In this file photo dated Sept 25, 2022, farmers walk on their rice field in An Giang province in central Vietnam. (PHOTO / AFP)

Phuvin Kongsawat used his training as an engineer to build a start-up that delivers artificial intelligence-based solutions to test the quality of rice.

The CEO of the Bangkok-based Easy Rice Digital Technology has no formal background in agriculture but since he grew up in Thailand – the world’s second biggest rice exporter – meant he was familiar with the rice production industry.

Phuvin’s research showed how time-consuming it was to manually inspect samples of rice kernel to determine its variety, quality and moisture content.

The Thai government, for example, gives subsidies for farmers to use drones that spread fertilizers and pesticides on croplands. Using drones is a more precise, faster and safer way to apply farm inputs

Easy Rice offers an AI-powered scanning technology which is not only more accurate than any inspection done with a naked eye but can also analyze a 600-grain sample in five minutes.

You can use AI to address pain points, he said. Easy Rice’s products solve the rice industry’s ‘pain points’ – adulteration of rice varieties and the inaccuracy of rice inspections.

Since Easy Rice launched its products in October 2021, the company has secured over 200 exporters and about 20,000 farmers as clients.

Phuvin said the company is also expanding to Vietnam, another key rice exporter and developing a similar technology that can inspect durian and coffee crops.

Easy Rice is an example of how Southeast Asia is using AI to transform its agriculture sector. The region is not just home to major commodity exporters like Thailand and Vietnam, but also a population of over 600 million that has a growing demand for food.

The members countries of the Association of Southeast Asian Nations, or ASEAN, have been increasingly using AI to build a productive, sustainable and climate-friendly farm sector.

The Thai government, for example, gives subsidies for farmers to use drones that spread fertilizers and pesticides on croplands. Using drones is a more precise, faster and safer way to apply farm inputs.

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In Vietnam, an irrigation system was piloted so that farmers can use smartphones to monitor weather conditions and irrigate their crops.

Siva Kumar Balasundram, associate professor of precision agriculture at the Universiti Putra Malaysia, said AI can resolve labor shortage problems in the plantation industry, as robots can be programmed to do the “dirty, dangerous and difficult” jobs like applying farm inputs on agricultural land

The Philippine-based International Rice Research Institute is using a $2 million grant funding from Google.org so that it can use AI to assess its rice gene bank. This can accelerate the development of high-yielding, climate-resilient varieties.

One of the key projects in Malaysia’s AI roadmap for 2021-25 is to build autonomous robots to harvest oil palms. Malaysia is one of the world’s biggest exporters of vegetable oils.

Siva Kumar Balasundram, associate professor of precision agriculture at the Universiti Putra Malaysia, said AI can resolve labor shortage problems in the plantation industry, as robots can be programmed to do the “dirty, dangerous and difficult” jobs like applying farm inputs on agricultural land.

“We can put all this information into an AI processing platform and then you can actually do that  using robots. That will solve a lot of bottlenecks,” he said.

Balasundram said AI can also ensure that you apply the right amount of fertilizer and pesticide, which will not only boost yields but also production cost as it will cut down wastage.

But AI is not exclusive to land-based agriculture. The ASEAN region is home to over 25,000 islands and its regional economy and culinary culture have been shaped by its maritime environment. This is why AI is also being used to develop the aquaculture industry.

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Singapore’s Marine Aquaculture Center, which breeds Asian sea bass in fish farms, uses a real-time object detection AI model, YOLOv3, to count rotifers. Rotifers are microscopic aquatic animals that are used as live feed in fish farms. Manually counting rotifers from culture samples was time-consuming, but AI has helped to accelerate this process

Singapore’s Marine Aquaculture Center, which breeds Asian sea bass in fish farms, uses a real-time object detection AI model, YOLOv3, to count rotifers. Rotifers are microscopic aquatic animals that are used as live feed in fish farms. Manually counting rotifers from culture samples was time-consuming, but AI has helped to accelerate this process.

A group of Australian and Indonesian researchers are developing a method that uses computer vision and machine-learning tools to map seaweed production using satellite imagery.

“Indonesia is such a large, geographically dispersed country, that collecting accurate data on seaweed production has been a major challenge for the industry, and as a result estimates of the scale of the Indonesian seaweed industry vary significantly,” said Zannie Langford, research fellow at Griffith University in Australia.

The study is funded by Australia’s Department of Foreign Affairs and Trade through its Collaboration for Knowledge, Innovation, and Technology Australia and Indonesia (KONEKSI) platform.

Researchers are conducting the study in the provinces of South Sulawesi, East Nusa Tenggara and Maluku, which are major seaweed producing areas in Indonesia.

Langford said one of the biggest risks for seaweed farmers are extreme changes in ocean conditions, especially high temperatures, and low salinity brought on by high rainfall.

This is why it is important to understand how weather patterns are linked to ocean conditions, how ocean conditions affect seaweed health, and how farmers can manage climate-related risks on their farms.

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She said the project focuses on making technological developments available to members of Indonesia’s government, seaweed industry, seaweed academics and the wider interested community.