Farm Animals’ Behaviors and Welfare Analysis with IA Algorithms: A Review

Debauche, Olivier; Elmoulat, Meryem; Mahmoudi, Saïd; Bindelle, Jérôme; Lebeau, Frédéric

doi:10.18280/ria.350308

Cited by 11 publications

(9 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…https://doi.org/10.17221/172/2022-CJAS In PLF, AI applications are mainly aimed at animal welfare and livestock farming. A previous study reviewed the use of AI algorithms to improve animal behaviour and welfare (Debauche et al 2021). PLF aims to continuously and automatically monitor and improve the animal health, animal behaviour and welfare, productivity, and environmental impacts using sensing technologies, big data, image analysis, and global positioning signals (GPS).…”

Section: Climate-smart Livestock Systemmentioning

confidence: 99%

Application strategy for sustainable livestock production with farm animal algorithms in response to climate change up to 2050: A review

Park¹

2022

CZECH J ANIM SCI

View full text Add to dashboard Cite

Global warming caused by climate change can increase heat stress and greenhouse gas (GHG) emissions, leading to food problems and livestock crises. Thus, pre-emptive responses are required to mitigate the food problems and livestock crises. The potential of a livestock crisis caused by global warming highlights the need for sustainable livestock production in response to climate change using a farm animal algorithm in order to address the population increase and avoid food problems in the future. In particular, the demand for animal-based foods has increased. Such a climate change threatens the livestock environment, production, reproductive efficiency, animal behaviour and welfare, while increasing the heat stress, livestock malodours, and GHG emissions. For these reasons, it is necessary to understand the concurrent mechanisms related to these effects of global warming, animal nutrition, animal feeding and management, animal heat stress and in ovo injection, and carbon neutral livestock. Climate-smart livestock systems are being implemented to overcome the livestock crisis caused by climate change and to maintain sustainable livestock production. This review emphasises the importance of sustainable livestock production using farm animal algorithms in response to a future livestock crisis caused by climate change in 2050.

show abstract

Section: Climate-smart Livestock Systemmentioning

confidence: 99%

Application strategy for sustainable livestock production with farm animal algorithms in response to climate change up to 2050: A review

Park¹

2022

CZECH J ANIM SCI

View full text Add to dashboard Cite

show abstract

“…Concretely, these sensors generate data related to physiological or behavioural parameters of livestock [35]. For instance, various data related to animal behaviour such as resting, ruminating, feeding, and walking habits can be analysed, and trends related to their health can be obtained [36]. BD can also provide supports with feed and disease management.…”

Section: Big Data (Bd)mentioning

confidence: 99%

Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry

Echegaray

Hassoun²,

Jagtap

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Meat 4.0 refers to the application the fourth industrial revolution (Industry 4.0) technologies in the meat sector. Industry 4.0 components, such as robotics, Internet of Things, Big Data, augmented reality, cybersecurity, and blockchain, have recently transformed many industrial and manufacturing sectors, including agri-food sectors, such as the meat industry. The need for digitalised and automated solutions throughout the whole food supply chain has increased remarkably during the COVID-19 pandemic. This review will introduce the concept of Meat 4.0, highlight its main enablers, and provide an updated overview of recent developments and applications of Industry 4.0 innovations and advanced techniques in digital transformation and process automation of the meat industry. A particular focus will be put on the role of Meat 4.0 enablers in meat processing, preservation and analyses of quality, safety and authenticity. Our literature review shows that Industry 4.0 has significant potential to improve the way meat is processed, preserved, and analysed, reduce food waste and loss, develop safe meat products of high quality, and prevent meat fraud. Despite the current challenges, growing literature shows that the meat sector can be highly automated using smart technologies, such as robots and smart sensors based on spectroscopy and imaging technology.

show abstract

“…In prior research, animal activity recognition and monitoring was exploited to study various types of animals, spanning from livestock animals [10][11][12][13][14][15] to wild animals [16][17][18]. In the former case, the animal monitoring can (a) optimize the asset management, as the animals can be maintained always within preset "virtual fences", (b) provide insights about the animals' health through tracking the fluctuation on their activity levels, and (c) designate the optimal pastures.…”

Section: Activity Recognitionmentioning

confidence: 99%

Deep Learning Empowered Wearable-Based Behavior Recognition for Search and Rescue Dogs

Kasnesis

Doulgerakis

Uzunidis

et al. 2022

Sensors

View full text Add to dashboard Cite

Search and Rescue (SaR) dogs are important assets in the hands of first responders, as they have the ability to locate the victim even in cases where the vision and or the sound is limited, due to their inherent talents in olfactory and auditory senses. In this work, we propose a deep-learning-assisted implementation incorporating a wearable device, a base station, a mobile application, and a cloud-based infrastructure that can first monitor in real-time the activity, the audio signals, and the location of a SaR dog, and second, recognize and alert the rescuing team whenever the SaR dog spots a victim. For this purpose, we employed deep Convolutional Neural Networks (CNN) both for the activity recognition and the sound classification, which are trained using data from inertial sensors, such as 3-axial accelerometer and gyroscope and from the wearable’s microphone, respectively. The developed deep learning models were deployed on the wearable device, while the overall proposed implementation was validated in two discrete search and rescue scenarios, managing to successfully spot the victim (i.e., obtained F1-score more than 99%) and inform the rescue team in real-time for both scenarios.

show abstract

Farm Animals’ Behaviors and Welfare Analysis with IA Algorithms: A Review

Cited by 11 publications

References 75 publications

Application strategy for sustainable livestock production with farm animal algorithms in response to climate change up to 2050: A review

Application strategy for sustainable livestock production with farm animal algorithms in response to climate change up to 2050: A review

Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry

Deep Learning Empowered Wearable-Based Behavior Recognition for Search and Rescue Dogs

Contact Info

Product

Resources

About