3. Smarter decisions for our oceans and soil (Special edition from R&I Days)
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CORDIScovery Vodcast- Episode #3 - Taking smarter decisions for our oceans and soil
This is an AI transcription.
00:00:16:13 - 00:00:44:15
Anthony Lockett
Hello and welcome to this special episode of the CORDIScovery Podcast. In today's episode, we are going to be looking at two key challenges that are deeply connected to our future: food security and the health of our oceans. Researchers are developing innovative solutions to make food systems more resilient and to safeguard marine ecosystems. I'm joined by representatives of two projects that have received funding from the EU Horizon Europe program.
00:00:44:17 - 00:01:10:13
Anthony Lockett
And these projects illustrate how science and innovation can help secure a healthier, more sustainable future, both for people and for the planet. So Wibi, Jérôme, hello.
Jérôme Gasperi
Hello. Nice to meet you.
Anthony Lockett
Thanks for joining us. Wibi is a geo visualization data scientist based at the OpenGeoHub Foundation in the Netherlands and is part of the consortium of the AI for Soil Health project.
00:01:10:15 - 00:01:35:04
Maulana Ikram Wibisana
Yeah, exactly.
Anthony Lockett
Jérôme is an Earth Observation expert and the Product Manager of the European Digital Twin of the Ocean, the EDITO project. So Wibi, if I could start with you, your project aims to use artificial intelligence to improve soil health. And you're working, if I understand correctly, on the development of a soil digital twin.
00:01:35:06 - 00:01:58:15
Anthony Lockett
How does that relate to the EU soil mission, which aims to transition to more healthy soils by 2030?
Maulana Ikram Wibisana
That's correct. But when we’re talking about soil digital twins, we try to adopt the concept of digital twins, but right now we're not exactly in the digital twins for now.
Anthony Lockett
You're sort of working towards.
Maulana Ikram Wibisana
Yeah working towards it. I know it's more of like a decision support tools, if I may say so.
00:01:58:17 - 00:02:24:05
Maulana Ikram Wibisana
We created the data sets in the European scale 30m, which then can be used for decision support information for general people, farmers, researchers. Because we created this data, also it’s fully open source. Everybody can access it. Everybody can use it. No licensing, just CC BY 4.0, everybody can use it basically. And
Anthony Lockett
So, the data was already there or?
Maulana Ikram Wibisana
The data is already there.
00:02:24:07 - 00:02:47:02
Maulana Ikram Wibisana
It's already been published. Then as we know, soil is pretty important stuff in this world. So especially for farmers and stuff like that, by them knowing what kind of soil properties they have. Sorry, I should have explained that from the beginning. So, we have several soil properties.
00:02:47:04 - 00:03:11:17
Maulana Ikram Wibisana
The data set is from the Soil Scientist. So, we have a legacy data from all the different countries, and we collected them, we filtered them and we cleaned them. We also measured new samples in a pilot site with the help of the soil missions community and the living lab, basically. And we use those points to create the map with machine learning algorithms, basically.
00:03:11:17 - 00:03:28:19
Maulana Ikram Wibisana
And combine it with satellite data.
Anthony Lockett
So, this kind of like if I understand correctly, a citizen science dimension to your project in that you're getting people to help you collecting the data that you can then feed into?
Maulana Ikram Wibisana
That was the next data variables basically. But right now, it's collected by a proper soil scientist.
00:03:28:19 - 00:03:46:17
Maulana Ikram Wibisana
So, it's like proper data of measurement of soil properties, right? Carbon acidity and stuff like that. And then we map them. We use one method of AI, which is machine learning, to find a pattern. And they will predict the basically like the value around the points. I will get like a map of the whole of Europe.
00:03:46:19 - 00:04:08:11
Anthony Lockett
Right. And what is the data showing you? I mean, how concerned should we be about the states of Europe soils?
Maulana Ikram Wibisana
As for the state of Europe soils, I think it really depends on the places. But in general, we can see a decline if you visit our website, edito.eu, and check one of the soil properties, because our data is from 2000 to 2022,
00:04:08:11 - 00:04:28:15
Maulana Ikram Wibisana
So, it's temporal, everybody can see the changes. And you can notice, like most parts of Europe have a decline in soil properties, which is of course not good. Like decreasing soil carbons, but mostly this decreasing soil carbon happens in the city because of the impervious surface around the coastal area and stuff like that.
