JAKARTA – The National Research and Innovation Agency (BRIN), through the Data Science and Information Research Center (PRSDI), is developing a radar satellite-based rice monitoring system to support food security.
Furthermore, this technology is being developed to manage agriculture in a modern way. This is deemed necessary to ensure Indonesia's agricultural sector continues to develop and keeps pace with other countries.
Hengki Muradi, a First Research Expert from the PRSDI Digital Government Research Group, presented the use of this technology. He explained that rice growth phase modeling is carried out using machine learning.
BRIN also utilizes data from the Sentinel-1, Sentinel-2, and Landsat-8 radar satellites to classify and predict rice growth phases. The methods used include Random Forest (RF) and Support Vector Machine (SVM).
"The application of supervised machine learning can produce significant results in remote sensing-based rice growth monitoring," Hengki said during a recent visit to the Sumatra Institute of Technology (ITERA) in Lampung, as quoted on Tuesday, September 2.
Hengki also mentioned the integration of optical and radar satellite data and the exploration of deep learning methods. This data collection process was assisted by Google Earth Engine, RGIS, and QGIS.
In line with this, Satrio Adi Priyambada, a Junior Research Expert at PRSDI, also explained other research they are conducting. As part of the Knowledge Data and Engineering Research Group, Satrio is developing specialized research with the Space Research Center at BRIN.
Through this collaboration, the two teams developed Daily F-Layer Critical Frequency (foF2) Value Extraction for use on satellites. This technology will later be used to support radio communication applications and ionospheric monitoring.
"We identified radio frequency ranges that can be used to support HF (high-frequency) communications and space weather monitoring by developing a method for automatically extracting foF2 from FTI imagery," Satrio explained.
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