From Indonesia to the Global Stage, YKAN Introduces FishFace to Bridge Fisheries Data Gaps

Yayasan Konservasi Alam Nusantara (YKAN) has introduced FishFace, a fisheries data collection application supported by artificial intelligence (AI), to the global stage at the Seafood and Fisheries Emerging Technologies (SAFET) Conference 2026 in Cebu, Philippines, held from 7–10 September 2026.  

YKAN shared its experiences developing FishFace during a panel session titled “Technologies and Lessons from Small-Scale Fisheries.” The panel provided a space to discuss technology implementation and lessons from small-scale fisheries, including adoption challenges and the need to tailor innovations to local contexts. On this occasion, YKAN also demonstrated FishFace workflows using smartphones, measuring boards, and miniature fish models.

FishFace was developed from over a decade of field experience in fisheries data collection with local fishers. The application is designed to improve the quality of catch information and strengthen data-driven fisheries management.

YKAN developed FishFace to address data collection challenges in Indonesia. Small-scale fisheries in the country face unique challenges, operating across numerous regions, using multiple fish species and gear types, and often lacking adequate recording tools and connectivity. Technology's role here is clear: streamline data collection while meeting field users' needs and realities.

“Reliable fisheries data is not only needed to know how many fish are caught, but also to understand species composition, fishing locations, and changes in resources over time. This information is crucial for determining appropriate management measures,” said Harxylen Kinanti Purnomo, YKAN’s Sustainable Fisheries Program Manager.

FishFace development builds on YKAN’s experience with the Crew-Operated Data Recording System (CODRS), an approach that engages fishers directly in catch documentation. Over the past 10 years, CODRS has documented more than 8.1 million fish specimens from 44,000 landings, with over 1,000 fishers participating as collaborators.

These experiences provided field knowledge and datasets that formed the foundation for FishFace. The application uses AI to identify fish species from images, mark fishing locations, and record catch information.

FishFace is also being developed to integrate with the Ministry of Marine Affairs and Fisheries (KKP) e-Logbook. This integration aims to reduce data gaps in reporting and link field data collection with the government’s fisheries information system.

In its current integration phase, FishFace aims to identify 25 tuna and snapper species, which collectively account for over 60 percent of relevant catches in Indonesia. This initial scope serves as a stepping stone towards gradually expanding the application’s capabilities based on data requirements and the availability of training datasets.

For Indonesia, strengthening fisheries data collection is vital, as fisheries management spans vast marine areas with diverse characteristics. Detailed data on catch composition, fishing locations, and fishing activities is required to support policy-making, management evaluations, and monitoring of resource sustainability.

This need is also relevant in the global context. Small-scale fisheries play a crucial role in food security and coastal community economies across numerous countries. However, the documentation of their activities and catches is often inconsistent. Data gaps can hinder managers’ ability to understand resource conditions and craft policies that reflect real conditions on the ground.

Field Testing and Learning

FishFace feasibility tests prove that application development requires more than just accurate AI models. On the user side, repeated hands-on training and clear communication about data use, including location and catch information, are essential for building trust and ensuring proper technology usage.

“Pilot trials revealed that AI accuracy is only part of success. Technology must work within a workflow that connects with existing systems and is understood by fishers. Therefore, development must run alongside users, not solely from the technology side,” said Siska Agustina, YKAN fisheries scientist.

Going forward, YKAN will continue to refine FishFace by expanding data diversity, improving model accuracy, strengthening application reliability, and enhancing data synchronisation and backup.

Fisher participation remains central to the process. Training, user-friendly design, and transparent communication about data usage are crucial to ensure that this technology doesn’t remain an isolated innovation but supports sustainable data collection practices.

FishFace showcases how field experience, data, and technology can effectively tackle data collection challenges for Indonesia and the global fisheries community. With better information systems, fisheries management can become more responsive to resource conditions and fishers’ needs, supporting sustainability for the future.