00:04:28:20 - 00:04:49:20
Maulana Ikram Wibisana
But of course, the fields are also affected because of the way farmers hand their fields with the chemical they use, and everything. This also affects the carbon content, for example, in their area. So, this is also useful for them to check later temporal factors like we can get like a statistic basically like a line of years.
00:04:49:22 - 00:05:09:01
Maulana Ikram Wibisana
And they can check “oh I have like a pretty good peak increase in certain years. What did I do at the time? Why do I have like a big decline in the next year?” So that's why I say that more like this is a support tool, because they can check by themselves and they can see and, go back to, how they handle their farms.
00:05:09:01 - 00:05:32:00
Maulana Ikram Wibisana
And they can review their methods further.
Anthony Lockett
Yeah. Yeah. And what actual practical tools are you putting in the hands of farmers? I believe you're working on a smartphone app. Is that already available?
Maulana Ikram Wibisana
The smartphone will be available next month, the first version. So the way we use AI is two things. One is machine learning that I just told you about, and the other one is large language models.
00:05:32:02 - 00:05:49:19
Maulana Ikram Wibisana
This one is pretty common, like chat bots and stuff like that. We make a prompt and ask, and they give the answers back. Basically, if we are not a real soil scientists, we wouldn't be able to tell what does the soil properties mean if it's certain levels, what does it mean? We want to know basically.
00:05:49:21 - 00:06:13:01
Maulana Ikram Wibisana
So, because of that we created the mobile phone app too. So, this will include the chat bot. This chat bot will try to interpret the soil properties and tell the user what does it mean for them. For example, the farmers can tell “Can you see my location right now through the geo location? Tell me what is the pH of my farm, and what does it mean for me.”
00:06:13:03 - 00:06:36:00
Maulana Ikram Wibisana
Stuff like that. So we tune it, or we train it in a simple terms, to make a simple lines or simple sentences that is easily understood by the general public because we already have, like several other platforms, that of course, the researcher can use easily and it's more complex, but that's not ideal for the other users.
00:06:36:02 - 00:06:55:19
Maulana Ikram Wibisana
Yeah, basically that.
Anthony Lockett
Thanks very much. And I think we can come back to some of those issues, perhaps, in a more general discussion in just a moment. But first of all, let me turn to Jérôme and bring you in. So, one of the main outputs of your project or what you're working on is the digital twin of the ocean.
00:06:55:24 - 00:07:23:00
Anthony Lockett
Can you just explain to us briefly what exactly that is?
Jérôme Gasperi
Yeah. Okay. For us, perhaps, a digital twin of the ocean is like a replica of the ocean, a digital replica of the ocean. So, you can see it like a globe where you can see all the physics of the ocean, which means, for instance, the ocean, it's like, you see the water temperature, the salinity and the winds occurrence, everything related to ocean.
00:07:23:02 - 00:07:42:20
Jérôme Gasperi
And it's interactive. You can play with this globe. So, it's like Google Earth, you see, you know, you see the Earth, and you can play with it. And here we are talking about science and the ocean. It's really about replicating the physics of the ocean, the physics and the biology of the ocean.
00:07:42:22 - 00:08:11:19
Jérôme Gasperi
Meaning, for instance, that you can see past conditions of the ocean, but also, future conditions and forecasts. And, here at Mercato, I work at Mercator Ocean International, we are in charge of the Copernicus Marine Service. And basically, Copernicus Marine Service is about to giving forecast about the condition of the ocean. So, on a daily basis, you have a ten days forecast of all of the ocean, will change the temperature, the salinity, the current, and so on.
00:08:11:21 - 00:08:41:11
Jérôme Gasperi
And the digital twin of the ocean is about taking this data and also other data, from in-situ data, satellite data and so on, and to put in place computing resources on top of that, AI, so we can give and develop applications what we call, what if application, that allows decision maker from the public to ask questions about what if I'm doing this, what is the reason?
00:08:41:11 - 00:09:09:24
Anthony Lockett
What difference does it make, figure out variables and different outcomes. And I think, I mean, Wibi you mentioned that you have data covering a 2- or 3-year period, 2020 to 2022. I think you have much longer time series data for the digital twin of the ocean, right Jérôme?
Jérôme Gasperi
Yeah, exactly. For the physics of the ocean, we have models from the 90s, so 30 years of forecast.
