A UK University’s AI Told Nigerian Strawberry Growers When to Water, and Yields Rose 40%

Anyone who has grown strawberries knows how fussy they are about water. Too little and the fruit stays small. Too much invites rot. At a strawberry farm in Nigeria, a research team from Nottingham Trent University (NTU) in England has been testing whether sensors and artificial intelligence could take some of the guesswork out of that decision.

The project, called SmartBerry, set up sensors inside and outside the farm’s greenhouses to track temperature, humidity, soil moisture and light, then used AI to help the growers decide when the plants actually needed water. In an article published Oct. 19, NTU says the partnership delivered a 40% increase in strawberry yield and a 15% reduction in post-harvest waste. The share of market-grade fruit rose from about 70% to 88%, and the business’s turnover grew from around £18,000 to £32,000.

Those figures come from NTU’s account of the recently completed partnership. The article does not describe how they were measured, and it does not say how much water the system saved. Still, the approach is practical and easy to picture, and it says something useful for gardeners far from Nigeria: water by what the plant needs, not by the calendar.

Who was involved

SmartBerry was an Africa Agriculture Knowledge Transfer Partnership supported by £245,725 in Innovate UK funding. NTU led it in collaboration with Ashley’s Strawberry Farms Limited and Covenant University in Nigeria. NTU says the project received an “Outstanding” (A) grade in its final report, recognizing its commercial, academic and societal impact.

The work sits inside a larger NTU research program called ENVIROBRAIN, led by Dr. Pedro Machado and Dr. Isibor Kennedy Ihianle of the university’s School of Science and Technology. The SmartBerry project page also lists Ahmad Lotfi, professor and head of NTU’s Department of Computer Science, and senior lecturer Jordan J. Bird.

The problem with a fixed schedule

According to NTU’s project page, strawberries are currently grown in only one of Nigeria’s two seasons. Managing temperature, water and plant health can make a significant difference to production there, and growers can rely on manual assessment and blanket irrigation, watering crops without precise information about how much they need.

The team’s stated goal was to use sensors to monitor environmental conditions and soil, then “request crop watering as and when needed, as opposed to a blanket watering approach.” Saving water, the project page says, could let growers use land where water is hard to come by. A business partner in Nigeria named “more production per hectare” as a key issue for farmers in the area, which became another aim of the work.

In practice, the sensors feed a steady stream of readings to the AI, which looks for patterns in how growing conditions affect the plants. Instead of watering on a timer, the system supports irrigation decisions based on whether the plants need water. NTU says the project also supported strawberry fruiting during less favorable growing seasons, which suggests the technology could help farmers stretch the period when they can produce fruit.

A “digital agronomist” that watches for stress

Water was only part of it. A strawberry plant can be thirsty, too hot or in the early stages of disease, and if nobody notices, the harvest suffers. SmartBerry combined the sensor data with AI-based plant health monitoring to flag changes that might mean a plant is stressed or getting sick, giving growers a chance to adjust conditions or take a closer look before the damage shows.

The researchers describe the system as a form of “digital agronomist,” one that gives farmers information based on live growing conditions.

“Through SmartBerry, we’re showing how AI can help strawberry farmers in Nigeria use water more efficiently, identify problems earlier and improve growing conditions,” Ihianle said in the NTU article. “It’s exciting to see how this technology is making a real difference, helping farmers improve productivity, increase their income and build more sustainable businesses.”

Keeping the computing on the farm

One design choice matters a lot for farms with spotty internet. ENVIROBRAIN uses edge computing, meaning the AI analyzes data close to where it is collected instead of constantly sending it somewhere else. The team is building specialized chips called field-programmable gate arrays (FPGAs) so AI models can run locally, and it is looking at low-power and renewable-energy setups.

The same idea runs through ENVIROBRAIN’s other project, which uses underwater cameras and hydrophones to watch farmed fish. AI models learn what normal swimming, feeding and group movement look like, then flag changes, such as fish turning unusually inactive or swimming erratically, that could signal stress or disease. NTU is careful to say the technology isn’t designed to replace veterinary diagnosis; it is meant to alert farmers sooner.

“Aquaculture is a vital source of food and income globally, but disease outbreaks in fish farms cause major economic losses and animal welfare issues, costing millions annually,” Machado said.

What comes next

The researchers want to push SmartBerry toward more autonomous farm management, including predictive climate modeling, greenhouse control and AI-based yield forecasting, and they are investigating whether the approach could work for other crops. The article does not give a timeline for that next stage.

What a backyard grower can borrow

Few home gardeners need a sensor network or a custom chip. But the core lesson of SmartBerry carries straight into an Arizona backyard, where strawberries are a cool-season crop and summer heat is hard on them. Watering on a fixed schedule is easy, and it is also how plants end up soggy one week and stressed the next. Checking soil moisture before you water, whether with an inexpensive meter or a finger pushed a couple of inches into the soil, is the low-tech version of what the SmartBerry sensors do all day.

The other lesson is about attention. The system’s value came from noticing small changes early, before they turned into lost fruit. A gardener who walks the beds every morning, looks under the leaves and notes what changed is doing the same work, one plant at a time.