AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (4.7 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Advances in research on pig-specific sensors

Liang YANG1Hui WANG1Ruipeng CHEN1Qingkai SHENG2Zhen YUAN3Benhai XIONG1( )
State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
Institute of Animal Husbandry and Veterinary Medicine, Shandong Academy of Agricultural Sciences,Jinan 250199,China
Shandong Xinji Animal Husbandry Co.,Ltd., Tai'an 271299, China
Show Author Information

Abstract

Traditional pig farming has disadvantages such as high labor costs, low breeding efficiency, and high work intensity, which restrict the development of agricultural modernization. The scale, intensive and digital-oriented breeding model has become an inevitable requirement for the development of precision animal husbandry, and individual farming and health monitoring technology based on professional sensors has become the main direction of research. This paper introduces the research progress of pig-specific sensors from three aspects: behavioural sensors, growth and physiological sensors, and disease detection sensors. The outbreak of African swine fever has increased the demand for sensor technology and accelerated the development of new sensing methods. In assessing the adaptive physiology of livestock, sensor technology is essential for measuring physiological parameters of livestock due to its ability to capture time series of behavioural and physiological data. Biosensors and wearable technologies, based on advanced statistical and computer science methods, are used to predict and assess adaptive responses and resilience of livestock. Real-time analysis of animal body condition data such as sound, images and video can improve the biological indicators of livestock. Future developments in sensor technology will facilitate farmers to gain a comprehensive understanding of the health and welfare of their animals. Sensor devices will gradually move from contact to non-contact, so as to reduce he psychological stress on the animals. In terms of behavioural monitoring, video monitoring avoids the problem of traditional wearable devices affecting animals through long-distance target tracking. In terms of the reliability of individual animal identification algorithms, achieving accurate identification of multiple target individuals will be the focus of research. In terms of the applicability of animal behaviour detection algorithms, the study of the complex behaviour of individual animals will be a key research direction. In terms of individual animal disease prediction, the focus will be on achieving early identification of physiological responses in pigs and improving animal health and welfare. This study introduces the purpose of pig sensors to create value for society by improving animal health and welfare, increasing animal productivity, reducing production costs, and minimizing environmental pollution.

CLC number: S24 Document code: A Article ID: 2096-7217(2023)02-0022-13

References

【1】
【1】
 
 
Journal of Intelligent Agricultural Mechanization
Pages 22-34

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
YANG L, WANG H, CHEN R, et al. Advances in research on pig-specific sensors. Journal of Intelligent Agricultural Mechanization, 2023, 4(2): 22-34. https://doi.org/10.12398/j.issn.2096-7217.2023.02.003

356

Views

0

Downloads

0

Crossref

Received: 06 April 2023
Accepted: 28 April 2023
Published: 15 May 2023
© Journal of Intelligent Agricultural Mechanization (2023)

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)