A Nigerian pharmacist, Ukachi Benita, has developed an artificial intelligence-powered device designed to help fish farmers detect dangerous changes in pond water conditions and reduce fish mortality.
The prototype, known as Aquamanne, was developed after repeated fish losses on her mother’s farm prompted Benita to investigate how poor water quality could be affecting fish production. In March 2026, more than 3,000 fish reportedly died in one of the farm’s ponds, which contained about 10,000 fish.
The experience led Benita to explore a technology-based solution that could give fish farmers earlier warnings when water conditions begin to threaten their stock.
Aquamanne uses sensors and an ESP32 microcontroller to measure key water-quality parameters, including dissolved oxygen, pH and temperature. The information is then processed using Google’s Gemma artificial intelligence model and relayed to an application on a farmer’s phone.
The system is designed not only to collect readings but also to interpret the information, alert farmers when conditions change and provide guidance on possible steps to reduce the risk of fish losses.
Benita developed the prototype during a Google Gemma hackathon, despite having limited previous experience with embedded systems. She said she relied on online tutorials and AI tools while working on the project.
The initial prototype cost about N30,000 and was completed within a week using components sourced in Nigeria. However, the developer encountered challenges obtaining some of the sensors she wanted locally and had to work with available alternatives.
One of the features Benita hopes to develop further is the system’s ability to communicate information in African languages. She said artificial intelligence could help bridge the gap between technical sensor readings and farmers who may not be comfortable interpreting conventional scientific data.
This could allow farmers to receive warnings and recommendations in simpler language and, eventually, through text and voice-based communication.
The innovation addresses a significant challenge in fish farming, where deteriorating water quality can quickly lead to major financial losses. Factors such as inadequate oxygen levels, changes in temperature and poor water conditions can threaten fish stocks if they are not detected early.
The problem has already affected Nigerian farmers. A study involving small-scale fish farmers found that many identified water pollution as a major contributor to disease on their farms. Other farmers have also reported substantial losses linked to oxygen depletion and poor water conditions.
Interest in Aquamanne has grown since Benita began sharing the project, with fish farmers contacting her about potential applications. One farmer operating a fish farm in Owerri while living in Lagos also asked whether the technology could support remote monitoring.
The feedback has encouraged Benita to move beyond the prototype and work towards developing Aquamanne into a product that can be used more widely.
She is currently working with two collaborators, including a Nigerian professor based at Oxford and an embedded systems engineer, to improve the device.
The team plans to make the hardware smaller and more durable, reduce its dependence on electricity, strengthen remote-monitoring capabilities and improve its support for African languages.
Keeping the technology affordable is also a major consideration, particularly because the intended users are farmers who may have limited resources to invest in sophisticated equipment.
If successfully developed and deployed at scale, Aquamanne could give fish farmers an accessible way to monitor pond conditions, respond to problems earlier and potentially reduce losses that affect the profitability of aquaculture businesses.
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