AI-powered research station scans schools of fish in a coral reef.

Can AI Save Our Seas? How Deep Learning Is Revolutionizing Fish Stock Estimation

Avery Sinclair in Climate & Environment December 2025 • 4 min read.

"A new low-cost approach to fish stock assessment uses AI to analyze underwater images, offering hope for better management of our oceans."

The world's fish populations face immense pressure. For years, experts have warned of potential collapses in commercially important fish stocks, driven by unsustainable fishing practices. While some high-income countries have seen improvements through strict regulations, the majority of the world's fisheries remain unassessed and vulnerable.

One of the biggest hurdles to effective fisheries management is the cost of traditional stock assessment. These assessments, which involve manually counting and identifying fish species, are time-consuming, require specialized expertise, and can be prohibitively expensive, particularly for developing nations. The US government alone spends hundreds of millions of dollars annually on this critical task.

Now, a promising solution is emerging: artificial intelligence. By harnessing the power of computer vision and deep learning, scientists are developing automated systems that can analyze underwater images and videos to accurately estimate fish populations at a fraction of the cost. One such system, called FishNet, offers a glimpse into a future where sustainable fisheries management is within reach for all.

FishNet: Deep Learning for Affordable Stock Assessment

AI-powered research station scans schools of fish in a coral reef.

Developed by researchers at the University of Hawai'i at Manoa and Yayasan Konservasi Alam Nusantara, FishNet is an innovative system designed to classify fish species and estimate their size from images captured with a low-cost digital camera. The system leverages a Mask R-CNN, a type of deep neural network, to detect and segment individual fish within an image, even when multiple species are present.

The FishNet system then uses separate machine learning models to classify each fish species and predict its length. To train these models, the researchers created a massive dataset of 300,000 hand-labeled images containing 1.2 million fish from 163 different species. This dataset, which includes fish ranging in length from 10 cm to 250 cm, was carefully curated using additional annotations and quality control methods.

The FishNet system demonstrates impressive accuracy:

92% intersection over union on the fish segmentation task
89% top-1 classification accuracy on single fish species
2.3 cm mean absolute error on fish length estimation

Crucially, FishNet requires no specialized equipment beyond a digital camera and a standardized board with fiduciary markings (colored markers of known size). This makes the approach feasible and financially practical for widespread adoption, particularly in developing countries where resources are limited.

A Future of Sustainable Seas

FishNet represents a significant step forward in making fish stock assessment more accessible and affordable. By combining citizen science with machine learning, this system has the potential to revolutionize fisheries management in developing countries and beyond, leading to healthier oceans and more sustainable seafood for all.

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Everything You Need To Know

What is FishNet and how does it work?

FishNet is an AI-powered system developed by researchers at the University of Hawai'i at Manoa and Yayasan Konservasi Alam Nusantara. It uses deep learning, specifically a Mask R-CNN, to analyze underwater images and videos. The system identifies and segments individual fish, classifies their species, and estimates their size. It works by analyzing images captured with a low-cost digital camera, making it accessible and affordable for assessing fish populations.

Why is FishNet important for sustainable fisheries management?

FishNet is crucial because it makes fish stock assessment more accessible and affordable, especially in developing countries. Traditional methods are expensive and time-consuming, hindering effective management. By using AI, FishNet reduces costs and allows for more frequent and widespread assessments. This enables better monitoring of fish populations, which is essential for implementing sustainable fishing practices and preventing overfishing that threatens marine ecosystems.

What kind of data was used to train the FishNet system?

The FishNet system was trained on a massive dataset of 300,000 hand-labeled images. These images contained 1.2 million fish from 163 different species. The dataset included fish ranging in length from 10 cm to 250 cm and was carefully curated using additional annotations and quality control methods to ensure the accuracy of the models.

What are the key performance metrics of FishNet?

FishNet demonstrates impressive accuracy in several key areas. It achieves a 92% intersection over union on the fish segmentation task, indicating high precision in identifying individual fish. It also has an 89% top-1 classification accuracy on single fish species, showing its ability to correctly identify different species. Furthermore, it has a 2.3 cm mean absolute error on fish length estimation, which means the system provides relatively precise measurements of fish size.

How does FishNet compare to traditional fish stock assessment methods, and what are the implications?

Traditional fish stock assessment methods often involve manual counting, identification, and measuring of fish, which are labor-intensive and costly, requiring specialized expertise. These methods can be expensive, especially for developing nations. FishNet, on the other hand, uses a digital camera and AI to automate many of these processes, drastically reducing both the cost and the expertise needed. The implications are significant: FishNet makes stock assessment accessible, allows for more frequent assessments, and can empower local communities to monitor their fisheries. This ultimately contributes to healthier oceans and the sustainable management of fish populations by providing the necessary data for informed decision-making.