{"id":132111,"date":"2026-09-02T04:54:57","date_gmt":"2026-09-02T04:54:57","guid":{"rendered":"https:\/\/www.seeedstudio.com\/blog\/?p=132111"},"modified":"2026-09-02T05:04:20","modified_gmt":"2026-09-02T05:04:20","slug":"when-ai-meets-aquaculture-teaching-a-camera-to-read-crayfish-behavior","status":"publish","type":"post","link":"https:\/\/www.seeedstudio.com\/blog\/2026\/09\/02\/when-ai-meets-aquaculture-teaching-a-camera-to-read-crayfish-behavior\/","title":{"rendered":"When AI Meets Aquaculture: Teaching a Camera to Read Crayfish Behavior"},"content":{"rendered":"\t\t
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Since leaving Shenzhen, the Chaihuo MCV<\/a> has spent 131 days on the road, traveling through 21 provinces and 49 cities and bringing edge computing, digital fabrication, and open-source hardware workshops into communities across China.<\/div>
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Many of those encounters have centered on agriculture. In Xinjiang, the team used reCamera to explore how computer vision could help a tomato processing plant improve produce screening \u2014 read that story here<\/a>.<\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Last week, at Tianjin Agricultural University, however, the question shifted from crops to crayfish: could edge AI help researchers better understand what is happening inside an aquaculture tank?<\/strong><\/div>
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At the university’s College of Fisheries, the MCV team met Professor Wang, Head of the Department of Aquaculture. One challenge his team studies is aggressive behavior in high-density aquaculture. In species such as redclaw crayfish (Cherax quadricarinatus<\/em>) and giant freshwater prawns (Macrobrachium rosenbergii<\/em>), aggressive interactions can lead to injuries, limb loss, and cannibalism during vulnerable stages such as molting \u2014 all of which can affect survival and farming yields.<\/div>
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Understanding those interactions requires more than monitoring the water around the animals. Researchers also need to understand the animals themselves.<\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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What Automation Still Can't See<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Modern aquaculture can automate much of the surrounding environment. Dissolved oxygen, water temperature, feeding, and other conditions can all be monitored and adjusted with sensors and control systems.<\/div>
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But those systems tell only part of the story.<\/div>
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They can tell a farmer what is happening in the water<\/strong>, but not necessarily what is happening between the animals<\/strong>: How often do they fight? What happens before an aggressive encounter? Do different male\u2013female groupings produce different behaviors? How does an animal raised in isolation respond when it is suddenly placed in a social environment?<\/div>
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Answering questions like these could eventually help researchers connect behavior with feeding, environmental conditions, stocking strategies, and animal stress.<\/div>
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That was where th team began exploring the use of reCamera<\/a>.<\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Designing the Experiment<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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The team built a controlled test environment using a 45 \u00d7 30 \u00d7 25 cm tank and deployed a reCamera for video capture.<\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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reCamera runs Linux on an embedded AI SoC with 1 TOPS @ INT8 computing performance and supports built-in YOLO models, giving researchers an open-source platform on which they can develop and eventually deploy custom vision models at the edge.<\/div>
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For this experiment, the first subjects were redclaw crayfish that had previously been kept in isolation.<\/div>
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The initial goal was straightforward: begin collecting enough visual data to distinguish different behaviors \u2014 from resting and exploration to threat displays and physical confrontation \u2014 and use that dataset as the basis for training a behavior-recognition model.<\/div>
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The experiment quickly showed that collecting useful behavioral data was more complicated than simply putting a camera in front of a tank.<\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Different sex combinations produced different interactions.<\/strong> The team observed several group configurations, including two males with one female, one male with one female, and two females with one male. Their interactions varied noticeably. Two males were more likely to confront each other, while a male\u2013female pair showed more courtship behavior than direct conflict.<\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Social history matters, too.<\/strong> Crayfish that had been raised in isolation did not immediately behave like animals accustomed to living in groups. When first introduced to a shared environment, they often went through an adaptation period \u2014 observing tank-mates and assessing potential opponents before engaging. Some showed very little aggression without an external trigger.<\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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And sometimes, researchers had to create a reason for an encounter.<\/strong> To collect enough examples of aggressive behavior for the dataset, the team occasionally introduced conditions that could prompt competition or confrontation. This helped capture interactions that might otherwise occur too infrequently during a limited observation period. Rather than simply recording “fights,” the challenge was to capture the sequence of behavior around them.<\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Building a Behavioral Dataset<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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By the end of the initial data-collection phase, the team had gathered more than 2,000 images<\/strong> covering different group compositions and behavioral situations.<\/div>
