Practical AI – Episode #175

🌍 AI in Africa - Agriculture

with Leo Mutuku & Godliver Owomugisha

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In the fourth “AI in Africa” spotlight episode, we welcome Leonida Mutuku and Godliver Owomugisha, two experts in applying advanced technology in agriculture. We had a great discussion about ending poverty, hunger, and inequality in Africa via AI innovation. The discussion touches on open data, relevant models, ethics, and more.



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Welcome to another episode of Practical AI. This is Daniel Whitenack. I am a data scientist with SIL International, and I’m joined as always by my co-host, Chris Benson, who is a tech strategist at Lockheed Martin. How are you doing, Chris?

Doing very well today, Daniel. How are you?

I’m doing great, because we have a really exciting follow-up show. We’ve been doing these sort of spotlight shows on AI in Africa, and we’ve been really pleased to partner with the Open for Good Alliance, and FAIR Forward, the Makerere AI Lab and IDRC on these shows. It’s just been really great to feature some of the amazing AI work going on in Africa.

We’ve got a few guests with us today. We’ve got Joyce Nabende, who is the head of the Makerere AI Lab, who has been joining us as a sort of co-host on these shows. Then we’ve got Leo Mutuku, who is a research and AI lead at the Local Development Research Institute in Kenya, and then we’ve got Godliver Owomugisha, who is a senior lecturer and researcher at Busitema University. Welcome, everyone!

Thank you.

Great to have you with us. I’m wondering – I’ll maybe pass it over to Joyce to start us out with… I’m wondering if maybe based on your experience with the Makerere Lab, which I know has looked at AI and agriculture, which is what we’re gonna talk about today… Maybe if you wanna kick us off into that subject.

Yeah. Thank you, Daniel, and thank you, Chris. It’s good to be back on the show here with you guys, and I’m very excited as well that we have Leo and Godliver on the show today. I believe it’s going to be a very interesting discussion that we’re gonna have today. So as you mentioned, the Makerere AI Lab has been doing work in AI for agriculture; it’s our strongest research work that has been going on in the Lab, and I feel excited that we are going to hear particularly about AI and agriculture from, first of all, Godliver, who has done her Ph.D. in AI for agriculture, and the experience that she has, and the thoughts that she has about the topic… But also from Leo as well, who has done a lot of research in AI for agriculture. So maybe just to start - I will start with Godliver. Godliver, you’re welcome to the show, once again.

[04:23] Thank you very much, Dr. Joyce, and everyone who is on the talk today.

Alright, thank you. So maybe my first question to you, Godliver, is can you really just give us a general introduction to AI in agriculture? And specifically for the African context. I believe that the African context is a unique one, because we have the highest percentage of smallholder farmers in agriculture, and people depending on agriculture for their livelihoods. So what is it about AI in agriculture that you think is very important for the African context?

Thank you very much once again. Yes, artificial intelligence in agriculture, specifically in Africa, is really an interesting part. When I started to do my research, I could not imagine how many challenges we have here, especially when it comes to outlining some of these applications that we’ve developed, and scaling down to regions, or talking to different stakeholders. You realize that there are challenges that you never even imagined to see. When you hear a smallholder farmer talking about things that you imagine maybe you could increment, or you could have [05:47] to them.

So to me, I think our challenges are very unique. Understanding the challenges that are affecting our smallholder farmers locally will give us the base assistance that we need here in Africa.

Yeah, thanks, Godliver. Maybe to Leo - well, just thinking about what Godliver is mentioning about the unique challenges that we have in the African context… Maybe can you briefly talk about these, but also looking at the broader context of AI in agriculture, particularly for Africa?

Thanks, Joyce, for the introduction. When I think about why AI plays a unique role, and especially in agriculture, and in the sub-Saharan context, one thing I want to point out is the issue of climate change. So when we look at the continent, we are being affected by climate change, and as you mentioned, a majority of the agricultural practices are by smallholder farmers. And traditionally, smallholder farmers have been relying on intuition and experience to farm and to take themselves through the planting cycle. However, when you start thinking about climate change and the need to move towards climate-smart agriculture, I think AI provides opportunities to help smallholder farmers cope with these effects of climate change, where rain-fed agriculture is no longer very reliable, as it was in the past.

