AI for ASC Shrimp Welfare Certification

The Aquaculture Stewardship Council (ASC) has introduced extensive welfare standards for farmed shrimp, but verifying that farms actually meet them is hard. Commercial ponds hold over a million animals in muddy water where workers spot-check submerged trays. Dead shrimp sink and are cannibalized within hours, so true mortality is unknown until harvest. The project investigated where AI tools could meaningfully close the verification gap to improve shrimp welfare.

I worked through this in two phases. First, I mapped all 41 ASC crustacean indicators and ran each through three filters: is it outcome-based (meaning does it measure welfare outcomes rather than inputs such as stocking density), does it carry high welfare consequence, and does conventional monitoring fail to detect violations? Four categories survived; mortality detection was prioritized for deeper review.

Second, I scoped the AI tool landscape against the two ASC mortality indicators, looking for commercial systems, academic prototypes, and finfish-aquaculture tools that might transfer to shrimp.

Aggregate cycle-level mortality has a working solution: Minnowtech's underwater sonar can produce biomass-based mortality estimation. This technology could feasibly be used commercially to improve welfare, at nearly the same price per shrimp helped as stunners.

But individual mortality detection is unsolved. No commercial tool can find a dead shrimp on a turbid pond floor before cannibals consume it, distinguish carcasses from shed exoskeletons, or do automated cause-of-death analysis. Finfish tools cannot be used for shrimp because they assume floating bodies in clear water. The credible path forward involves environmental DNA surveillance, passive acoustics for cannibalism detection, and range-gated laser imaging. None of these technologies exist in commercial form yet.