EPISODE SUMMARY:
This episode examines how data systems and AI can transform food-security planning in Sub-Saharan Africa. The discussion highlights major data gaps such as outdated soil information, limited environmental monitoring, weak yield records, and shows how tools like IoT sensors, satellite data, and predictive analytics can enable more proactive, evidence-based agricultural policies. It also stresses the need for inclusive frameworks that connect farmers, researchers, private sector actors, and policymakers, ensuring smallholder farmers benefit from accessible, affordable technologies. Ultimately, the episode envisions a future where localized data, digital innovation, and strong governance shape a resilient, sustainable, and equitable agricultural ecosystem for the region.

Dr. Oluwanbepelumi Olanubi: Hello everyone. Welcome to another insightful episode of the Kingsgate Brief. As you all know, this is the podcast series of the Kingsgate Advisors Institute and I come in every month with guests who are practitioners, experts, professionals and even cutting-edge leaders in their field to discuss some of the burning and major issues affecting Africa and the global south countries, and today is nothing short of that, it’s another insightful episode to discuss something very important to Africa and even to the countries in the global south in general.

As you know, my name is Dr. Oluwanbepelumi Olanubi. I serve as the Executive Director of the Institute and if you haven’t already, at this point, I want to invite you to subscribe to our YouTube channel, follow our LinkedIn page, and our Instagram page, so that you can be the first to know when we record and upload another insightful episode of this brief. Check our website online to have a deeper view into those things that we’ve been doing, there you can get to know what we do within the Institute and see how you can be a part of the community. I’m sure there’s something we have for you and there’s something you can learn from us. So, to take the conversation further today we are talking about something that is very important. We’re talking about data for policy, and how Artificial Intelligence (AI) and advanced technologies can strengthen food security in Sub-Saharan Africa?

To set the conversation and the tone, SSA faced one of the most pressing development challenges of our time, which is food security, a growing population, climate change, and resource constraints. Despite the central role of agriculture in most of these African countries, we’ve seen that there have been data gaps, insufficient systems, and fragmented policies that have continued to undermine the productivity and even the resilience of the region. Advances in AI have also come to play a significant role in helping to solve this problem.

AI data analytics and data technology offer an unprecedented opportunity to strengthen evidence-based policy making in agriculture, from predictive yield models to IoT enabled sensors, and even to remote sensing data. These tools have become very useful and has been transformational in how government  researchers and even farmers anticipate and mitigate risks. This conversation is going to explore this and much more to see how AI systems can advance policy design, improve coordination and drive agricultural transformation across the region and beyond.

Why does this really matter? It matters because food security is not just an agricultural issue, but it is an economic, social, and governance issue, at the heart of Africa’s development. Data-driven policy making can bridge the long-standing disconnect between research insights and field realities and even national strategies. However, the challenges remain weak data infrastructure, low digital literacy, inadequate governance structures, and limited collaboration between the public and private sectors. All this has continued to hinder progress by understanding how technology can build that bridge and can help us address these long-standing barriers becomes so important and pertinent.

It’s very crucial to achieve this inclusive sustainable food system across the region. To discuss this and more, I have a very brilliant guest, and you will take it by my words, a researcher, an engineer and also it’s a privilege that he is a member of our board of directors. Today we have Dr Temitope Odedeyi.

Dr Temitope Odedeyi is a senior research fellow at the University College London, and is the leading voice in the intersection of engineering, digital technology and sustainable development. He holds a BSc in electrical and electronic engineering from Olabisi Onabanjo University in Nigeria and he held both his MSc and PhD from University College London. He is a recipient of the prestigious Nigerian Presidential Special Scholarship for Innovation and Development.

His research interest focuses on analysis and design of high-speed telecommunication devices and electronic instrumentation with applied interest in food quality prediction and precision in our culture. In 2023 he was awarded the Royal Academy of Engineering Research Fellowship where he leads a groundbreaking 5-year engineering for development project. This initiative is developing IoT-enabled technologies that support data-driven farming and improve cassava yields in sub-Saharan Africa, advancing food security and climate-smart agriculture. Dr Odedeyi’s work blends between continent innovation with real-world impact, particularly in Global South where he is hoping to reshape the future of agricultural productivity through digital transformation and smart systems.

Hello Dr. and thank you for joining us today.

Dr Temitope Odedeyi: Thank you very much for having me, it’s a privilege to be here indeed.

