Citation

Maniam, Shamala (2025) Recurrent neural network based integration of multiple sensors in a universal platform for internet of things devices. PhD thesis, Multimedia University.

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Abstract

The global population is experiencing significant growth, leading to a corresponding increase in food demand. In response to the growing population, agriculture (the foundation of human civilisation) has encountered an increasing demand for efficient and sustainable farming practices. Hence, precision farming represents a crucial element in modern agriculture, employing technology to optimise crop growth while minimising environmental impact. Traditional irrigation methods also exhibit inefficiencies (over-watering or under-watering of crops), resulting in reduced productivity and crop failure. Therefore, water management-related issues (a crucial determinant of crop yield) within agriculture were effectively addressed in this study. The proposed solution contained IoT-based sensors, LSTM-RNN machine learning algorithms, a mobile application, and a dashboard to predict and monitor subsurface soil moisture content. Furthermore, this study presented background information concerning the project, its objectives, and the employed methodology. This information effectively offered an overview of the challenges in modern agriculture and demonstrated how it addressed these issues using advanced technology. Overall, this study could serve as a guide to elucidate the significance and potential impact of IoT-based sensors, LSTM-RNN, mobile applications, and dashboards in agriculture. The author of this study also hoped that this project could advance precision agriculture and establish a framework for sustainable and efficient farming practices. Particularly, this study represented a modest advancement in the application of technology to enhance agricultural productivity, sustainability, and environmental stewardship.

Item Type:	Thesis (PhD)
Additional Information:	Call No.: TK5105.8857 .S53 2025
Uncontrolled Keywords:	Internet of things
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	16 Jul 2026 03:57
Last Modified:	16 Jul 2026 03:57
URII:	http://shdl.mmu.edu.my/id/eprint/16372

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