And then, again, granted that smallholder farmers are the majority of the agricultural producers in our context, they tend to mainly farm for subsistence farming… So ideally, this means that their issues, when they are not able to have a plentiful harvest, when it comes to food and nutrition security. So AI I think supports interventions in the agricultural space that can be even provided at a granular level, at their household level, as opposed to typical interventions, which tends to just look at a region… But AI can really support those subsistence farmers at their household level.

[08:09] I have a quick follow-up for you… For those in the audience who may not have had to experience food insecurity in a direct way - we have a global audience, and some people have to deal with that issue, and some don’t. For those who don’t, can you kind of describe a little bit about what the implications of that are, so that we can kind of understand the problem better, before we dive into how AI is helping to remedy that?

So in terms and food and nutrition security, when I mentioned that smallholder farmers, and especially in sub-Saharan Africa, are mainly subsistence farmers, it means that first and foremost the crops they produce are to feed their families, and then if there is any surplus, that is what is sold.

Typically, in more food-secure nations, you tend to be able to have large-scale farming that provides food and nutrition for the general population. So you purchase your food. Purchasing your food, you are actually producing it, and then what you’re selling is a surplus. So ideally, if there is crop failure, let’s say due to drought, due to disease outbreak, then it means that, as a household that is relying on subsistence farming, you’re likely to go hungry if you don’t have the income to purchase the supplemental nutrition. So that is why food and nutrition security is a very important issue in this context, and why it needs to be addressed with immediate efforts, due to issues such as climate change.

To think about how maybe advanced technology and specifically AI can help with some of these issues, some people might be thinking, “Well, AI requires data. How is their data related to maybe small farms in the African context?” What’s available for us to work with, and that sort of thing. Maybe I’ll pass it over to Godliver, and maybe Joyce as well, since I know her lab works in this area… Godliver, could you start us out and maybe just describe generally what are the categories of what people are trying to do related to agriculture with AI or advanced technology methods, and what data is sitting behind that, what data is enabling that?

Thank you very much, Daniel. Interesting question. Yeah, so there is a lot of data that is surrounding this, apparently, to understand some of these barriers in agriculture. Mostly, I could say [unintelligible 00:11:00.08] crop data that we see, but also the practices these farmers are applying on their farms. [unintelligible 00:11:09.24] about it recently. Yeah, so you realize the challenge - for example, we have crop pests and diseases, which we’ve been trying to handle in a long time. But you also realize that that may not be the major problem, or the major challenge some people are specifically facing; or they even don’t know what it is.

So I don’t know – talking about more data, I think it’s going to move from what we see in the gardens, but also how these farmers are trying to relate, or the practices they use, and where they get resources, and which types of resources they get. Maybe that is the farm materials, and all that. So if we’re to really intervene in there, to understand why is it we have this many problems in agriculture, I think we have to go back to understand maybe the data sources. I think Joyce can add on more here; she’s been experienced more in data acquisition. Yeah, thank you.

[12:29] Thanks, Godliver. I think you raised very important issues around data, also just thinking about what Leo mentioned earlier, that the farmers have the knowledge, they have this information, but maybe it’s not captured… Like, you know, when is it going to rain, when am I going to harvest. I always think about that – like, that’s indigenous knowledge, that the farmers know and have, and it would be very good to capture that in a systematic way. That can be incorporated with other data sources that are openly out there, that can help the farmers.

But also, Godliver, you hinted on the crop pests and diseases. I know that the work that we’ve been doing has really been around capturing image data for crop pests and diseases, and providing the potential for building AI models… But there are also data sources; some might be that the farmers don’t have access to them, but some that they might have… For example, if we are looking at the farmers, what is important to them is my soil, “Do I have good soil?” So information or data around their soil, soil health monitoring data… That’s something that’s very important for them.

