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Real-time paddy grain drying and monitoring system using long range-internet of things

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 15 No. 1 (2025)

DOI
10.11591/ijece.v15i1.pp448-454
ISSN
2722-2578, 2088-8708
Published
2025-02-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Grain drying environmental parameters are an important issue throughout the paddy grain production process. A real-time monitoring system requires rapid, online, and accurate measurement results. In the paddy grain drying process, the heated air velocity, temperature, relative humidity, and moisture content have to be carefully monitored and maintained to ensure product quality and safety. This study aimed to propose a real-time paddy grain drying and monitoring system using a long-range internet of things (LoRa-IoT). The real-time monitoring system consisted of sensors, LoRa, and IoT platforms. The LoRa end node and gateway were utilized as a wireless radio communication platform of IoT for long-distance signal transmission. From the experiment, the gateway received data from the end node at a distance of 2 km with a time on air (ToA) of 981 ms. As a result, the proposed monitoring system succeeded in measuring and recording the heated air velocity, temperature, and relative humidity data during the paddy grain drying process from 25% moisture content down to 14%. Regarding moisture content, the accuracy of real-time monitoring information was confirmed with a direct measurement method, resulting in a root mean square error (RMSE) of 6.17%.

Collaboration network of the authors

6 authors · 11 co-authorship links

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  • Tanjungpura University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Ismail YusufTanjungpura University2
Kho Hie KhweeTanjungpura University2

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Oct 2026, last seen 1 Nov 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Real-time paddy grain drying and monitoring system using long range-internet of things</dc:title><dc:creator>Hiendro, Ayong</dc:creator><dc:creator>Syaifurrahman, Syaifurrahman</dc:creator><dc:creator>Wigyarianto, F. Trias Pontia</dc:creator><dc:creator>Husin, Fitriah</dc:creator><dc:description>&lt;jats:p&gt;Grain drying environmental parameters are an important issue throughout the paddy grain production process. A real-time monitoring system requires rapid, online, and accurate measurement results. In the paddy grain drying process, the heated air velocity, temperature, relative humidity, and moisture content have to be carefully monitored and maintained to ensure product quality and safety. This study aimed to propose a real-time paddy grain drying and monitoring system using a long-range internet of things (LoRa-IoT). The real-time monitoring system consisted of sensors, LoRa, and IoT platforms. The LoRa end node and gateway were utilized as a wireless radio communication platform of IoT for long-distance signal transmission. From the experiment, the gateway received data from the end node at a distance of 2 km with a time on air (ToA) of 981 ms. As a result, the proposed monitoring system succeeded in measuring and recording the heated air velocity, temperature, and relative humidity data during the paddy grain drying process from 25% moisture content down to 14%. Regarding moisture content, the accuracy of real-time monitoring information was confirmed with a direct measurement method, resulting in a root mean square error (RMSE) of 6.17%.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2025-02-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v15i1.pp448-454</dc:identifier><dc:identifier>10.11591/ijece.v15i1.pp448-454</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 15 No 1 (2025); 448</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/36464/17954</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>