November 13, 2025 | 06:51 pm
TEMPO.CO, Jakarta - Researchers from Indonesia's National Research and Innovation Agency (BRIN) have introduced photonics technology that can detect toxins in plants. This system, which is also based on artificial intelligence, combines sensors, the Internet of Things (IoT), AI, big data, automation, and blockchain.
The developer, Sensus Wijanarko, stated that the innovation aligns with the vision of efficient and sustainable agriculture. "Precision agriculture is part of smart farming that utilizes data and technology for optimal land, water, and plant nutrient management," Wijonarko said in a written statement on Wednesday, November 12, 2025.
According to Wijanarko, Photonics technology can help modern farmers increase agricultural yields. The lead expert researcher at the BRIN Photonics Research Center compared photonics systems with electronics. While electronics use electrons, photonics uses light to control pepper soaking, measure seed hardness, and perform visual detection tasks such as laser distance measurement.
The BRIN team has developed photonics technology to measure the brightness of cardamom and detect toxins in potatoes. Wijanarko is optimistic that this approach can maintain efficiency and safety in the food sector. "We want Indonesian agriculture to move towards being more modern, efficient, and environmentally friendly, without disregarding local wisdom," he expressed.
The Chief Expert Researcher at the BRIN Photonics Research Center, Edi Kurniawan, explained that AI mimics human thinking to perceive and make decisions. "AI is a broad umbrella that encompasses machine learning and deep learning, allowing computers to recognize patterns and classify data with high accuracy," he said.
One of BRIN's studies on deep learning is the classification of 11 mangrove leaf species from Bali. A total of 5,500 images were used for AI training. This research resulted in scientific publications, copyrights, and open datasets in the BRIN repository.
There is also research to detect six types of rice diseases from leaf images. The system processes more than 2,600 images, including field images, to identify the location and type of disease. BRIN researchers also employed AI to categorize coffee beans according to the Specialty Coffee Association of America (SCAA) and local standard SNI 01-2907-2008.
"AI has great potential to support precision agriculture from pre-harvest to post-harvest. However, the successful implementation depends largely on the availability of representative datasets," Edi stated.
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