7.16%, 6.06%, 2.86%, 2.18%, and 2.69%, respectively, when detecting the states of Holstein cows grazing, standing, lying, licking, estrus, fighting, and empty bedding. Finally, although the Params and FLOPs of the CAMLLA-YOLOv8n model increased slightly compared with the YOLOv8n model, it achieved significant improvements of 2.18%, 1.62%, 1.84%, and 1.77% in the four key performance indicators of Precision, Recall, [email protected], and [email protected]:0.95, respectively. This model, named CAMLLA-YOLOv8n, effectively meets the need for the accurate and rapid identification of Holstein cow behavior in actual agricultural environments. This research is significant for improving the economic benefits of farms and promoting the transformation of animal husbandry towards digitalization and intelligence. Full article
(This article belongs to the Special Issue Precision Livestock Farming: New Techniques for Monitoring the Behaviour and Welfare of Farm Animal)
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14 pages, 3115 KiB  
Article
Estimating the Energy Expenditure of Grazing Farm Animals Based on Dynamic Body Acceleration
by Pedro Gonçalves, João Magalhães and Daniel Corujo
Animals 2024, 14(15), 2140; https://doi.org/10.3390/ani14152140 - 23 Jul 2024
Viewed by 717
Abstract
Indirect methods of measuring the energy expenditure of grazing animals using heartbeat variation or accelerometers are very convenient due to their low cost and low intrusiveness, allowing animals to maintain their usual routine. In the case of accelerometers, it is possible to use [...] Read more.
Indirect methods of measuring the energy expenditure of grazing animals using heartbeat variation or accelerometers are very convenient due to their low cost and low intrusiveness, allowing animals to maintain their usual routine. In the case of accelerometers, it is possible to use them to measure activity, as well as to classify animal behavior, allowing their usage in other scenarios. Despite the obvious convenience of use, it is important to evaluate the measurement error and understand the validity of the measurement through a simplistic method. In this paper, data from accelerometers were used to classify behavior and measure animal activity, and an algorithm was developed to calculate the energy expended by sheep. The results of the energy expenditure calculations were subsequently compared with the values reported in the literature, and it was verified that the values obtained were within the reference ranges. Although it cannot be used as a real metering of energy expended, the method is promising, as it can be integrated with other complementary sources of information, such as the evolution of the animal’s weight and ingestion time, thus providing assistance in animals’ dietary management. Full article
(This article belongs to the Special Issue Precision Livestock Farming: New Techniques for Monitoring the Behaviour and Welfare of Farm Animal)
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14 pages, 2015 KiB  
Article
Machine Vision Analysis of Ujumqin Sheep’s Walking Posture and Body Size
by Qing Qin, Chongyan Zhang, Mingxi Lan, Dan Zhao, Jingwen Zhang, Danni Wu, Xingyu Zhou, Tian Qin, Xuedan Gong, Zhixin Wang, Ruiqiang Zhao and Zhihong Liu
Animals 2024, 14(14), 2080; https://doi.org/10.3390/ani14142080 - 16 Jul 2024
Viewed by 753
Abstract
The ability to recognize the body sizes of sheep is significantly influenced by posture, especially without artificial fixation, leading to more noticeable changes. This study presents a recognition model using the Mask R-CNN convolutional neural network to identify the sides and backs of [...] Read more.
The ability to recognize the body sizes of sheep is significantly influenced by posture, especially without artificial fixation, leading to more noticeable changes. This study presents a recognition model using the Mask R-CNN convolutional neural network to identify the sides and backs of sheep. The proposed approach includes an algorithm for extracting key frames through mask calculation and specific algorithms for head-down, head-up, and jumping postures of Ujumqin sheep. The study reported an accuracy of 94.70% in posture classification. We measured the body size parameters of Ujumqin sheep of different sexes and in different walking states, including observations of head-down and head-up. The errors for the head-down position of rams, in terms of body slanting length, withers height, hip height, and chest depth, were recorded as 0.08 ± 0.06, 0.09 ± 0.07, 0.07 ± 0.05, and 0.12 ± 0.09, respectively. For rams in the head-up position, the corresponding errors were 0.06 ± 0.05, 0.06 ± 0.05, 0.07 ± 0.05, and 0.13 ± 0.07, respectively. The errors for the head-down position of ewes, in terms of body slanting length, withers height, hip height, and chest depth, were recorded as 0.06 ± 0.05, 0.09 ± 0.08, 0.07 ± 0.06, and 0.13 ± 0.10, respectively. For ewes in the head-up position, the corresponding errors were 0.06 ± 0.05, 0.08 ± 0.06, 0.06 ± 0.04, and 0.16 ± 0.12, respectively. The study observed that sheep walking through a passage exhibited a more curved knee posture compared to normal measurements, often with a lowered head. This research presents a cost-effective data collection scheme for studying multiple postures in animal husbandry. Full article
(This article belongs to the Special Issue Precision Livestock Farming: New Techniques for Monitoring the Behaviour and Welfare of Farm Animal)
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