00:09:10:01 - 00:09:37:03
Jérôme Gasperi
It's the forecast we call that hindcast, so 40 years of reanalysis of these forecasts originally. So basically, we have 40 years off sea temperature, current salinity and so on. So it's very important because this way we can see the trends and we can see change, for instance, due to climate change, through all these parameters. And this is very important in particular for AI, for instance, because you know that AI you need to train it with data.
00:09:37:05 - 00:09:53:09
Jérôme Gasperi
And the more the data you have, the better it is. The older they are, the more the data and the more accurate it is if you want to predict change.
Anthony Lockett
Yeah. Oh, sorry. Wibi, please to come in.
Maulana Ikram Wibisana
Is there... No, I just I'm agreeing with it.
00:09:53:11 - 00:10:10:14
Maulana Ikram Wibisana
The data is very important for, for example, for our case soil properties like various. Right. They have chemicals, they have physical, they have biological attributes as well. For us, the biological itself, we didn't map it because the model is not added up so good because of the lack of data and the noise inside of the data.
00:10:10:16 - 00:10:27:09
Maulana Ikram Wibisana
So, in this kind of process where you use AI and the type of machine learning it is very important to have good data. So clean data and enough data for like analysis especially for him, it’s like a whole globe. Yeah, that is like proper data with distribution as well. It’s a good point.
00:10:27:10 - 00:10:49:14
Anthony Lockett
Yeah. And can I ask Jérôme, I mean what is different between this digital twin approach and you know, computer modeling which has been around for quite some time, is it the AI dimension or what? What's new?
Jérôme Gasperi
Yeah. In fact, of course, Digital Twin is using a computing model of course. But you can see it as the computing models are a bit static.
00:10:49:14 - 00:11:22:03
Jérôme Gasperi
You have just forecast data that is a bit static. It's very science related. And the digital twin is about dynamic stuff. It's interactive. You use this model, but you can play with it. And it's easier for non-expert people to understand them. So, it's more so in the ways, there's a dynamic of the stuff. And AI and of course, on top of it, can bring some new stuff about this new way to extract information and to ease the understanding of the ocean.
00:11:22:08 - 00:11:40:06
Jérôme Gasperi
But really, it's really about modeling, of course, because we can use a lot of modeling, AI is training with modeling and so on. So, it's not at all the end of old school modeling, I would say not at all. But it's like taking a snapshot with some modeling versus having an interactive way to play with it.
00:11:40:08 - 00:12:06:04
Jérôme Gasperi
So that will be the main difference with a digital twin of the ocean.
Anthony Lockett
And how can you be sure or check that, I mean the ocean environment is a very complex and multi dimensional ecosystem. How can you be sure that the model correctly reflects the interplay between the different factors?
Jérôme Gasperi
Yeah. As you say, the ocean is an incredibly complex medium.
00:12:06:04 - 00:12:38:23
Jérôme Gasperi
I mean, it interacts with everything and of course there is no one model, a fantastic model that will magically explain everything. So, we have basically a particular model for each of the parameters. And you can see the digital thing of the ocean as multiple layers, like for instance, you have the layers where you have the ship routing, you have the physical information, you have fishing efforts, you have pollution, any kind of information.
00:12:39:04 - 00:13:10:22
Jérôme Gasperi
You can mix everything. And we have then you have a model to track every of these aspects. And with the digital twin, you are able to mix everything to try to extract information from them to do this. So of course, in this area, AI is very useful because it helps you to manage all this data because on the human level it's very difficult to manage when you have a lot of databases and AI can help you.
00:13:10:24 - 00:13:35:14
Jérôme Gasperi
AI can help you find trends, for instance. Subtle changes are extracted from this from this tool.
Anthony Lockett
It sounds like for the digital twin of the ocean, you have more years of data that you can, you can use perhaps more diverse data. In your journey towards a digital twin of the soil
00:13:35:20 - 00:13:58:20
Anthony Lockett
Do you think you need more data?
Maulana Ikram Wibisana
You mean for the other years ?
Anthony Lockett
Other years, I don't know, perhaps other factors.
Maulana Ikram Wibisana
Yeah. We do need more data for that. So, for the current soil properties, the layers, the map that is out, it’s the one with actual clean and enough data for us to model and map it.
