> Something that surprised us early on: only a tiny fraction of farmed fish species have been through genetic improvement programs. Chickens grow 4x faster than they did in 1950 because of decades of selective breeding.
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.
> The math of feeding 10 billion people only works if we farm the ocean
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
As a home aquaponics grower, I am really interested in the opportunity to develop tools that help this industry grow smarter. The impact to open-water fisheries can be undone if the markets can be affected to appreciate farm-raised fish for their quality.
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.
This is a great comment. You are absolutely right about the data opportunity. The industry is so data sparse right now that even basic measurements at scale would be a step change. We are seeing that firsthand with our customer. They went from sampling a few dozen fish by hand to continuous measurement and the insights are already compounding.
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?
Tilapia, because the grow-out plan is very well documented. I'd happily sacrifice growth rate for a fish with higher "desirability" factor, and perhaps a lower optimal temperature.
I previously tried Bluegill and lost them, I think, due to stress from temperature variation. I'd like to try them again or go with Catfish. Catfish are the top species (for food, by weight) produced in the US, and they seem nearly as durable as Tilapia in small systems.
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.
Tilapia is a great species and the resilience is impressive. We have not started working with tilapia yet but love that it is one of the best species being grown in developing countries due to ability to thrive in warm and turbid water.
Thanks! Yes turbidity has been one of our bigger challenges. Water clarity can shift dramatically throughout the day depending on feeding, fish activity, and weather. We have had to build our calibration datasets to capture that variance otherwise the quantized models degrade fast in production. We are also experimenting with different lighting setups to cut through particulate but it is still a work in progress :)
Have you familiarized yourself with Whooshh Innovations? They have been operating in this space for over a decade and have solved many of these problems. It is an interesting space for sure! Best of luck!
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.
Really impressive stack — especially the quantization workflow with TensorRT/INT8 on Jetsons. We've been dealing with similar tradeoffs (speed vs segmentation accuracy) in other domain:
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)
Thanks! The quantization tradeoffs have been a grind. We do not have an exact number but we found that a few thousand images was not enough once you account for the variance on farm. Lighting changes throughout the day, water clarity shifts between feedings, fish density varies by tank. Early on our calibration sets were too homogenous and the INT8 models would work great in testing and then fall apart when conditions shifted.
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.
Great question. We are building our entire labeling and data management system in house. Early on we tried existing platforms but they did not fit our workflow. We have a lot of video data and need custom labeling for things like keypoints, body outlines, and deformity classification that off the shelf tools do not handle well.
Building it ourselves is cheaper at our scale, gives us tighter integration between labeling, training pipelines, and deployment, and lets us iterate faster. We can assign tasks to annotators, version datasets, and push models to edge devices from one system. When you are trying to close the loop between data collection on farm and deployment you cannot afford fragmented tooling.
Shinkei definitely has cool tech!
Aquaculture has already surpassed commercial fishing in terms of production and has become the cheapest source of protein in many countries. We are excited to help the industry grow even further.
I am a fan of the fish cursor. We should make the internet quirky again. This is like a modern take on Geocities websites, and they should do things like fish cursors now while they still can before a board of VCs comes in and makes them remove the fish cursor.
> Something that surprised us early on: only a tiny fraction of farmed fish species have been through genetic improvement programs. Chickens grow 4x faster than they did in 1950 because of decades of selective breeding.
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...
> The math of feeding 10 billion people only works if we farm the ocean
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
As a home aquaponics grower, I am really interested in the opportunity to develop tools that help this industry grow smarter. The impact to open-water fisheries can be undone if the markets can be affected to appreciate farm-raised fish for their quality.
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
This is a great comment. You are absolutely right about the data opportunity. The industry is so data sparse right now that even basic measurements at scale would be a step change. We are seeing that firsthand with our customer. They went from sampling a few dozen fish by hand to continuous measurement and the insights are already compounding.
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?
Tilapia, because the grow-out plan is very well documented. I'd happily sacrifice growth rate for a fish with higher "desirability" factor, and perhaps a lower optimal temperature. I previously tried Bluegill and lost them, I think, due to stress from temperature variation. I'd like to try them again or go with Catfish. Catfish are the top species (for food, by weight) produced in the US, and they seem nearly as durable as Tilapia in small systems.
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!
Tilapia is a great species and the resilience is impressive. We have not started working with tilapia yet but love that it is one of the best species being grown in developing countries due to ability to thrive in warm and turbid water.
This is an awesome concept. Thanks for sharing.
Have you had any issues with turbidity so far?
Thanks! Yes turbidity has been one of our bigger challenges. Water clarity can shift dramatically throughout the day depending on feeding, fish activity, and weather. We have had to build our calibration datasets to capture that variance otherwise the quantized models degrade fast in production. We are also experimenting with different lighting setups to cut through particulate but it is still a work in progress :)
Have you familiarized yourself with Whooshh Innovations? They have been operating in this space for over a decade and have solved many of these problems. It is an interesting space for sure! Best of luck!
Thank you!
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.
Really impressive stack — especially the quantization workflow with TensorRT/INT8 on Jetsons. We've been dealing with similar tradeoffs (speed vs segmentation accuracy) in other domain:
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)
Thanks! The quantization tradeoffs have been a grind. We do not have an exact number but we found that a few thousand images was not enough once you account for the variance on farm. Lighting changes throughout the day, water clarity shifts between feedings, fish density varies by tank. Early on our calibration sets were too homogenous and the INT8 models would work great in testing and then fall apart when conditions shifted.
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.
Great product!
I wonder how do you manage data labeling? Do you outsource it by using data label vendors or do you have something in-house?
Great question. We are building our entire labeling and data management system in house. Early on we tried existing platforms but they did not fit our workflow. We have a lot of video data and need custom labeling for things like keypoints, body outlines, and deformity classification that off the shelf tools do not handle well. Building it ourselves is cheaper at our scale, gives us tighter integration between labeling, training pipelines, and deployment, and lets us iterate faster. We can assign tasks to annotators, version datasets, and push models to edge devices from one system. When you are trying to close the loop between data collection on farm and deployment you cannot afford fragmented tooling.
Shinkei was for the rich; you guys make it for all
Shinkei definitely has cool tech! Aquaculture has already surpassed commercial fishing in terms of production and has become the cheapest source of protein in many countries. We are excited to help the industry grow even further.
The fish cursor is cute, but extremely annoying.
I like it, but it should just be visual. It jacks my default scrollpad/mouse behavior, which is the annoying part.
Perhaps add a hook below the usual mouse real pointer and have a fish that is just a decoration that slowly swims to it.
^ this
interesting, I like the idea
yup I got similar feedback from other batchmatches, gonna fix this.
Agreed. Feels icky. Made me want to leave the page as quickly as possible again.
We got a little too excited about the fish theme. Noted for the next iteration.
^ no way this was tested by anyone with eyes before it was deployed
I am a fan of the fish cursor. We should make the internet quirky again. This is like a modern take on Geocities websites, and they should do things like fish cursors now while they still can before a board of VCs comes in and makes them remove the fish cursor.
Ha, thank you. We figured if we are building robots for fish we might as well commit to the bit. Enjoy it while it lasts.