The Ghana Atomic Energy Commission (GAEC) is positioning Ghana at the forefront of Africa’s technological transformation by advancing the strategic use of Artificial Intelligence (AI) across key sectors for national development.
Capitalising on the Commission’s multidisciplinary expertise in nuclear science and related technologies, including space science, GAEC uses AI for applications in geoscience, radiation medicine, environmental monitoring, and big data analytics.
Additionally, GAEC is deploying AI‑driven solutions that directly support Ghana’s agricultural productivity, health systems, climate resilience, and energy security.
At the Artificial Intelligence for Sustainable Development (AI4SD) Mini Conference held from February 17-19, at KNUST, GAEC researchers, Dr Theophilus Narh and Dr Kofi Asare, presented cutting‑edge applications.
They demonstrated how scientifically grounded AI methods can overcome incomplete datasets and turn complex national challenges into opportunities for precision decision‑making and sustainable growth.
AI is increasingly recognised as an essential tool for sustainable development across Africa, particularly for data‑scarce environments. GAEC’s approach emphasises method‑first innovation, ensuring that every AI solution is rooted in scientific rigour, transparent workflows, and reproducible results.
Consequently, at the Commission’s recent presentations at the AI4SD conference, the researchers from GAEC highlighted how the Commission’s Institutes, including the Ghana Space Science and Technology Institute (GSSTI) and the Radiological and Medical Sciences Research Institute (RAMSRI), are developing interoperable AI frameworks that work across agriculture, health, radio astronomy, and subsurface energy systems.
During Dr Kofi Asare’s presentation on “From Pixels to Productivity: AI for Enhancing Food Security in Ghana”, he noted that the conversation around AI in Africa is often abstract.
Dr Kofi Asare’s focus was practical: How do we turn satellite pixels into real productivity gains for farmers?
In Ghana’s smallholder landscapes with fragmented fields, variable management practices, and increasing climate stress, reliable crop intelligence is not optional.
It is essential for food security planning, risk management, and livelihood protection.
During the session, Dr Asare demonstrated how GAEC is integrating Earth Observation data with Machine Learning models to build a national-scale agricultural monitoring pipeline.
The system can map fields, detect early crop stress, classify crop types, and forecast yields with increasing accuracy.
But the real value is not the model. It is what the model enables, such as earlier intervention when crops show signs of stress, smarter allocation of inputs, improved yield management, better climate risk assessment, and stronger support for insurers, banks, and agribusiness actors.
AI must move beyond dashboards and demonstrations. It must strengthen systems. It must reduce uncertainty. It must support decision-making.
From pixels to productivity was not just a presentation title; it was a vision for how AI can serve food security in Ghana and across Africa.
Furthermore, Dr T. Ansah‑Narh’s presentation, “Integrated AI Frameworks for Big Data Analytics in Health Diagnostics, Radio Astronomy, and Petroleum Systems,” showed how three very different fields- health, space science, and energy- actually face the same fundamental problem: how to make sense of messy, incomplete, or noisy data.
He demonstrated how GAEC uses a unified, AI‑driven approach to solve these challenges across Radio Astronomy, Health Diagnostics, and Petroleum Exploration.
He began by posing a simple but powerful question: How do scientists uncover reliable information when the data they work with is imperfect? To make this clear, he walked the audience through examples from each field:
- In Radio Astronomy, signals coming from distant galaxies are distorted by the Earth’s atmosphere and human-made radio interference.
- In Petroleum Exploration, especially in places like Ghana’s Keta Basin, where no core samples exist, geoscientists must rely on indirect measurements (well logs) that are full of noise from tools and the environment.
- In Health Diagnostics, reported malaria cases do not provide the full picture. Many infections go unnoticed, and medical images of blood cells are often affected by uneven lighting and staining.
Despite their apparent independence, all these fields struggle with the same three issues: noisy measurements, missing information, and extremely complex data.
Under radio astronomy, Dr Narh showed how a new deep-learning model called R-Net dramatically improves the detection of radio interference, helping astronomers get clearer views of the universe.
Like the popular U-Net model, R-Net estimates the likelihood of interference in telescope data, but it performs noticeably better. Across multiple real-world datasets (HIDE, MeerKAT, KAT7), R-Net achieved very high F1 scores (up to 0.97), consistently outperforming existing methods.
This improvement allows astronomers to more effectively separate genuine cosmic signals from human-generated noise.
In the energy domain, he demonstrated how AI helps interpret limited geological data from the Keta Basin.
First, a mathematical technique called the FFT is used to clean noisy well logs, removing irrelevant high-frequency spikes while preserving important geological trends.
These cleaned signals are fed into a 1D convolutional autoencoder, an AI model that learns hidden patterns in the data.
By clustering the model’s compressed “latent” features, the system can automatically group different underground rock units, even without physical samples.
This creates a coherent picture of the basin’s geology, generated purely from AI analysis of imperfect data.
In the health sector, Dr Narh used AI to uncover malaria transmission clusters in Ghana (2014–2023). Simple maps showed that malaria risk was not uniform across the country, and by applying a Gaussian Mixture Model, the AI identified overlapping clusters and tracked their stability over time.
He also highlighted the difficulty of identifying malaria parasites in blood cell images. A PCA analysis showed that infected and uninfected cells often look too similar for simple models to distinguish.
To address this, he introduced a Global-Local Inference Method that detects subtle, localised signs of infection to improve diagnostic accuracy even when images are noisy or inconsistent.
These examples show that, across all sectors, GAEC’s work highlights a consistent mission: recovering reliable signals from imperfect data to guide the national decision-making process.
The presentation’s key insight was that this is a method-driven endeavour, not a domain-specific one.
The computational principles of denoising, feature extraction, and unsupervised clustering are transferable across radio astronomy, petroleum exploration, and health diagnostics.
This unified philosophy is the foundation of GAEC’s interoperable AI frameworks.
Furthermore, the Commission’s remote sensing capabilities extend beyond agriculture to water resource monitoring, land cover change detection, and pollution tracking.
By applying AI-enhanced anomaly detection to satellite data, agencies can strengthen early-warning systems and deploy field inspections more efficiently to help safeguard Ghana’s natural resources.
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