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The footage included long stretches of inactivity alongside exploration, threat displays, confrontation, retreat, and recovery.<\/div>
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That variation matters.<\/div>
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If a future recognition model sees only the most obvious moments of fighting, it may miss the subtler postures and movements that happen beforehand. And if all of its training data comes from one carefully controlled tank, it may struggle when lighting, water clarity, background, or stocking density changes.<\/div>
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So the immediate goal is not simply to “teach AI what a fight looks like.” It is to build a dataset broad enough to support a model that can eventually recognize behavior under different conditions. With the first dataset in place, the team has now moved into preliminary model training and testing.<\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Taking the Experiment Beyond the Lab<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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The next stop was the university’s aquaculture facility at another campus.<\/div>
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Unlike the controlled laboratory setup, the facility operates a larger aquaponics system in which crayfish and vegetables are raised within a circulating water system. Environmental variables such as oxygen, ammonia, and water temperature are continuously monitored, while the animals themselves live in a much more complex group environment.<\/div>
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For the team, this offered an opportunity to continue collecting data under conditions closer to real aquaculture production \u2014 with different backgrounds, lighting, animal densities, and social interactions.<\/div>
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The current work is still at an early stage. The initial dataset has been collected, and preliminary model development has begun, but building a system that can operate reliably in aquaculture will require considerably more data.<\/div>
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Different seasons, species, farm layouts, animal densities, and environmental conditions can all change what the camera sees.<\/div>
That is also why this project does not end with a single training session.<\/div>
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Following this initial experiment, Seeed will continue working with the College of Fisheries to expand the behavioral dataset, iterate on the recognition model, and explore how the approach could develop into a more complete research platform.<\/div>
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Professor Wang shared examples of the surprisingly complex social behavior found in some mantis shrimp species. In species that form long-term pairs, females may remain in the burrow caring for eggs while males forage outside and return with food.<\/div>
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For a team that had arrived to experiment with computer vision, the visit became something broader: a glimpse into how much behavioral information exists in an aquaculture environment before anyone even begins building a model to interpret it.<\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Why Open Hardware Matters Here<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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For the experiment, the team used a standard reCamera 2002 HQ PoE: an open-source, Linux-based AI camera with replaceable M12 lenses and support for custom models.<\/div>
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That flexibility matters in research because the question rarely stays the same for long.<\/div>
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Today, the task may be distinguishing aggressive behavior in redclaw crayfish. Tomorrow, the same underlying platform might be adapted to investigate feeding behavior, movement patterns, or other species entirely.<\/div>
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Researchers need room to change the camera setup, the model, the data pipeline, and even the research question itself.<\/div>
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An open platform makes that kind of iteration easier.<\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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From Aquaculture to Interspecies Research<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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The Tianjin experiment is one stop in the MCV’s journey, but it also connects to a broader direction Seeed is exploring: how open hardware can help researchers collect, interpret, and work with data from other species.<\/strong><\/div>
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Seeed is working together with Interspecies Internet<\/a><\/strong> and other collaborators to develop the Interspecies Open Datalogger<\/strong>, an open-source reference design for modular biotelemetry and edge data-logging devices. The goal is to build a shared technology base that can support data collection across different species and research contexts, rather than creating a separate tool for every animal or individual project.<\/div>
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The direction is becoming clearer: to combine communication, computation, and sensing<\/strong> in a modular, open platform that can be adapted across species, habitats, and research contexts. The aim is not to build a device that \u201ctranslates animals,\u201d but to provide more open and reproducible infrastructure for researchers studying and interpreting animal behavior.<\/div>
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The aquaculture experiment in Tianjin is one small example of what that can look like.<\/div><\/div>
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Every stop on the MCV journey reinforces the same principle: the people closest to a problem usually understand it best. <\/strong>Our role is to bring open tools closer to those people \u2014 and make it easier for them to build, test, learn, and iterate. Stay tuned.<\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Since leaving Shenzhen, the Chaihuo MCV has spent 131 days on the road, traveling 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