There’s also satellite imagery data, although as a smallholder farmer I might not have access to that data, but I might have access maybe to the output of that data, depending on how advanced the people who are building the technology can be able to build out the applications that the farmers may have access to… But that’s also data that’s available, that can be used by the farmers in building out the AI applications for health.

Maybe moving forward, to Leo - so Leo, in your experience, what datasets do we have, especially in the African context, because I think this is a unique context, that if you were to build an AI application in our context, that means that you probably have to collect the data yourself. If you are to pull a dataset out there, that might not work for us, in our context. So what is your thoughts generally around the data, and using this data for building applications in our context?

Thanks so much, Joyce. I agree with you and Godliver on the data sources that you mentioned, that might be available in terms of supporting the building of technologies to support agriculture. In our context, we have tried to use mobile phones to basically collect groundtruth data which typically is not available, as you mentioned. Datasets out there might not be relevant for our context.

So we are trying to see what are the easiest tools and technologies we can use to collect this data directly from the farms themselves, from the farmers… And we’ve found that mobile phones, using tools such as the GeoODK app, really help in maybe creating proxies for some of more advanced data collection tools such as the Garmins, which might be expensive in our context… And we’re using this to collect data, for instance, on farm boundaries. It’s very important to calculate an area and the cultivation, and what is being cultivated within these different lots, so that we can try and estimate, let’s say, the yield production, the access to inputs required for bountiful harvests.

[15:51] So farm boundaries are one of the datasets we also try to collect. The other is the access to specific inputs. When we talk about climate-smart agriculture, in this case we would like our farmers to access hybrids of seeds, for instance, and fertilizers that work well in their context. So access to input data and what inputs are being applied in different parts of farming communities is a very important dataset to collect.

You mentioned the use of things such as photos to be able to identify pests and diseases as part of farm management practices, but I would also want to talk about another dataset that we find very important, which is commodity prices. So what are the market prices of these crops once they are harvested at different times throughout the year… Because if those are predictable in a way, then it’s easier even for farmers to estimate what their income would be once they take their crops to the marketplace.

So I think across the general ecosystem, different stakeholders have a role to play to make these datasets available. For instance, the market prices might come from government sources, and yield estimates, while a lot of projects are trying to see how we can make it easy to collect this groundtruth data from farm samples that can be used to train artificial intelligence algorithms and tools.

Okay, so as we move forward here, I’m fascinated by this discussion about the data side of things, because that’s really in the practicalities of this… And I wanna follow up with you, Leo - you were just discussing about the various datasets, and as I explore the LDRI website, I was learning about this African Open Data Network; particularly struck by this kind of line about unless the right people have the right data, in the right forms, to help them make the right decisions, our development goals will remain unattainable. So I’m wondering maybe if you could kick off a little bit of discussion about why open data and networking and community is important as it relates to working in this area. And then Joyce and Godliver, if you have any follow-ups on that, I would love to hear them as well.

[20:28] So when I think about open data, it is in a way democratizing access to data… And this means that if some investments are put in place to collect this data, it’s still beneficial to our wider group of people that just those who collected the data.

Now, specifically in AI and agriculture, as I’ve mentioned earlier, it’s quite difficult to get accurate groundtruth data to support interventions. So there are a few projects now that are supporting the collection of that data; this data has been collected from a small sample of the farms in the region, so the applicability of this data is only as useful as whether it can be reused on repurposed in other areas, or at least used as training datasets for algorithms that may not necessarily impact the places where the data has been collected, but are for similar contexts.

Traditionally, we have been saying that one of our biggest development challenges here in Africa is the scarcity of data. So open data I think creates mechanisms and communities around sort of meeting that data gap, and being able to support further application of datasets.

You’ve mentioned the issue of community, and I find the concept of open data serves communities in two ways. So there’s the community of the producers, that can pool together resources, they can pool together data, and reused them, reproduce whatever experiments or research that has been done, without having to expend resources. But at the same time, open data I think has a close relationship to what is usually called citizen science, that we can also encourage the communities themselves to contribute to the creation of this data, and then when it’s placed in an open format and in these open repositories, it’s also accessible back to them, either as outputs of technologies applied on this data, or they can use it directly.

