By Simpson Global Media News Desk
A Nigerian pharmacist has turned a devastating fish-farming loss into a technology project aimed at helping farmers detect dangerous changes in pond conditions before they result in large-scale fish deaths.
Ukachi Benita developed an artificial-intelligence-powered prototype known as Aquamanne, a monitoring system designed to measure important water conditions and translate the readings into warnings and practical information for fish farmers.
The project began after more than 3,000 fish died in one of her mother's ponds in March 2026. The pond reportedly contained about 10,000 fish, making the incident a significant loss for the family farming operation.
Rather than treating the incident simply as a financial setback, Benita began investigating what could have caused the deaths and how technology could help farmers identify dangerous changes in their ponds earlier.
Her response eventually led her into an area far removed from her formal training as a pharmacist: embedded electronics, sensors, artificial intelligence and aquaculture technology.
The first Aquamanne prototype was reportedly developed during a Google Gemma hackathon, using an ESP32 microcontroller and sensors capable of measuring dissolved oxygen, pH and temperature.
The project illustrates how technology is increasingly being applied to practical problems within Nigeria's agricultural sector, where farmers can lose substantial amounts of money when environmental conditions change faster than they can detect and correct them.
FROM A FAMILY FARM LOSS TO A TECHNOLOGY PROJECT
The story of Aquamanne began with a problem that many fish farmers understand: fish live entirely within the environment created by their pond water.
Unlike livestock that can leave an unhealthy feeding or resting area, fish cannot simply move away from poor water conditions. Changes in oxygen, temperature, acidity or other water characteristics can therefore become dangerous if they are not detected quickly.
For Benita's family, the problem became particularly serious when thousands of fish died in one pond.
The loss prompted her to investigate the relationship between water conditions and fish mortality.
The incident also exposed a practical challenge facing farmers who may not have access to continuous monitoring equipment.
A farmer can visually inspect a pond and observe fish behaviour, but some dangerous changes in water quality may occur before obvious warning signs become visible.
By the time fish begin swimming abnormally, gathering at the surface or dying, the underlying problem may already have become severe.
Benita's idea was therefore to create a system that could continuously collect measurements and provide an interpretation of what those measurements could mean.
That distinction became central to Aquamanne.
Instead of simply displaying numbers on a screen, the system is intended to help turn those numbers into understandable information for farmers.
WHAT AQUAMANNE IS DESIGNED TO DO
The prototype uses sensors to measure several water parameters.
Among the conditions being monitored are dissolved oxygen, pH and temperature.
These measurements are important because fish require suitable environmental conditions to survive and grow.
Dissolved oxygen is particularly significant in intensive fish farming. Fish obtain oxygen from the water through their gills, and insufficient oxygen can place severe stress on fish.
Temperature also matters because fish are affected by changes in water temperature. Water temperature can influence fish metabolism, feeding behaviour, growth and the way oxygen behaves within the pond.
pH is another important measurement because substantial changes in acidity or alkalinity can create stressful conditions for aquatic organisms.
The purpose of Aquamanne is not simply to tell farmers that a reading has changed.
Its artificial-intelligence component is designed to interpret sensor information and communicate what the change may mean in simpler language.
That approach could be useful for farmers who are not trained in interpreting technical water-quality measurements.
A number on a sensor can be difficult to understand without knowledge of what constitutes a safe or dangerous range.
A system capable of interpreting the measurement could instead communicate that a particular condition requires attention.
The technology is therefore intended to bridge the gap between data collection and decision-making.
WHY WATER QUALITY MATTERS SO MUCH IN FISH FARMING
Fish farming depends heavily on maintaining suitable water conditions.
A pond may look normal from the outside while conditions underneath the surface are changing.
Feed leftovers, fish waste, organic material, temperature changes, inadequate water exchange and other factors can influence water quality.
In intensive systems, the number of fish within a limited volume of water can make environmental management even more important.
When fish are stocked at relatively high densities, the consequences of poor water conditions can become significant.
This creates a difficult management problem for farmers.
They have to balance feeding, stocking, water exchange, aeration, cleaning and other activities while also watching for changes that could affect fish health.
The cost of a mistake can be substantial because fish represent both the farmer's production and invested capital.
A farmer may have spent money on fingerlings, feed, pond construction, water systems, labour and other inputs long before the fish reach market size.
A sudden mortality event can therefore erase months of expenditure.
THE SCALE OF NIGERIA'S CATFISH INDUSTRY
Aquaculture is an important component of Nigeria's food-production system, and African catfish is particularly significant within the country's fish-farming sector.
Nigeria is recognised as one of the world's major producers of African catfish, with the species widely farmed for domestic consumption.
The sector includes small household operations, commercial farms, hatcheries, feed suppliers, processors, transporters, traders and other businesses.
