Interesting to see CV applied to aquaculture — this is one of those domains where the ROI on automation is enormous but underexplored because the talent pipeline skews toward adtech and fintech.
One thing I'd push on: how are you handling distribution shift from turbidity and lighting variation across facilities? In my experience deploying vision models in non-controlled environments (industrial, not fish specifically), the gap between lab accuracy and production accuracy is almost entirely driven by domain shift in image quality. Continuous calibration pipelines — where you flag low-confidence predictions for human review and retrain on the corrected labels — tend to matter more than the initial model architecture choice.
Also curious about the welfare angle that came up in the thread. Selective breeding guided by phenotype scoring has obvious parallels to the poultry industry's problematic optimization for growth rate. Are you building in any multi-objective constraints (e.g., health markers alongside growth metrics) to avoid that failure mode?
One thing I'd push on: how are you handling distribution shift from turbidity and lighting variation across facilities? In my experience deploying vision models in non-controlled environments (industrial, not fish specifically), the gap between lab accuracy and production accuracy is almost entirely driven by domain shift in image quality. Continuous calibration pipelines — where you flag low-confidence predictions for human review and retrain on the corrected labels — tend to matter more than the initial model architecture choice.
Also curious about the welfare angle that came up in the thread. Selective breeding guided by phenotype scoring has obvious parallels to the poultry industry's problematic optimization for growth rate. Are you building in any multi-objective constraints (e.g., health markers alongside growth metrics) to avoid that failure mode?