So I think open data creates this community resource that, again, preserves some of that indigenous knowledge we alluded to earlier, but at the same time promotes the development of new technologies where there might be sparse data previously, because of this increased availability or relevant datasets.

Thank you very much, Leo. I think you’ve said it all in terms of open data sources. From my experience actually, if I look at when I started my masters research and even Ph.D. work, there was really nothing to do with open data source that I could to implement some of the ideas that I had. So we started to collect our own data. Of course, we’ve also been working closely with different research institutions like [unintelligible 00:23:49.18] who supported us, but here we are moving to communities like the previous, because as I have said, farmers helping us with this information which we are collecting together, and also unveiling to other people.

[24:09] So I think really having an open data source in these kinds of problems that we have in agriculture would really help a lot in coming up with data technologies. Also, us putting down the protocols that we are using to collect this data, so others improve on them or reproduce this in data that we are getting is one key thing that we are doing apparently at the AI Lab. Joyce, I think there is an important data source [unintelligible 00:24:46.09]

Yes, Godliver, you mentioned an unconventional data source that people don’t really think about… But just to get back to one of the points that you mentioned, especially because for us, we are in a university setting, and so students of course will come up and they’re very excited about building applications for AI in agriculture… And then where do they start? They need the data. So I think this whole topic of open datasets for AI and putting them out there and making sure that there’s proper documentation, like Godliver has said, for these AI datasets - it’s something that is very key and important to us in the Lab, because that’s the hinging point where we start to build the applications, and making them available will encourage more people to come on board, more students to come on board to have applications that they can be able to build for agriculture.

We try and make sure – we’ve thought about having the datasets as diverse as possible… Godliver mentioned the radio dataset - that’s a unique one, which is also very sensitive, but one of the projects that I mentioned earlier on that talk was where we are trying to build speech recognition models for radio data, particularly looking at agriculture, because we know that the smallholder farmers listen to radio, they will call into radio, and then they’ll make sure that – you know, they think that they will get help from there, because the agriculture experts can hold talk shows. And that’s something that doesn’t come easily to us. If we’re looking for a dataset, that’s not a dataset that you would conventionally think about… And I will pick up on news, but we think that it’s unimportant dataset.

So just thinking about open datasets in general, we need to think outside the box and think of what are the unique datasets where we think we’re going to get data, but also where we think that these are datasets that will eventually also help the farmers… Because if they know that “This is a unique radio dataset and people are listening to me”, they will be more enthusiastic to try and get help through that source… But it comes back to the developers to ensure that that data is mined out, formatted in a way that is representative for the agriculture experts to respond back to the farmers. So it’s kind of like a two-way that we think about the open datasets, but we also think about the farmers from which we are collecting the data; the farmers for which we are building the AI models for, again.

I’ve got a quick follow-up… It’s one that’s been developing over the course of the episode in my mind. If I go back to my question early on to Leo about defining food insecurity - and I think that was a really useful explanation for me, and it made me realize how fortunate I am that I’m not living in a situation where food security is one of my primary motivators. And it might be that way for many people in our audience, and it’s really gotten me thinking about the challenges that subsistence farmers are facing, that I’m probably not thinking about on a day-to-day basis in North America, where we have these large corporations that drive farming, and where AI in that context of farming is typical because there’s lots of technology approaches just kind of built-in to that farming culture at this point.

[28:19] I’m curious, as you’re working with subsistence farmers that are trying to produce enough, and they’re dealing with these very basic concerns day to day to provide the food for their communities, and even just for their families, and the risk of going hungry, and children going hungry… When you’re bringing – it seems like a big jump to say… This is a very immediate concern, that affects people in a very direct way, and we have these amazing technologies that we’re talking about… Is it culturally a jump to get a community to say “Help us with open data. Help us apply these tools to the vital work that you’re doing”? Is there any challenge in buy-in or understanding to take the most advanced technologies in the world and apply them to the most basic food security issues from a cultural sense?