00:13:58:22 - 00:14:19:23
Maulana Ikram Wibisana
Of course, from the legacy point itself, the data that's collected by soil scientists in Europe; it also contains data sets starting from 1916, 1970s. But okay, the distribution of this data itself is not comparable to the one in 2000 to 2022. So, if we can model it, we can map it, but the quality will not be the same basically.
00:14:20:00 - 00:14:40:12
Maulana Ikram Wibisana
So that's why we didn't do that. It's the same reason for the biological properties of the soil. And I think, for the digital twin, do you have a what-if scenario, right? This way I'm saying that we are more like a decision support tool, because I know there's a clear line between digital shadow and digital twins.
00:14:40:17 - 00:15:02:02
Maulana Ikram Wibisana
And what part of it is the scenario-based situations, which is the what-if that they had, for us we are not there yet. So, this is very interesting. I love what-if scenarios. I really love your setup too.
Jérôme Gasperi
Thank you.
Anthony Lockett
Can I ask you, I mean, would these projects be working amongst others with farmers to provide them with tools they can use?
00:15:02:08 - 00:15:30:20
Anthony Lockett
I mean, you potentially have a lot of uses, for the digital twin of the ocean, you know, everything from politicians and policymakers to the shipping industry, fishermen, the fishing industry. You know, what kind of use are they making of the tool? Do you do anything, in terms of outreach to those different communities to explain to them what's available and encourage them to use it?
00:15:30:22 - 00:15:48:10
Jérôme Gasperi
Yeah. And, so the digital twin, the European Digital Twin of the Ocean, the EDITO project, as we call it, is pretty new. It starts three years ago, but it's based on, as I explained, it's based on data that has been in for a long time. And in particular, it's based on the Copernicus Marine Service.
00:15:48:10 - 00:16:11:18
Jérôme Gasperi
So, the Copernicus Marine Service, that's a European service. So, perhaps I can introduce the Copernicus project. It's a flagship project from the European Commission. So basically, the Copernicus project is a monitoring project on the Earth, and, so it means that we have satellites,
00:16:11:18 - 00:16:29:23
Jérôme Gasperi
we have in-situ data and satellite data to monitor all aspects of the Earth. And there are six services. So, marine service, land service, emergency service, weather service, climate change service and so on. And so, the marine service is about giving forecast data on the ocean.
00:16:29:23 - 00:17:13:15
Jérôme Gasperi
And this is, of course, used by a lot of people from scientists to industry and to private companies that make business of it. There is something like more than 100,000 registered users, million of downloads per month. And so, this is a basic data that is used. So the European Digital Twin of the Ocean is built on top of that and built on top of also another network of which is called EMODnet, which is about in-situ data, is here to fills in gap between just giving the data and help people to make decision and to have information extraction of data.
00:17:13:20 - 00:17:36:23
Jérôme Gasperi
So, we put in place the infrastructure and the tools, so computing resources and tools to do that. And so to, to more directly answer your question, for the moment, for the first three years of the project, it was basically more on the research project, meaning that we are also discussing a lot with a researcher and with scientists that develop models on top of it.
00:17:37:00 - 00:18:01:06
Jérôme Gasperi
And we developed some what-if scenarios just to showcase how it can be used not only for scientists, but also for decision makers and the general public. So for instance, for fishery, we develop scenarios on Sargassum algae. Sargassum is an algae that goes to the Caribbean,
00:18:01:06 - 00:18:20:19
Jérôme Gasperi
And when it goes to the beach, it's a bit of a problem because there are cases that are linked to this sargassum and it's bad for tourism. And we have a model that detects sargassum from satellites and then a modeling of the drifting of the sargassum.
00:18:20:19 - 00:18:39:23
Jérôme Gasperi
And then you know when it will arrive in your economic zone. And then you can go with your boat to fish for it. To fish for it, because it can be also a resource, because if you take it, you can use it to make bricks, for instance, to build houses. So, this is an example of something that is a problem.
00:18:39:23 - 00:19:10:18
Jérôme Gasperi
Sargassum on the beach can be also an opportunity and resource. But to know that, you need to know as a fisherman when the sargassum will come, is there a sufficient sargassum so it's interesting economically to take the boats, and so on. So, this is the kind of example we have. And the idea now with the next phase of the EDITO project is to onboard this kind of, I would say public or private, user to use the platform and to make the best of it.
