Development of Effective Cattle Health Monitoring System based on Biosensors

Park, Myeong-Chul; Ha, Ok-Kyoon

doi:10.14257/astl.2015.117.42

Cited by 9 publications

(5 citation statements)

References 3 publications

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“…Detailed observation by AI-powered image analysis could enable early detection of injuries and illnesses (Singh, 2019). A variety of devices, such as automated tools that integrate audio-andvideo-captured data for early disease detection and warning systems have been used by Park and Ha (2015).…”

Section: Livestock Farm Monitoringmentioning

confidence: 99%

‘Artificial Intelligence’ as an Intelligent Tool for the Future Animal Production and Better Management: A Review

Priya

Dutt

Singh

et al. 2022

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With the tremendous increase in human population the demand of milk and meat is also increasing. This necessitates the centralization of animal husbandry sector both in developed and developing countries. More production has to be taken from fewer animals which seem to be possible only with the usage of advanced technologies. Earlier this seems to be impractical due to non-availability of such technologies to the farmers. Nowadays, computational power is very cheap and easily available. Artificial intelligence is one such field of computer science devoted to creating computing machines and systems that perform operations analogous to human learning and decision-making. Artificial intelligence includes the machine learning, deep learning and predictive analytics. With the implementation of various software and algorithms, the production has been increased from the farm animals along with better management and welfare. This paper illustrates the application domains and maneuvering of artificial intelligence in the animal husbandry sector.

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Section: Livestock Farm Monitoringmentioning

confidence: 99%

‘Artificial Intelligence’ as an Intelligent Tool for the Future Animal Production and Better Management: A Review

Priya

Dutt

Singh

et al. 2022

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“…Myeong et al [7] proposed an effective livestock monitoring system (LMS) using biosensors for cattle health monitoring systems. The monitoring system aims to collect biometric data directly associated with the diseases from an individual entity and prevent them from occurring or spreading.…”

Section: Zigbee-based Animal Health Solutionsmentioning

confidence: 99%

Wireless Sensor Networks-Based Solutions for Cattle Health Monitoring: A Survey

Gameil

Gaber

2019

Advances in Intelligent Systems and Computing

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Wireless Sensor Networks (WSN) are nowadays becoming an active research in different fields. Precise irrigation, agriculture, earthquake, fire monitoring in forests and animal health monitoring are few applications of WSN. Animal health monitoring systems (AHMS) are usually used to monitor physiological parameters such as rumination, heart rate, and body temperature. Traditional methods to monitor animal health such as (traditional surveillance, single observation, and simple tabular and graphic techniques) are not efficient to achieve high performance in the large herds' management systems. These methods can only provide partial information and introduce a large cost in staffing and physical hardware. Thus, it is of important need to overcome a foresaid drawback by using alternative low cost, low power consumption sensor nodes, and providing real-time communications at a sensible hardware cost. The objectives of this paper are: reviewing existing WSN solutions for cattle health monitoring models and determining the requirements needed for building an effective WSN model suitable for cattle health monitoring and detect animal diseases. From this review, requirements of the effective WSN-based solution for cattle health monitoring were suggested.

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“…Such systems have the ability to change the landscape of animal farming by providing comprehensive information on animal wellbeing. These systems are also able to rapidly identify livestock diseases and prevent economic losses stemming from loss of productivity (Park and Ha, 2015).…”

Section: Biosensors For Animal Diseasesmentioning

confidence: 99%