Machine Learning for Indoor Farming


Eugene, Teo Phum and Lee, Chu Liang and Dambul, Katrina D. (2022) Machine Learning for Indoor Farming. Periodic Research Publication, Faculty of Engineering. (Unpublished)

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In the last few years, continuous food supply remains a concern which may prove to be an issue in the near future. Indoor Farming is one of the methods that have been researched heavily to provide added food supply. However, the indoor farming method is far from optimized. Thus, a lot of research have been done to improve on it. The research that are held were all based on improving the indoor farming through implementing the latest technology into it. Machine Learning is the process of machines making their own perception on data that is being fed. This allows for fast predictions and analysis of data which can be used to further improve indoor farming. This paper presents an example of implementing machine learning into indoor farming parameters in order to make future recommendations.

Item Type: Other
Uncontrolled Keywords: Machine learning, Image preprocessing, indoor farming
Subjects: T Technology > TJ Mechanical Engineering and Machinery > TJ1480-1496 Agricultural machinery. Farm machinery
T Technology > TJ Mechanical Engineering and Machinery > TJ212-225 Control engineering systems. Automatic machinery (General)
Divisions: Faculty of Engineering (FOE)
Depositing User: Assoc. Dr Chee Pun Ooi
Date Deposited: 30 Nov 2022 04:06
Last Modified: 30 Nov 2022 04:06


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