Journal of Robotics Spectrum

An Evaluation of Smart Livestock Feeding Strategies

Journal of Robotics Spectrum

Received On : 25 January 2023

Revised On : 28 February 2023

Accepted On : 05 March 2023

Published On : 16 March 2023

Volume 01, 2023

Pages : 066-077


The wasteful utilization of feeds is associated with a decrease in profitability. As the demand for feed increases in the future and the competition between food, feed, and fuel intensifies, it is anticipated that there will be significant environmental and social ramifications. The increasing demand for cattle products has given rise to various social, economic, and ecological concerns. This article examines various feeding techniques, encompassing the utilization of smart technology. The implementation of digital technology has facilitated the adoption of a farming technique known as "smart livestock feeding," which ensures the provision of nutritionally balanced food to animals. The result is the production of animals that exhibit improved health conditions and require reduced amounts of both sustenance and medical attention. Farmers can enhance their profits from the trade of leaner and more efficient cattle through the reduction of costs. The significance of this issue arises from the challenges faced by numerous farms worldwide, including factors such as disease outbreaks and insufficient availability of animal feed. The practice of intelligently feeding cattle incorporates advanced technologies such as predictive analytics, big data, and Internet of Things (IoT), information and communication technology (ICT), artificial intelligence, and genomics.


Smart Livestock Feeding, Scheduled Feeding, Limit Feeding, Full Feeding, Free Access Feeding, Supplemental Feeding.

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Author(s) thanks to Dr.Yoni Danieli for this research completion and support.


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Cite this article

Sim Sze Yin and Yoni Danieli, “An Evaluation of Smart Livestock Feeding Strategies”, Journal of Robotics Spectrum, vol.1, pp. 066-077, 2023. doi: 10.53759/9852/JRS202301007.


© 2023 Sim Sze Yin and Yoni Danieli. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.