Juliet Obaro, an Internet of Things engineer, says that Edge artificial intelligence can bridge connectivity gaps and empower smallholder farmers with real-time agricultural insights.

She said this in an interview on Thursday in Abuja.

Ms Obaro explained that Edge AI is an emerging technology that enables data processing directly on devices without reliance on continuous internet connectivity.

She said the biggest barrier to digital agriculture was not the lack of innovation but the dependence of most solutions on internet access, which remains unreliable in rural farming clusters.

“Most smart farming tools today are cloud-dependent, meaning they require constant internet connectivity to function effectively. But for rural farmers, especially those in remote communities, this model simply does not work,” Ms Obaro said.

Ms Obaro said Edge AI addressed this gap by embedding intelligence directly into farm-based devices such as sensors, drones, and handheld tools, enabling them to analyse data locally and provide immediate feedback to farmers.

Ms Obaro stated that with Edge, devices could process soil conditions, weather patterns, and crop health data in the field without sending it to distant servers. According to her, this means farmers get real-time insights even when they are completely offline.

She explained that such systems could be deployed using low-power IoT devices equipped with pre-trained machine learning models that detect crop diseases, monitor moisture levels, and predict yield outcomes.

“Imagine a smallholder farmer in a remote village being alerted instantly that his crops are under pest attack, or that soil moisture is critically low, without needing internet access. That is the power of Edge AI,” Ms Obaro stated.

Ms Obaro further highlighted that integrating solar-powered devices could enhance the sustainability of Edge AI systems in rural areas, where electricity supply is also a challenge.

She identified power as another constraint, adding that designing these systems to run on solar energy would ensure that farmers were not limited by grid electricity. According to her, this makes the solution adaptable to rural realities.

She said localisation of AI models was crucial to effectiveness, noting that procedures should be tailored to Nigeria’s climate, soil types, and crop varieties.

“AI systems built with foreign datasets may not perform well here. We need locally trained models that understand indigenous crops like cassava, yam, and maize, as well as region-specific pests and diseases,” Ms Obaro said.

Ms Obaro said that for smallholder farmers who operate on thin margins, affordability is imperative. She maintained that Edge AI significantly lowers recurring costs because once the device is deployed, it can function independently.

On data security, Ms Obaro explained that Edge AI offered an added advantage by minimising data transmission risks. She said that since most data processing happens locally, there would be less exposure to cyber threats associated with cloud-based systems, allowing farmers to retain more control over their data.

Despite its potential, she acknowledged that challenges such as initial deployment costs and limited technical expertise remained.

(NAN)