5G utilization for smart farming to enhance productivity of sugarcane plantations in Indonesia
Year: 2025
Type: article
Abstract: In Indonesia, sugarcane as the main commodity in the plantation sector, has a strategic role in contributing to the national economy. However, the current digital technology in the sugarcane plantation sectors is still under utilized. This research aims to identify the potential of Internet of Things (IoT)-based smart farming for sugarcane plantations in Indonesia using Fifth Generation (5G) networks considering the size of the sugarcane plantation area which requires many IoT sensor devices and a fast and stable communication network. The research methods used include systematic literature review and in-depth interviews, qualitative analysis, and design of a proposed network architecture for sugarcane farming. The key findings from the systematic literature review consist of: (1) the availability of 700 MHz frequency spectrum in Indonesia to support the implementation of 5G in smart agriculture, (2) The Indonesian government’s initiatives align with best practices for sugarcane sector development, and (3) the use of IoT and 5G networks could serve as an integral technological framework to realize precise, efficient, and sustainable sugarcane farming. The research results show the feasibility of 5G and Narrowband-IoT (NB-IoT) in sugarcane plantations in Indonesia because of its ability in accommodating many devices with minimal data transfer rates, low power consumption, minimal sensitivity to delays, and economical costs. This study concludes the significance of the use of digital dividend frequency in Indonesia to implement the 5G massive Machine-Type Communications (mMTC) scenario, and proposes the use of 4-layer NB-IoT System Architecture to streamline the monitoring system for sugarcane cultivation.
Source: Discover Sustainability
Institution: National Research and Innovation Agency
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,390
Funder: Badan Riset dan Inovasi Nasional
Award: G3298376887
OpenAlex ID: W4417293200