Dr Oluwanbepelumi Olanubi: All right that’s so good. So, by the end of this conversation we would have a clear understanding of how data and AI technologies can shape smarter agriculture and food security policies. Also, you’d have a deeper insight into institutional quality and ethical considerations in implementing these technologies. You would also be exposed to evidence-based perspectives on how sub-Saharan Africa can build sustainably in a tech-enabled food system. And lastly, I believe that a vision for regional collaboration in leveraging data for inclusive agricultural transformation is one of those things that you will be taking note of today. So, it doesn’t matter if you’re a policy maker, if you’re a researcher, if you’re a data scientist, a development practitioner, an agricultural expert, or you’re just a student, this is one conversation you want to spend some time to dive right into it.

We are so ready for you, and I’ll say let’s get started. By way of starting Dr., before we go fully into the conversation, I want you to give us a bit of a background on what you’ve done, what you’ve been working on, especially in terms of the agricultural technology domain. I know you’ve done a lot of work in that area; can you just take us into your world of that so that we can understand the picture of what you’ve been working on.

Dr Temitope Odedeyi: Thank you very much, and I think that’s a very good place to start. So, my foray into agricultural technology or agri-food electronics as we term it technically, started around about 10 years ago. By then I was still an early PhD student, well it started actually with a competition, and the competition was set up by my university, the University College London, and the oldest agricultural research institute in the world, Rothamsted, they’re kind of very close to London here. 

It was a hackathon event to develop solutions for agriculture for Africa, and at the start of that competition really I didn’t have much in my mind of exactly what I wanted to do, but the driving force was me being an electrical engineer. They were trying to synthesize or to develop solutions for agricultural technology, and I didn’t have an idea but it would be like a crime for me not to be involved.

That was my first step, and thankfully the idea we came up with then, myself and a team of three other PhD students, won that competition. The idea then was how can we develop a low-cost handheld device to do crop quality measurements that’s tailored to the particularly important African crop, which is cassava. And from that step we got not a lot of money then, it was £10,000 in grants, and that was the initial step that really set me up in that direction.

And we just wanted to continue to build momentum in that area, and the next major step we had was a £100,000 grant from the university, also to take the idea that we started with and to make it a bit more robust, to get it into prototype stage, into a proper device. Then, step by step we got also, I was involved in another big grant that was funded by the Horizon Europe framework. This was in a collaboration with 13 other institutions across Europe and Africa called NESLA.

That was a big step, because then, we started to look beyond the limited scope of what we can do with cassava. It has been my mandate crop for a few years, what can we do with cassava, to going to what can we do with different contexts of agriculture under what is called the One Health framework. Essentially, how does quality and safety in how we produce food, both crops and livestock, affect human health. That kind of broadened my horizon significantly, and after that, as you mentioned earlier, I was privileged to have the fellowship, the Royal Academy of Engineering Research Fellowship under the Engineering for Development Research Framework, which enabled me now to push much more across the application of, or adaptation of technologies and development of new technologies targeting Sub-Saharan Africa agriculture. So that’s kind of like a very high-level summary of my journey so far.

Dr Oluwanbepelumi Olanubi: Interesting, what started with just what we can call a small competition has become a major research product that is being used across Africa. That is really beautiful. Now, to build on that, I just want to also ask you, across SSA now, agriculture has remained the backbone of many economies, yet food insecurity persists.

How can advanced data systems and AI help governments and institutions design smarter, evidence-based agricultural policies? What can we do?

Dr Temitope Odedeyi: Okay, that’s a very interesting question. First of all, I have to kind of present a caveat. I’m going to be speaking mostly with the heart of an electronics engineer, or like an agri-food electronics engineer. I have some experience, or at least some knowledge of policy, how policies are crafted, but I’m not an expert there. So, if I step on a few policymaker toes, please bear with me. But I look at this question on four fronts, right?

I think many of us, even whether we have a policy background, engineering background, or we’re just keen observers, we’ll be able to identify these four points. The first one is that we have mostly a reactive approach to problem  solving across many sectors, where it’s particularly dire in agriculture, rather than a proactive approach. Hence, our responses tend to be more reactive than proactive.

For instance, we have cases where, there’s a drought, and then the agricultural production fails significantly in a particular year. And then we quickly start to move to how do we rescue the situation? But robust data systems can actually enable a more proactive response approach, where we can predict that there’s a high likelihood that we’re going to have this kind of problem by this period in this year. Then we start to look at how can we develop a system to help us minimize the shocks and the effect on livelihoods, both for the farmers and of course also for the consumers.

That’s a key area where that data can actually make a huge difference, where we become more proactive in our responses. Another area is where we can actually do more long-term planning, and more long-term structuring to support the agricultural system. For instance, I was reading a report and it says even up till date, a lot of the responses that we have to agricultural shortfalls and challenges tend to be more around subsidies, and the ripple effect of that is we end up creating more problems than we solve. For instance, we have like, say maize fails or rice production fails in a particular year because the rains didn’t come on time, right?