Generative Simulation Benchmarking for Sustainable Aquaculture Monitoring

The article explores using generative AI models like GANs and Diffusion Models to create high-fidelity digital twins of fish farms, enabling simulation-based training for robust aquaculture monitoring systems with ethical auditability.

Details

The author's journey into the intersection of AI, environmental science, and ethics began on a salmon farm in Norway, where a standard computer vision model for fish counting failed spectacularly due to the complex, noisy real-world conditions. This failure sparked a multi-year research effort to build robust, ethically-sound AI monitoring systems for aquaculture. The core problem is that traditional ML relies on expensive, slow, and ethically fraught real-world data collection, while the 'long tail' of rare but critical events is absent from these datasets. The breakthrough came when the author started experimenting with generative models like GANs and Diffusion Models to simulate environmental states and dynamics, rather than just creating images. By training these models on sensor and video data, they can learn the distribution of real-world conditions and generate plausible sequences of underwater conditions and fish behaviors, enabling simulation-based training for aquaculture monitoring systems. The author also discusses baking ethical auditability directly into the core of the system design.