The wider value chain therefore extends well beyond the ponds themselves.
Farmers need fingerlings to begin production. Feed manufacturers supply the inputs needed for growth. Equipment suppliers provide pumps, tanks and aeration systems. Labourers support daily farm operations. Processors and traders handle fish after harvest.
When mortality increases, the impact can therefore spread beyond the farmer who owns the pond.
Reduced production can affect supply, farm income and the businesses that depend on the sector.
This helps explain why relatively simple technologies capable of improving fish survival can have significance beyond an individual farm.
A ₦30,000 PROTOTYPE
Benita's initial prototype reportedly cost about ₦30,000 to build.
That figure is important because the project was not initially developed as an expensive commercial system requiring a large industrial laboratory.
Instead, the prototype was assembled using components that could be sourced locally, although the developer encountered limitations because some of the sensors she wanted were not readily available.
The first version was therefore an experiment rather than a finished commercial product.
It demonstrated a concept: sensors could collect information from a fish pond, a microcontroller could process the data and artificial intelligence could help interpret the readings.
The challenge now is transforming that demonstration into a device that can operate reliably on real farms.
A prototype can work under controlled conditions while a commercial product must survive weather, dust, water exposure, power interruptions and the daily demands of farmers.
The device also needs to be simple enough for people who may not have technical training.
A PHARMACIST LEARNING HARDWARE DEVELOPMENT
Benita's professional background makes the development particularly notable.
She studied pharmacy at Nnamdi Azikiwe University in Awka, Anambra State, rather than engineering or computer science.
Before beginning the project, she reportedly had limited experience with embedded systems.
She therefore had to learn many of the technical concepts involved while developing the prototype.
Online tutorials and artificial-intelligence tools became part of that learning process.
Her experience highlights a broader change in how technology development is taking place.
The barrier to experimenting with electronics and software has fallen as more learning resources, development platforms and AI-assisted tools become available.
People trained in one profession can increasingly apply technology to problems in completely different fields.
In Benita's case, pharmaceutical training did not directly prepare her to build a pond-monitoring device.
But her experience of a real agricultural problem provided the motivation to learn what was necessary to attempt a solution.
WHY EARLY DETECTION COULD MATTER TO FARMERS
Fish mortality can become expensive very quickly.
Consider a farmer who has invested several months in growing fish toward market size.
The farmer has already paid for fingerlings and feed and has spent money maintaining the pond.
If thousands of fish die shortly before harvest, much of that investment can be lost.
Early detection does not guarantee that every mortality event will be prevented.
However, the ability to identify deteriorating water conditions earlier could give farmers more time to investigate the cause and take appropriate corrective action.
That could include checking aeration, reviewing feeding practices, changing water, inspecting equipment or seeking technical advice.
The key point is time.
A monitoring system can potentially identify changes before a farmer would notice the problem through visual observation alone.
FARMERS HAVE ALREADY EXPERIENCED THE PROBLEM
The challenges Aquamanne is intended to address are not limited to Benita's family farm.
Other fish farmers have reported losses associated with poor water conditions.
One Lagos-based farmer interviewed in connection with the project described operating multiple ponds containing thousands of fish and experiencing a major loss after oxygen depletion.
Another farmer recalled losing about 5,000 catfish during an earlier stage of his farming experience.
Such experiences demonstrate why water monitoring can be commercially important.
A farmer who has suffered a major loss may be more willing to invest in technology capable of providing early warnings.
However, affordability remains a central issue.
Small and medium-sized farmers cannot necessarily purchase sophisticated imported equipment.
A device that is technically advanced but too expensive for ordinary farmers would have limited practical impact.
That is one reason Benita has emphasised the need to keep Aquamanne affordable.
THE LANGUAGE CHALLENGE
One of the more distinctive elements of the project is its proposed use of artificial intelligence to address language barriers.
Many agricultural technologies provide information in technical or English-language formats.
For farmers who are more comfortable using local languages, this can create another barrier between the technology and the person expected to use it.
Benita's vision is for Aquamanne to eventually support text and voice communication in African languages.
That could allow a farmer to receive information in a more familiar form rather than having to interpret technical terminology.
The idea goes beyond translation.
A voice-based system could potentially make the technology easier to use for farmers who are uncomfortable with complex interfaces or who spend most of their working day away from computers.
The project's language ambitions remain part of its development plans rather than a fully deployed national service.
Nevertheless, the approach demonstrates how AI can be applied not only to data analysis but also to communication.
FROM SENSOR READINGS TO PRACTICAL ADVICE
Traditional monitoring equipment generally performs a straightforward task.
It measures a condition and displays the result.
The farmer then has to interpret the result and determine what action should follow.
Aquamanne is being designed to add another layer.
The artificial-intelligence component is intended to analyse the information collected by the sensors and provide a simpler explanation.
