I agree that there is an opportunity here for getting more calories per fish (and especially per input of feed, which is really what decades of chicken optimization are about). But the consequences of these changes for chicken welfare have been disastrous [0] and we're seeing a concerted effort to move to higher-welfare breeds (though still more efficient than ancestral breeds). Likewise, intensive salmon farming has led to widespread '“environmental dewilding,” or the process of modifying natural water bodies with artificial infrastructure — in this case, fish farm pens and cages — and polluting them' [1]. It sounds like there are lots of ways in which using more robots can make monitoring less-invasive, and therefore less stressful for fish. I certainly hope to see those attributes, rather than the potentially disastrous ones, emphasized as you move forward.
[0] https://www.ciwf.org/programmes/better-chicken/
[1] https://www.vox.com/future-perfect/468348/atlantic-salmon-fa...
Even for marketing puffery, "only" seems reductive when most resource usage seems specific to a few animal products like cows and lamb: https://ourworldindata.org/land-use-diets
I think there is such an incredible opportunity in the sector, and it probably looks a lot like any of the other sectors that have been augmented by data - gather giant piles of any measurable detail, and hope that after filtering you see a pattern that doesn't depend on your production environment running as many sensors ( or tensors ).
Last Thought: Fish transfer pumps are not only a thing, but one of the best ways to have the whole pond population march past your camera in a lighting environment where you have more control.
https://www.miprcorp.com/fish-pumping/ - just one example with decent pictures
Thank you for the fish pump link. We have looked at pump based systems as a way to create controlled measurement environments. You get consistent lighting, predictable fish orientation, and the fish are already moving through a constrained path. The challenge is you are still dealing with water turbidity, particulates, and bubbles in the flow which can mess with imaging. It is better than open water but not a free pass on the vision problems.
We have also been looking at pescalators which use an Archimedes screw design to lift fish out of the water. Some setups combine this with anesthetization for operations that require handling. The tradeoff is you are adding stress and complexity but you get a much cleaner imaging environment. There is no single right answer here and the best approach depends on the species, life stage, and what you are trying to measure. This is definitely technology that will develop over time as the industry matures.
What species are you working with in your aquaponics setup?
The pescalators sound great. There are so many tools like that where the application specifics ( species, system, life stage ) could make room for a scalpel-precise optimization of some tool, but the benefits would have to come from scale, and there just haven't been many first-movers ( or they keep quiet and defend the moat ) who seem poised to raise the tide for the whole industry. It is very ripe for the work you are doing to help the downstream gains over generations of stocks.
Cheers to you guys!
Have you had any issues with turbidity so far?
Whoosh has really interesting tech more focused on the fish transport side with products that move fish from tank to tank while performing some operations.
Our initial focus with inspection is taking high quality images of fish to pull insights needed for maximizing efficiency and improving breeding programs. We have designed our system to easily drop-in to the current operations so it is seamless.
I wonder how do you manage data labeling? Do you outsource it by using data label vendors or do you have something in-house?
Curious — how many labeled fish images did you need before the quantized models stopped falling apart in production?
(Also, for anyone tracking W26, we've got OctaPulse on our prediction market: ingene.win/?utm_source=hn_comment&utm_medium=social&utm_campaign=mar2026)
We also found that segmentation required significantly fewer images compared to keypoint pose detection models. Segmentation generalizes faster since you are just finding body boundaries. Keypoints are more finicky because anatomical landmarks vary a lot more across species, life stages, and body deformation while swimming. We had to be much more intentional about diversity in the keypoint training data. What made the difference overall was building calibration sets that intentionally captured edge cases. Low light, high turbidity, dense occlusion, different life stages. We also started stratifying by time of day and tank conditions rather than just grabbing random frames. It is still not perfect but the models are much more stable now.