Context-aware self-powered intelligent soil monitoring system for precise agriculture

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.

Authors

Institutions

Publication Details

Journal
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering
Published
2024-11-18
DOI
https://doi.org/10.11591/ijece.v15i1.pp1123-1131
Citations
3
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
1.80
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Context-aware self-powered intelligent soil monitoring system for precise agriculture

Ramli Rashidi, Keh-Kim Kee, Andrew Han, Owen Kwong-Hong Kee et al.
3 citations
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering
Smart Agriculture and AI
1.80
article

Context-aware self-powered intelligent soil monitoring system for precise agriculture

Ramli Rashidi, Keh-Kim Kee, Andrew Han, Owen Kwong-Hong Kee, Isaiah Zunduvan Patrick, Loreena Michelle Bawen
article en
3 citations

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.

International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer EngineeringVol. 15(1)
University College of Technology (CM), University of Technology Sarawak
Zero hunger
Openalex Percentile: Top 13%
Smart Agriculture and AI
1.80
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Context-aware self-powered intelligent soil monitoring system for precise agriculture — Ramli Rashidi, Keh-Kim Kee, et al. · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (2024) | TGRS Research Map | TGRS