For example, instead of merely presenting a technical reading, the system could alert the farmer that a change requires attention.
This model reflects a wider trend in agricultural technology.
Farmers increasingly have access to sensors, satellite information, weather data and digital record-keeping systems.
The challenge is making the information useful.
A farmer does not necessarily need more numbers.
The farmer needs information that can help answer practical questions: Is something wrong? How serious might it be? What should be checked? How quickly should action be taken?
Aquamanne is attempting to address that gap.
REMOTE MONITORING COULD BE ANOTHER MAJOR USE
The project's development received additional encouragement after farmers began contacting Benita through social media.
One farmer reportedly operated a farm in Owerri while living in Lagos and wanted to know whether Aquamanne could allow him to monitor pond conditions remotely.
That request points to another challenge within modern agriculture.
Farm owners may not always be physically present at their farms.
A business owner can have workers managing daily operations while the owner remains in another city.
Remote monitoring could allow such owners to receive information about important farm conditions without being physically present.
The technology could potentially provide alerts when readings move outside desired ranges.
However, remote monitoring requires reliable communication infrastructure, power and hardware capable of operating continuously.
These are among the technical challenges the development team now has to solve.
MAKING THE DEVICE SMALLER AND MORE DURABLE
The current prototype is only an early stage of the project.
Benita and her collaborators are working to make the system smaller and more durable.
The team is also examining ways to reduce its dependence on electricity.
That is important for rural agricultural environments where electricity may be unreliable.
A monitoring system that stops working whenever power fails would have a major weakness because power interruptions could occur precisely when continuous monitoring is most important.
Possible improvements therefore include better power management and hardware capable of operating for longer periods.
The team also wants the system to be easier to install.
For widespread adoption, farmers should not need to hire a specialist every time the device is installed, adjusted or maintained.
THE ROLE OF THE DEVELOPMENT TEAM
Benita is no longer working alone on the project.
She is collaborating with a Nigerian professor based at Oxford and an embedded-systems engineer.
The combination brings together different areas of expertise.
Benita provides the original problem perspective and product direction.
An embedded-systems specialist can contribute to hardware design, sensors, microcontrollers and power management.
Academic expertise can help with research, testing and validation.
Such collaboration may be important if Aquamanne is to move beyond its initial prototype stage.
Developing a reliable agricultural device requires more than an idea.
The system needs repeated testing under different pond conditions.
Sensors must produce dependable readings.
The software must interpret those readings appropriately.
The hardware must survive real-world conditions.
The communication system must work reliably.
And the final product must remain affordable.
NIGERIA'S AQUACULTURE SECTOR ALSO FACES OTHER CHALLENGES
Water quality is only one of the problems facing Nigerian fish farmers.
Research into the country's catfish industry has identified feed costs, financing constraints, farm management, biosecurity and market conditions among the issues affecting profitability.
Feed is particularly important because it represents a significant operating cost for many farmers.
The economics of fish farming can therefore change when feed prices rise.
Farmers also have to manage diseases and biosecurity risks.
A national study of catfish farms in Ogun and Delta states found gaps in biosecurity practices and documented unusual mortality on some farms.
Those findings demonstrate that technology for water monitoring cannot solve every cause of fish mortality.
Aquamanne is designed around one particular part of the production challenge: detecting changes in pond-water conditions.
Its success will therefore depend partly on how farmers combine the information it provides with other good farming practices.
DATA COULD BECOME AN IMPORTANT FARMING ASSET
If systems such as Aquamanne eventually become widely adopted, they could generate another valuable resource: farm data.
Continuous measurements of temperature, pH and dissolved oxygen could provide farmers with a record of how pond conditions change over time.
That information could help farmers compare different production cycles.
They could potentially identify recurring problems, examine the relationship between feeding and water conditions, and determine whether certain management practices are associated with better outcomes.
Data could also help researchers understand conditions across different farms.
However, collecting data creates responsibilities.
Farmers need to understand how their information is stored and used.
Technology providers need reliable systems for protecting data and ensuring that farmers retain appropriate control over commercially sensitive information.
These issues may become increasingly important as agricultural technology expands.
THE TECHNOLOGY MUST PROVE ITSELF IN REAL FARM CONDITIONS
A promising prototype is not the same as a commercially proven agricultural product.
Aquamanne will need extensive testing before farmers can depend on it for important production decisions.
The sensors will have to remain accurate over time.
The device will need to operate under different weather conditions.
The system will need to cope with different pond types and water conditions.
The artificial-intelligence model will also need to avoid giving misleading recommendations.
This is particularly important because farmers could potentially make financial decisions based on the information provided by the device.
The system should therefore complement professional agricultural advice rather than be treated as an infallible authority.
Testing, calibration and independent validation will be important stages as development continues.
