Using Computer Vision to Automate Fish Stock Assessment in Coastal Fisheries

Reliable fish stock information is essential for managing coastal ecosystems, protecting food supplies, and sustaining fishing livelihoods. Yet many fisheries still depend on occasional surveys, manual species identification, landing-site interviews, and catch records that vary in quality. These methods can leave decision-makers with an incomplete view of changing marine populations.

Computer vision offers a practical way to improve this evidence base. Cameras, machine learning models, and connected data systems can identify species, estimate fish size, count catches, and detect trends across harbours, vessels, markets, and underwater habitats. When integrated with local knowledge and responsible data governance, automated observation can make fisheries monitoring faster, broader, and more consistent.

For governments and development partners across Asia-Pacific, the technology also creates an opportunity to connect digital infrastructure with food security and environmental management. Effective deployment requires more than advanced algorithms: it depends on affordable equipment, trained users, interoperable data, and partnerships that include fishing communities.

Why Coastal Fisheries Need Better Monitoring

Small-scale fisheries often operate across wide and difficult-to-monitor coastlines. Landing sites may be dispersed, vessels may use different reporting practices, and field officers may have limited time to inspect catches. Seasonal changes, illegal fishing, climate pressures, and shifting market demand add further complexity to stock assessment.

Traditional surveys remain valuable, especially for collecting biological samples and understanding fishing practices. However, they can be expensive to repeat and difficult to scale. Manual measurements may also introduce inconsistencies between observers, while paper-based records can delay analysis until management decisions have already been made.

A computer vision system can capture standardized images or video at key points in the supply chain. Automated tools can then support species recognition, length estimation, catch counting, and verification of reported landings. These outputs do not replace fisheries scientists; they give them a larger and more timely stream of evidence.

How Image-Based Assessment Works

A basic system combines cameras, controlled lighting, image-processing software, and a database. At a landing centre, fish can pass across a marked surface while cameras record their shape, colour, length, and visible features. Software detects individual specimens and compares them with trained reference models.

Underwater cameras provide another source of information. Fixed or mobile devices can record reef fish, pelagic species, or habitat conditions without requiring every observation to be made by divers. Acoustic sensors and environmental instruments can supplement visual data where water clarity, depth, or species behaviour limits optical detection.

The model must be trained on images from the locations where it will operate. Fish can look different according to age, sex, season, water quality, handling, and camera angle. Local image libraries, expert validation, and regular model updates are therefore central to accurate species identification and biomass estimation.

Turning Observations Into Fisheries Intelligence

Images become useful for management when they are connected to structured metadata. Each record may include the date, landing site, vessel category, gear type, geographic area, species, estimated length, and confidence score. Combining these observations with effort data helps distinguish a larger catch caused by increased fishing from a larger catch caused by healthier stocks.

Computer vision can support several indicators:

The results should be interpreted alongside scientific surveys, fisher observations, market information, and ecological data. A model that counts fish accurately may still produce misleading conclusions if sampling is concentrated at one port or if certain vessel groups are missing from the dataset.

Assessment approach Main strength Common limitation Best digital enhancement
Manual landing inspection Expert judgement and flexible observation Slow and difficult to standardize Image-assisted species and size recording
Underwater visual census Direct habitat and abundance information Costly, weather-dependent, and limited by visibility Automated detection and repeated camera surveys
Fisher catch reporting Broad operational coverage Inconsistent formats and possible underreporting Mobile image verification and structured forms
Acoustic assessment Effective for schools and deeper water Species identification can be uncertain Fusion with video, environmental, and vessel data
Market monitoring Shows trade and demand patterns May miss unmarketed or discarded catch Automated recognition at wholesale and retail points

Designing Systems For Real Field Conditions

Technology must fit the working environment. Cameras need protection from saltwater, dust, heat, unstable electricity, and inconsistent internet access. Edge computing can allow devices to process images locally and synchronize results when a connection becomes available. Solar power, low-cost smartphones, and modular equipment can reduce operating costs in remote areas.

User experience matters just as much as hardware. Fishers, enumerators, and port officials should be able to correct an identification, flag an unusual image, and continue working when the model is uncertain. Interfaces should support local languages and display practical information rather than technical scores alone.

Privacy and ownership also require clear rules. Vessel identities, fishing locations, and catch records may have commercial or regulatory sensitivity. Data-sharing agreements should define who can access raw images, who controls derived indicators, how long records are retained, and how information can be used in enforcement or research.

Building Capacity And Trust

Successful adoption depends on local technical and institutional capacity. Fisheries departments need training in image annotation, quality assurance, model evaluation, and interpretation of uncertainty. Universities and research institutes can help develop regional datasets that reflect local species and fishing methods rather than relying on models trained in distant waters.

Community participation improves both accuracy and legitimacy. Fishers can identify species that are difficult for automated systems, explain seasonal patterns, and help determine where cameras can be installed without disrupting work. Transparent pilot projects should publish performance results, including false identifications and gaps in coverage.

Regional cooperation can reduce duplicated effort. Through the ICTD-ASP platform, public agencies, technology providers, development organizations, and civil society can connect computer vision pilots with broader digital development, investment, and capacity-building initiatives across Asia-Pacific.

Practical Priorities For Implementation

A phased approach allows agencies to test value before committing to large-scale deployment. Start with one or two high-volume landing sites, a limited set of priority species, and clearly defined indicators. Baseline measurements should be collected before automation begins so that improvements in speed, consistency, and coverage can be demonstrated.

Procurement should consider the full lifecycle of the system. The cost of cameras is only one part of the budget; annotation, connectivity, maintenance, storage, user training, cybersecurity, and model retraining also require sustained support. Open standards can help prevent dependence on a single supplier and make it easier to exchange data with national fisheries information systems.

Key recommendations include:

Scaling From Pilot Projects To Public Value

Once a pilot demonstrates reliable performance, the next step is integration with fisheries management workflows. Dashboards can provide alerts about unusual catch composition, high juvenile proportions, or sudden changes in landing volumes. Planners can use these signals to prioritize field inspections, adjust survey schedules, or investigate emerging pressure on vulnerable stocks.

Investment partnerships can support expansion across borders where species, ecosystems, and fishing fleets are shared. Regional standards for image formats, species taxonomies, metadata, and performance reporting would make datasets easier to compare. This creates a foundation for evidence-based marine planning while helping countries avoid isolated technology projects.

Computer vision is most effective when treated as part of a wider digital public infrastructure for sustainable fisheries. With sound science, accountable governance, and meaningful participation from coastal communities, automated observation can turn routine images into actionable knowledge. Development partners, fisheries agencies, researchers, and responsible technology firms should begin with focused pilots and build toward interoperable systems that strengthen both ocean stewardship and coastal livelihoods.