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All Journal International Journal of Electrical and Computer Engineering
Rashidi, Ramli
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Computer Science & IT Electrical & Electronics Engineering

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Context-aware self-powered intelligent soil monitoring system for precise agriculture Kee, Keh-Kim; Rashidi, Ramli; Kee, Owen Kwong-Hong; Han, Andrew Ballang; Patrick, Isaiah Zunduvan; Bawen, Loreena Michelle
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1123-1131

Abstract

The agricultural sector is transforming with advanced technologies such as internet of things (IoT), cloud computing, and machine learning, for increased productivity and sustainability. However, fixed sensor deployments struggle to capture the dynamic and heterogeneous soil properties with irregularities in farming operations, and negatively impacting crop performance and resource utilization. This paper presents a novel context-aware, self-powered intelligent soil monitoring system (ISMS) applied in precision agriculture. By integrating advanced sensors, energy harvesting, real-time data analytics, and context-aware decision support, ISMS provides real-time context insights into soil, energy, and weather conditions. The informed decisions are enabled and tailored to their specific agricultural environment. The system utilizes a multi-parameter soil sensor, photovoltaic (PV) panel, and intelligent context-aware analytics for a sustainable, cost-effective solution powered by solar energy and OpenWeather application program interface (API) for weather data. Field tests over two months demonstrated the system's effectiveness, together with continuous operation without grid power. This research highlights ISMS's potential in enhancing soil nutrient management and decision-making and offering significant economic and environmental benefits for modern agriculture, especially in remote areas.
Assistive tool of energy metering system for power utility companies Kee, Keh-Kim; Rashidi, Ramli; Ting, Huong-Yong; Hsiung, Lo Tzu; Kee, Owen Kwong-Hong; Zheng, Yeo Hong; Ini, Michelle Anak
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp577-586

Abstract

The growing demand for electricity and the complexity of power quality management highlight the need for advanced energy monitoring systems. Existing solutions often could not provide the real-time, detailed data necessary for smart grids, smart cities, and Industrial 4.0. They also fail to monitor power quality effectively, avoid equipment damage and ensure safety. To address this, we developed an internet of things (IoT)-based tool that leverages standard energy meters. The system monitors and analyzes electrical energy consumption and its power quality in real-time. The system adopts a multi-layered IoT architecture, where fog computing handles immediate data processing and the cloud computing supports machine learning for power quality detection. In this work, measurement accuracy is validated against a commercial power multimeter, achieving mean absolute percentage error (MAPE) values below 1.0% across different appliances. A companion web portal allows for real-time data visualization, time-series analysis, remote control of appliances and power quality detection that comply with IEC and IEEE standards. The proposed system is scalable and user-friendly, offering a practical smart metering solution for modern energy management. It aligns with the needs of smart grids and smart cities, contributing to efficient and intelligent energy consumption in the context of Industry 4.0.
Co-Authors Bawen, Loreena Michelle Han, Andrew Ballang Hsiung, Lo Tzu Ini, Michelle Anak Kee, Keh-Kim Kee, Owen Kwong-Hong Patrick, Isaiah Zunduvan Ting, Huong-Yong Zheng, Yeo Hong