AFFORDABILITY COULD DETERMINE ADOPTION
The first prototype reportedly cost about ₦30,000, but that does not mean a finished commercial device will necessarily cost the same.
Commercial production introduces additional expenses.
The hardware needs a durable enclosure.
Sensors need to be reliable.
Software requires maintenance.
Connectivity can create recurring costs.
Customer support, distribution and repairs also have to be considered.
At the same time, the product cannot become so expensive that small-scale farmers are excluded.
This creates a difficult balance.
The developers need to build something reliable enough to justify its cost while maintaining a price that farmers can realistically afford.
The economics will ultimately depend on whether the device can help prevent losses large enough to justify the investment.
A WIDER OPPORTUNITY FOR NIGERIAN AGRICULTURAL TECHNOLOGY
Aquamanne's development comes as Nigerian agriculture increasingly attracts interest from technology developers.
Farmers face problems involving weather, irrigation, livestock health, crop diseases, supply chains, market access and food processing.
Many of these challenges involve data that can potentially be measured and analysed.
The opportunity for Nigerian technology developers is to create tools specifically designed around local conditions rather than simply importing solutions developed for different agricultural environments.
Local development can make it easier to understand the practical realities faced by farmers.
It can also create opportunities to design systems around local languages, available electricity, local equipment and local farming methods.
Aquamanne is an example of that approach.
Its original problem came from a Nigerian family farm, and its intended users are farmers operating within the Nigerian agricultural environment.
WHAT HAPPENS NEXT
The next phase for Aquamanne will involve improving the prototype and preparing it for more practical use.
The development team wants to make the hardware smaller and more durable, improve remote monitoring, reduce electricity dependence and expand language capabilities.
The project is also intended to remain affordable.
Benita has indicated that she wants the system to be accessible to farmers rather than becoming an expensive specialist product.
A functional product is expected to be developed further during 2026.
Before widespread adoption, however, the system will need additional testing, refinement and validation.
The developers will have to determine how accurately the sensors perform over long periods, how well the AI interprets the readings and how farmers respond to the information.
WHY THE STORY MATTERS BEYOND ONE FARM
The story of Aquamanne is ultimately about more than a single fish farm.
It demonstrates how an agricultural problem can become the starting point for technological experimentation.
A family business suffered a major loss.
Instead of stopping at identifying the loss, Benita investigated the underlying problem and began learning new technical skills.
The result was an early-stage device designed to provide farmers with information that could help them respond more quickly to dangerous changes.
Whether Aquamanne eventually becomes a commercially successful product remains to be demonstrated.
Its development, however, shows the potential of combining local knowledge, affordable hardware and artificial intelligence.
It also illustrates an important principle for agricultural technology: innovation does not necessarily begin in a large laboratory.
It can begin with a farmer asking why thousands of fish died.
THE BIGGER QUESTION FOR FISH FARMERS
For Nigerian fish farmers, the most important question is not whether artificial intelligence is fashionable.
The question is whether a technology can solve a real production problem at a cost the farmer can afford.
Aquamanne is attempting to answer that question by focusing on a basic requirement of fish farming: understanding what is happening in the water.
If the technology can reliably detect dangerous changes, communicate them clearly and provide useful guidance, it could become a practical tool for farmers who currently depend heavily on periodic manual checks.
But the system will have to prove that value in real-world conditions.
The success of the project will ultimately depend on reliability, affordability, simplicity and measurable results.
A TECHNOLOGY PROJECT BORN FROM LOSS
Benita's journey from pharmacist to agricultural technology developer began with a difficult event on her family's farm.
Thousands of fish died, leaving her family with a significant loss and a question about whether earlier detection could have changed the outcome.
That question led to Aquamanne.
The prototype now combines sensors, an ESP32 microcontroller and artificial intelligence to monitor and interpret pond-water conditions.
Its developers are working toward a smaller, more durable and more accessible system capable of remote monitoring and communication in African languages.
For Nigerian aquaculture, the potential value is straightforward.
Fish farmers invest significant amounts of money before their fish reach the market.
Anything that helps them understand changing pond conditions earlier could potentially help them protect that investment.
Aquamanne is not yet a finished solution to fish mortality, and its commercial effectiveness remains to be established through further testing.
But its development represents a growing type of Nigerian innovation: technology built around a specific local problem, using tools that are becoming increasingly accessible to people outside traditional engineering and computer-science professions.
From a pharmacy graduate learning embedded systems to a prototype designed for fish farmers, the project has moved from a family farming setback into an experiment with broader agricultural possibilities.
As the development team continues refining the device, the next test will be whether the idea can move successfully from prototype to dependable farm equipment.
For the farmers who have experienced the sudden loss of thousands of fish, that transition could be the difference between simply receiving a warning after disaster strikes and having enough information to act before the losses become irreversible.
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