Umitron Pte. Ltd. has launched its Fish Appetite Index (FAI), a real-time ocean-based fish appetite detection system that uses efficient machine learning and image analysis techniques to extract relevant data that are then be used to accurately quantify fish appetite.

Umitron said the software has already been rolled out to existing customers to optimize their fish feeding operations.

Over the past 20 years, the aquaculture industry has been expanding at an exponential rate, with annual production tripling during this brief time span, Umitron said. At the same time, aquaculture feed prices have also risen dramatically.

This presents an ever-growing challenge for farmers since feed costs account for a majority of their operational overhead. Umitron said fish farmers must walk a tight line: Underfeeding their fish risks lower growth rates and slower time to market, while overfeeding increases costs and potentially harms the environment.

New data analytics technologies such as Internet of Things devices and machine learning offer farmers a solution to improve their feeding operations, the company said.

The new FAI algorithm takes in the same visual information that people would and then scores fish appetite and presents it in an easy-to-understand chart. When used in tandem with a smart feeder, the time intervals between feedings and amounts fed can be automatically adjusted with minimal human interference. Farm operators can utilize FAI to fine-tune their feeding schedules, ensuring that fish are always satiated. This is easily done via smartphones, where operators can check and remotely adjust feed settings based on the FAI feedback.

FAI benefits farmers by reducing wasted feed and improving profitability as well as environmental sustainability. Using FAI in combination with advance feeding technologies will allow farmers to stay onshore during dangerous weather conditions or holidays while still keeping a close eye on their fish stocks. Furthermore, it reduces the need for every employee to be an expert at feeding and instead frees workers to focus on other tasks that improve fish welfare.

"Today, there are many companies developing machine learning algorithms for a variety of industries but only testing them under ideal conditions. The Umitron Fish Appetite Index, on the other hand, is already being embraced by our existing customers at their ocean-based farm sites, where it operates under real-world conditions. It might be difficult for some of our potential customers to completely trust artificial intelligence at first, but FAI is an important tool that can be used to increase productivity and reduce waste," Umitron managing director Masahiko Yamada said.

"Our appetite analysis approach is being developed with customer feedback in mind. Umitron will continue to develop similar value-added software services that can be automatically rolled out to our existing customer base. Also, we are open to developing other practical applications after discussions with potential customers or equipment partners," Umitron chief technology officer Takuma Okamoto added.

Umitron is looking for partners interested in using FAI for species such as Atlantic salmon, rainbow trout, European sea bass and gilthead sea bream. Similarly, Umitron is looking for feeding system manufacturing partners who wish to utilize data analysis software such as FAI to improve their current products.

Umitron is a Singapore- and Japan-based deep-tech company whose aim is to solve worldwide food and environmental problems by empowering aquaculture through technology. It builds user-friendly data platforms for aquaculture by using the Internet of Things, satellite remote sensing and artificial intelligence. Its ultimate goal is to utilize computer models in combination with aquaculture to help the world sustainably and efficiently deliver protein in a human-friendly and nature-friendly way.

Source: Umitron, which is solely responsible for the information provided and is wholly owned by the source. Informa Business Media and all its subsidiaries are not responsible for any of the content contained in this information asset.