So of course, with any technology adoption, one of the biggest issues is the issue of trust. So we can’t just consider ourselves experts, dive into a community and just apply technologies and try to change their way of life directly. So for us, even just practicing AI in this agriculture space has been quite a journey, that started with us building trust in these communities. And we’ve used a very interesting approach whereby – so another challenge that might not necessarily come up in this conversation is the issue of extension services. So we tend to find that for farmers to learn about new science, new technologies, or what to do with disease outbreaks, they tend to rely on a government agriculture officer, who passes on this information maybe in a way that is digestible to them. So in this sense, they act as information intermediaries who - I mean, based on new research that the government wants to apply in local contexts - would pass on this information.

Now, there tends to be a shortage of these knowledge workers who tend to – I mean, ideally, we should be having a ratio of one extension farmer to 400 farmers, but we tend to find that right now there’s one to 10,000 farmers, or even more. So it’s very difficult to start passing on this information. So you can imagine how that becomes a problem when introducing outputs and recommendations from an AI.

So what we’ve done is that we’ve actually built a community of what we call care farmer educators. So in each village that we work in, we have identified farmers who we treat as champions, and we train them on agricultural best practices, and then they tend to use – it’s almost like a training of trainers. Then they go and train on their fellow farmers on (let’s say) these new approaches, and these new agricultural practices that might improve their harvest.

So you see, by working with trusted members of the community, even as we start introducing our outputs from an AI, or if we need their support in collecting data, we have already established trust, because we are working with community representatives. And you know, we’re not just (again) passing information top-down, we also get to spend time with them and understand what their challenges are when it comes to what’s going on in the community, and what approaches to best use to get the community to adopting your practices.

[32:06] Now, that’s just one side of things… The other very important stakeholder when it comes to agriculture is the government itself. In the case of Kenya, agriculture is a devolved function, so ideally, the way you’d have states running issues in the U.S, here we have what are called counties, and governors running agricultural issues. So we also take time to also bring the county governments into the fold when it comes to explaining to them what the AI can do, and using them as informediaries, as well as knowledge holders to design and develop the AI.

So for us, we are not just sitting in our ivory tower in the city and building technologies and then taking them back to the farm; we are actually, through this network of peer farmer educators, as well as the county government and other local communities, using them as co-designers and collaborators in the design of the AI. Because again, with any AI, what matters is the output. So we will not, for instance, give them metrics as output. Maybe fancy dashboards do not work in that context. But if we are able to craft the messaging of let’s say the recommendations for instance, saying that we have observed through satellite imagery and through the groundtruth data that there’s a likelihood of crop failure in the near future - you know, you just don’t show them graphical images. What would be more important is how to design the messaging around maybe potentially do not rely on rain-fed agriculture, but it is time to irrigate.

So at that point, designing how those messages would be crafted and how the outputs of the AI has reached the communities require effort that’s not just by us, technology developers… So that’s why I love this approach of design thinking and how versatile it is in different contexts, including in us building AIs for agriculture.

We’ve talked a lot about generally AI and agriculture, the data involved in AI and agriculture… I’m really interested in some of the sort of success stories, or challenges you all are currently tackling, so maybe I’ll pass it over to Godliver, and the others can join in afterwards… But Godliver, I’d love to hear about some of your own work; in particular, I’ve seen some of your published work around detecting diseases in plants, and using spectral data, and even diagnosing bacterial wilt, and other things… So I’m wondering, could you give us a highlight of a couple of the projects that you’ve worked on, and what the challenges were and the results were?

Yeah, thank you very much, Daniel. Specifically in that area, about crop disease detection - so over time, as we worked on these tools to put them on the mobile phone, and [unintelligible 00:35:38.21] But then we realized there is also a very big challenge. Usually, if you look at some of these diseases, they will come out visually when the status quo [unintelligible 00:36:00.04] as we had from the start. Most of these diseases can even take up to 100% of the damage. If you look at the cassava crops, [unintelligible 00:36:15.03]

[36:20] So instead of waiting for these visual features which we can see with our eyes, which you can see through the mobile phone, think about other technologies that will help us to detect these diseases that are very early stage. That’s how spectrometry came in place. This is more to do with using light; in a layman’s language, like how you go to an X-ray system, and it scans through your body. The same way we are scanning through these crops. So we are able to read information which we can’t visually see with our eyes, and also to experiment, actually; we’re able to detect that you can see the disease at least six weeks once the plant has been infected.

