Ji Hoon Lee
ABSTRACT
Various smart farm technologies are currently being developed around the world to enhance agricultural competitiveness. Korea is also speeding up the development of Korean smart farm technology suitable for domestic environment, but it is difficult to develop high-reliability sensors and systems, and has problems such as preventing sensors from failing, so in this paper, environmental data values such as temperature, humidity, carbon dioxide, ammonia, etc. are sensed, refined, and pretreated to derive correlation and heat maps between sensors. This will not only predict the RUL (Remaining Useful Life) of the sensor using machine learning in the future, but also develop a reliable system by detecting failures and errors.
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Cross Ref
- GJ Kim and JD Huh. 2015. Trends and prospects of smart farm technology. Electronics and Telecommunications trends 30, 5 (2015), 1–10.Google Scholar
- Muhammad Hunain Memon, Wanod Kumar, AzamRafique Memon, Bhawani S Chowdhry, Muhammad Aamir, and Pardeep Kumar. 2016. Internet of Things (IoT) enabled smart animal farm. In 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2067–2072.Google Scholar
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