Launch HN: OctaPulse (YC W26) – Robotics and computer vision for fish farming
You might be wondering how the heck we got into this with no background in aquaculture or the ocean industry. We are both from coastal communities. I am from Goa, India and Paul is from Malta and Puerto Rico. Seafood is deeply tied to both our cultures and communities. We saw firsthand the damage being done to our oceans and how wild fish stocks are being fished to near extinction. We also learned that fish is the main protein source for almost 55% of the world's population. Despite it not being huge consumption in America it is massive globally. And then we found out that America imports 90% of its seafood. What? That felt absurd. That was the initial motivation for starting this company.
Paul and I met at an entrepreneurship happy hour at CMU. We met to talk about ocean tech. It went on for three hours. I was drawn to building in the ocean because it is one of the hardest engineering domains out there. Paul had been researching aquaculture for months and kept finding the same thing: a $350B global industry with less data visibility than a warehouse. After that conversation we knew we wanted to work on this together.
Hatcheries, the early stage on-land part of production, are full of labor intensive workflows that are perfect candidates for automation. Farmers need to measure their stock for feeding, breeding, and harvest decisions but fish are underwater and get stressed when handled. Most farms still sample manually. They net a few dozen fish, anesthetize them, place them on a table to measure one by one, and extrapolate to populations of hundreds of thousands. It takes about 5 minutes per fish and the data is sparse.
When we saw this process we were baffled. There had to be a better way. This was the starting point that really kicked us off.
Here is the thing though. Most robots are not built to handle humid and wet environments. Salt water is the enemy of anything mechanical. Corrosion is such a pain to deal with. Don't get me started on underwater computer vision which has to parse through water turbidity and particles. Fish move unpredictably and deform while swimming. Occlusion is constant. Calibration is tricky in uncontrolled setups. Handling live fish with robotics is another challenge that hasn't really been solved before. Fish are slippery, fragile, and stress easily. All of this is coupled with the requirement that all materials must be food safe.
On the vision side we are using Luxonis OAK cameras which give us depth plus RGB in a compact form factor. The onboard Myriad X VPU lets us run lightweight inference directly on the camera for things like detection and tracking without needing to send raw frames over USB constantly. For heavier workloads like segmentation and keypoint extraction we bump up to Nvidia Jetsons. We have tested on the Orin Nano and Orin NX depending on power and thermal constraints at different sites.
The models themselves are CNN and transformer based architectures. We are running YOLO variants for detection, custom segmentation heads for body outlines, and keypoint models for anatomical landmarks. The tricky part is getting these to run fast enough on edge hardware. We are using a mix of TensorRT, OpenVINO, and ONNX Runtime depending on the deployment target. Quantization has been a whole journey. INT8 quantization on TensorRT gives us the speed we need but you have to be careful about accuracy degradation especially on the segmentation outputs where boundary precision matters. We spent a lot of time building calibration datasets that actually represent the variance we see on farms. Lighting changes throughout the day, water clarity shifts, fish density varies. Your...
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[ 14.0 ms ] story [ 46.6 ms ] threadHave you had any issues with turbidity so far?
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
Is not so simple. Marine Aquaculture needs huge amounts of wild fishes to feed the farm fishes. The more aquaculture you have, more pressure on some fisheries. Also less pressure on other, that in fact adds an extra of pressure to the first fisheries. China has increased its fleet in the last decades for that.
All this "fishes are sentient" stuff is toxic for your job. My advice is to avoid it like a plague. This people would be typically unable to keep a carnivore fish alive.
I wonder how do you manage data labeling? Do you outsource it by using data label vendors or do you have something in-house?
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
On a different note, if you bring this up or think about India too, how will it impact manual farmers whose entire livelihood is tied to doing the job? Or am I reading it (automation) wrong?
Btw, I’ve never liked a website taking over my mouse pointer or the scroll UI and behavior. But yours is so well done, it is lovely, cute, and is indeed very fishy.
The labor dynamics are also different at these large farms. The work we are automating is repetitive, physically demanding, and hard to staff consistently. Most of the farm managers we talk to are not trying to replace people, they are struggling to find enough workers willing to do this work in the first place. Automation at this scale tends to shift jobs rather than eliminate them entirely. And thank you for the kind words on the website! We have gotten mixed reviews on the cursor so enjoy it while it lasts lol
Aqua startups need each other.
Would love to connect with your cohort. North East India has a ton of freshwater aquaculture potential and we are always looking to learn from founders working in different geographies and species.
You are right that aqua startups need each other. The industry is so fragmented and underserved that collaboration makes more sense than competition at this stage.
one thing: the fish cursor on the site is frustrating pls allow disabling.
Fish sentience is increasingly well-supported in the neuroscience literature. We already kill somewhere around 1-2 TRILLION fish annually... a number that dwarfs land animal slaughter yet attracts almost no ethical scrutiny. Optimising and scaling that system is worth examining carefully.
The part of your website that says "land can't feed 10B people, wild fisheries are maxed out, therefore aquaculture" also quietly ignores plant-based protein, which is more land-efficient and doesn't require instrumentalising sentient animals at industrial scale.
I'm not saying the engineering problems aren't interesting. They clearly are. But at 1-2 trillion deaths per year, this is the largest scale of animal killing in human history, and we're building better tools to do more of it.
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?