Some of the challenges we had is some of these technologies are very expensive. So if we talk about [unintelligible 00:37:25.09] back to a smallholder farmer, this technology is over thousands of dollars. And so no one can use it; even interpreting it is difficult. So our challenge here and our talks here is to come up with low-cost technologies that we think should be understandable by these people we are developing the products to, the farmers.

So that’s the state where we are, and we’ve had a few prototypes which we are building on again. That relates to other different crops. So we started with cassava, and now we are moving to more different crops. So that’s what I can say about the spectrometry technology, which is very promising, apparently.

Yeah, Godliver, you mentioned a very important point there, that with spectrometry we look at the diseases before they manifest, right? Many of the applications that are out there focus on image data when the diseases are already on the leaves, but you try to do that early prediction. I think that’s very paramount research work, such that then we don’t wait for that damage; once the disease is manifested, they spread really quickly, and for a smallholder farmer, that’s such a big problem for them as well.

I’ll also probably just highlight one topic, one research area, also to allude to what Leo said, the issue of having – we call them agriculture expats in Uganda; the ones that try and reach out to extension farmers, and reach out to the individual farmers to help. So if a farmer has a problem, they will go to an extension farmer to have the extension farmer maybe come to their garden, look and see if there’s a problem or not, and provide a solution. But the ratio, as Leo pointed out, is usually very big; one expat cannot attend to all other farmers.

I think in one of the first episodes we had Mutembesa talking about a mobile application that we have built in the Lab called [unintelligible 00:39:32.17] where we have tried to provide this connection between the farmers and the expats, such that the agriculture expats can easily be able to reach out to more farmers… But also, getting back to what Leo talked about, that we have the champions who are also farmers - they are more knowledgeable than other farmers, and are also able to come in and help their fellow farmers in their community, to try and help and do that.

[40:00] So one of the applications that we are currently working on is to try and build a recommender model, where the farmers on the network can come and post a question, and the application is able to automatically provide or give them a response. If someone says, for example “I am in a certain area and I want to find clean planting materials. The season is coming up, I don’t know where to get clean planting materials.” And they don’t know who to contact. But through the application, one, it’s able to respond and say “Oh, if you need clean planting materials, maybe this is where you can find them.” Or maybe “I have identified these symptoms in my crop. What disease is it?” and the recommender model is able to provide a response out to the farmers to say “Okay, if those are the symptoms that you have, it’s probably this disease.” But also, those are the things that can easily be flagged out to the agriculture expats.

But I like also the peer-to-peer learning. When we have that network, when the expat is not available, we can have another farmer who’s also knowledgeable, who can quickly respond and come back to the smallholder farmers and provide a solution for them. So I imagine that there’s a lot of potential for this work. What is important as well is to try and see how to provide these solutions in the local languages, that the farmers understand. Many of them are not very knowledgeable in English, and so if you come and you want to provide solutions in English, that might not work. I think for Kenya it’s Swahili; that’s the main language, and so you have to also target the languages that the farmers speak into, or that they understand better.

So those are some of the things that we are trying to do, and build the solutions out in the local languages, that the farmers understand, through natural language processing technologies. Leo, any feedback on applications from Kenya?

Yes, so I had started describing our project earlier on, but just to go into a little bit more detail - we are trying to build a localized early warning system for the nutrition security. The idea is that existing early warning systems tend to basically look at agriculture from a regional perspective, and by the time let’s say there’s a likelihood of crop failure and the information is reaching the farmers, it’s the same issue that Joyce mentioned; I mean, you can already see that my crops are failing.

