When more than 3,000 fish died in her mother’s pond in March 2026, Nigerian pharmacy graduate Ukachi Benita saw more than a devastating farming loss. She saw a problem that technology could help solve.
Her mother had spent years raising fish, but repeated losses had become part of the reality of running the farm. This time, however, the scale of the loss was difficult to ignore.
Benita began looking for answers and discovered that poor water quality can contribute to fish stress, disease and mortality. The challenge was that her mother had no way of continuously monitoring what was happening inside the pond.
That experience inspired Benita to build Aquamanne, a low-cost AI-powered water monitoring system designed to help fish farmers understand what is happening in their ponds before it is too late.
From a family problem to an AI solution
Benita started developing Aquamanne during a Google Gemma hackathon. With about ₦30,000, an ESP32 microcontroller, sensors and materials she could find locally, she built her first prototype in just one week.
The system monitors three important water conditions,temperature, pH and dissolved oxygen,and sends the readings to an AI model.
But Benita wanted the technology to do more than simply display numbers.
Aquamanne is designed to interpret the readings and turn them into simple, understandable alerts for farmers. If oxygen levels drop or the pH moves outside a healthy range, the system can explain that the pond conditions are changing and suggest what the farmer should consider doing.
That is where Benita sees the real potential of AI.
Traditional sensors can tell farmers what the numbers are. Aquamanne aims to help them understand what those numbers mean.
The system also has an important African dimension. Benita wants farmers to eventually receive information through text or voice in local languages, including Yoruba, Igbo and Hausa. The prototype also supported Swahili during the hackathon.
Learning AI and hardware along the way
Benita’s journey is also a reminder that innovation does not always begin with someone who has a traditional technology background.
She trained as a pharmacist at Nnamdi Azikiwe University and had little experience with embedded systems before starting the project.
To build Aquamanne, she taught herself through YouTube tutorials and turned to AI tools such as Claude when she encountered technical challenges.
The first version was far from a finished commercial product. It operated offline through Bluetooth, with a laptop hosting the local AI model.
But instead of stopping at the hackathon demonstration, Benita began thinking about how to turn the prototype into something farmers could actually use.
Farmers saw the opportunity
After sharing Aquamanne on X and LinkedIn, Benita began hearing from fish farmers who faced the same problem.
One farmer based in Lagos, for example, had a fish farm in Owerri and wanted a way to monitor the water remotely after farm managers reported that the fish had died. He asked whether Benita could develop the technology so he could monitor the pond from wherever he was.
The response convinced Benita that Aquamanne could become more than a hackathon project.
She has since joined forces with a Nigerian professor based at Oxford and an embedded systems engineer. The three-person team is now working to improve the hardware, software and language capabilities of the system.
Building technology farmers can actually afford
The next challenge is turning a promising prototype into a practical product.
The team is working to make Aquamanne smaller, more durable and less dependent on electricity. They are also exploring remote monitoring and making installation simple enough for farmers without specialised technical knowledge.
Cost remains central to the design.
Benita wants to use reliable hardware without creating a product that is too expensive for the farmers it is meant to serve.
The team is also developing a feature that will allow farmers to ask the system questions, while improving its performance across more African languages.
Benita hopes to have a functional product by the end of 2026.
For her, however, the goal is bigger than building an AI product.
It is about preventing other farmers from experiencing the kind of loss that first pushed her to start building.
What began as a ₦30,000 hackathon experiment to help her mother is now becoming an ambitious attempt to bring affordable AI-powered monitoring to fish farmers.
And Benita’s story shows what can happen when technology is not built simply because AI is popular, but because there is a real problem that needs solving.
About the Author
