That feedback loop architecture sounds right — the homogenous dataset trap is one of the most common failure modes in production CV and it's good you caught it early. The welfare data angle is particularly interesting. The distinction between "the tool is neutral but the selection criteria are up to the breeder" makes sense bc it avoids the poultry industry's mistake of optimizing the selector itself. Have you published anything on the calibration pipeline? Would love to see the methodology.
We have not published anything yet but it is on our roadmap. Right now we are heads down on deployment and iterating fast so documentation has taken a backseat. Once we have more data across multiple facilities and species we want to write up what we have learned about calibration strategies for non-controlled environments. There is not a lot of practical literature out there on edge CV in wet and variable conditions so it feels like something worth sharing.