So if we could pass them on information, as well as possible interventions and remedies in near real-time, or just-in-time, so that they can change their current agricultural practices. So that is the motivation for the project we are doing. And for us, it presents an interesting challenge, because 1) we are trying to build it for smallholder farmers, and of course, the policymakers who work with them. And the issue of smallholder farming is very unique, because unlike what Chris was mentioning, there have been those commercial farms; farms there tend to be very small, about 0.5 to 3 hectars in size. And then at the same time, a lot of farmers tend to do practice of intercropping. For instance, on the farm they may not just have maize, they might have maize and beans. So how do you detect maize, how do you detect beans, and how do you detect whether they’re getting the right amount of moisture, whether they are likely to maybe dry out, or you need to change, apply fertilizer, apply different inputs to prop up the yield. So we are trying to collect groundtruth data with tools that are very locally available to farmers and to their representatives on the ground, so using mobile phones, using the typical camera on a mobile phone, and trying to see if we can use this groundtruth data and reference it to app observation data, in order to basically start predicting instances of crop failure… Again, using a very similar process to what Joyce was describing for the disease interventions, but now here in general plant health, and especially as related to climate variances… And then trying to see whether we can detect that early enough, and then in near real-time send messages with interventions to both the policymakers, but also to the farmers.

[44:46] So for us it’s a very interesting challenge, given that, again, we are trying to use low-cost technology, but figure out sustainable ways to scale these AI to various seasons, and monitoring from maize to other different crops that tend to be what farmers plant for subsistence.

And maybe just a small addition - given that we are trying to also estimate predicted yield, such data can also be used in other use cases, such as insurance, and access to financial services for these farmers, because if we are able to estimate their yields, then it’s very easy to offer them the right financial services, that make sense for them.

So as we finish up, I would really like to get each of your takes on what you’re excited about in the future. I’d like to start with Godliver, and then maybe get some follow-up from Leo and Joyce about where are you excited about things going in the short or longer-term, if you think that there’s a longer-term play there. What is AI doing that you think will really impact things going forward. Godliver?

Thank you so much, Chris. Really, the future should be very exciting. If I see in a short time with my experience how these farmers are adapting to some of these tools that we have, I think it will be really good in the near future. I only had a concern, maybe to Leo or someone else - she mentioned about bias in terms of technology… And also, we’ve had some of the assistants incentivize – like, give an incentive to these smallholder farmers to use this tool.

So I don’t know how this is going to work well in the future if we still have incentives in place, but [unintelligible 00:46:57.16] challenges say in languages, and also sometimes maybe typing… Or if we could just have visual graphics into these tools, that maybe when someone’s talking, there’s something happening with their tool they are using, so it could make life easier in the future. Thank you.

Leo, how about you?

I think I’m most excited about the potential for solving this grand challenge of food and nutrition security, but also the potential for AI to increase local incomes. I think when there’s predictability in the production process, from when you plant a seed to when you get a return on your harvest, then it’s very easy to create plans around your farm, and also uplift your livelihood. So for me, that is very exciting.

Also, I think just the ability to bring more people into the fold, hopefully, given that there’s more data available, then issues such as marginalized communities or increasing gendered approaches to where women are considered also and are brought into the fold, where communities have agency over how [unintelligible 00:48:33.12] self determination I think those for me are really exciting things about AI, and especially AI that is locally designed, developed and built, because hopefully they understand the contexts, the nuances, and acknowledge the biases that exist.

Thank you. And Joyce, you have the final word. What’s the future like for you?

Yeah, thanks. I think I’m also very excited… You know, just looking back at what we’ve been doing, and how the farmers are also very excited with technology; I feel the key is to involve them early in the development of the technology, such that there is also early acceptability of the tools that are coming up.

I think that AI has great potential to make the farmers more resilient to agriculture threats, to help in prediction of yields, and what Godliver is doing around early disease detection - I think that these are all technologies that can be enhanced, but also adopted by the agriculture expats.

So I think bringing all the key stakeholders on board as we are building and deploying of AI applications in agriculture is something that’s very important, to have the growth of AI and its use for smallholder farmers across the globe. Thanks.

Yeah, thank you all. This has been a fascinating conversation, and just really important work. I appreciate all that each of you are doing in the community you’re building, and thank you for taking time to join us on the podcast. It’s been a pleasure.

Thank you.

Thank you for having us.


Our transcripts are open source on GitHub. Improvements are welcome. 💚

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