Over the past twenty years the aquaculture industry has been expanding at an exponential rate with annual production tripling during this brief time span. At the same time, however, 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. 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 IoT devices and machine learning offer farmers a solution to improve their feeding operations.
The FAI algorithm takes in the same visual information that humans would and then scores fish appetite and presents it in an easy to understand chart. When used in tandem with a smart feeder such as UMITRON CELL, the feed time intervals and amounts can be automatically adjusted with minimal human interference. Farm operators can utilize FAI to fine-tune their feeding schedules, ensuring fish are always satiated. This is easily done via their smartphones with the UMITRON app, where they can check and remotely adjust feed settings based on the FAI feedback.
FAI benefits farmers by reducing wasted feed, improving profitability as well as environmental sustainability. FAI in combination with technology such as CELL allows 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.
Existing UMITRON customers have already begun using FAI alongside CELL. "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," said Masahiko Yamada, managing director of UMITRON. "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," added Takuma Okamoto, CTO of UMITRON.
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 will participate in AquaNor, which will be held in Trondheim, Norway from August 20-23, and The Japan International Seafood & Technology Expo from August 21-23 in Tokyo, Japan. Interested parties should contact UMITRON in advance to set up an informational meeting.
UMITRON is a Singapore and Japan based deeptech company whose aim is to solve worldwide food and environmental problems by empowering aquaculture through technology. We build user-friendly data platforms for aquaculture by using IoT, satellite remote sensing, and artificial intelligence (AI). Our technology helps farmers improve farm efficiency, manage environmental risks, and in turn increase business revenues. Our final 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. Ultimately, we aim to "install Sustainable Aquaculture on Earth".
Photo - https://mma.prnewswire.com/media/958203/FAI2_002.jpg Photo - https://mma.prnewswire.com/media/958204/FAI2_004.jpg