00:19:10:20 - 00:19:32:11
Anthony Lockett
Okay. And Wibi, is there an equivalent example that you could mention either that has happened or that you could see happening, based on the data you're making available on soil health? So, is there a kind of use case or scenario that you could mention?
00:19:32:17 - 00:19:53:06
Maulana Ikram Wibisana
Not a scenario-based situation. But if you want me to give an example of the usage of this data. I can tell you that because we are also like, within the soil missions. So, we also have collaboration with the Living Lab and stuff like that. In fact, yesterday we have like, course in Spain at the Living Lab as well.
00:19:53:08 - 00:20:18:23
Maulana Ikram Wibisana
So, in this course we also use this platform that we currently have published,the Eco Data Cube, and we educate farmers basically on how can they use this. And I think yesterday they were testing it on the scale of their farm, for example, one person's farm and they checked in the temporal factors by the statistics by time, from 2000 to 2022.
00:20:19:00 - 00:20:35:06
Maulana Ikram Wibisana
And they do exactly what I told you the first time, like oh, there is like a step increase and step decline in these certain years. And they go back to their farming methods and they check what kind of chemicals they use, what kind of stuff they used to take care of their farms.
00:20:35:08 - 00:20:58:20
Maulana Ikram Wibisana
And this actually makes sense and correlated a lot with the data sets that we have, the layers that we have, and they can actually tell which part of their farming methods that caused this. And then they can plan for the future, what kind of method they should have done for certain kind of plants, for example. Because our data is also based on depth as well.
00:20:58:22 - 00:21:18:14
Maulana Ikram Wibisana
So, depending on what type of plant they were planting, they need to see which depth does it correlate to. So of course, each step has different pattern.
Anthony Lockett
I see. Perhaps, just a last question for both of you. I mean things are obviously evolving very quickly with artificial intelligence and so on.
00:21:18:14 - 00:21:36:24
Anthony Lockett
If you could look into your crystal balls, into the future, perhaps five years ahead, where would you see, ideally, your project being? What further breakthroughs do you think you may be able to make? And what difference could it bring? Perhaps Jérôme, you would like to start off?
00:21:37:01 - 00:21:59:10
Jérôme Gasperi
Yeah. I think, ideally, it would be that EDITO is the reference for oceanography. Like, always I take this similarity, it's not a good one, but if you think of video, you think YouTube, if you think picture, you think Instagram. Let’s say you think I need oceanography information and data; you go to the EDITO platform.
00:21:59:12 - 00:22:24:08
Jérôme Gasperi
And, with the artificial intelligence coming in. Ideally, what would happen I think, is that now we start with chatbots that when you ask a question, you say “I would like to see the temperature in the Gulf of Naples” for instance, “next week”, the chatbot will tell you how to get the results.
00:22:24:10 - 00:22:52:24
Jérôme Gasperi
So, the next phase will be the assistant, the AI will give you the result, meaning it is able to launch processes, right? To use all this data, all these models, to combine them, to give you the result, and to really give you information. And this could be, I think, a game changer to move this amount of data, which is enormous, that is really embedded by a kind of ocean assistant, an ocean intelligence.
00:22:52:24 - 00:23:20:21
Jérôme Gasperi
That gives you an answer directly. Because a very important thing is, I think, if there is only one thing to remember from this is that the healthier the oceans are, the healthier our future in fact. And it's important that we make better choices. And the digital twin is not just a scientific tool, but it's like a window to open to people to see the ocean, to understand it.
00:23:20:21 - 00:23:45:07
Jérôme Gasperi
Yeah. And better knowledge means better choices and better choices, it means a better planet.
Anthony Lockett
So better knowledge means better choices. Do you share a similar vision for your project Wibi?
Maulana Ikram Wibisana
Absolutely. We're also very excited with the mobile app including the chatbot, because when we did the first feedback round with the farmers for example, they were clearly divided into two parts.
00:23:45:07 - 00:24:02:23
Maulana Ikram Wibisana
The one that likes visualization and the other one that doesn't really care about it at all. They just want to know what is happening. And chatbot for sure, as he explained, will help a lot because nobody knows what the speech means, nobody knows what carbon content in the soil means. What does it do with my farm? With the chatbot
00:24:03:01 - 00:24:25:24
Maulana Ikram Wibisana
With the chatbot they can simplify the terms, and they can give like a simple explanation of how it works. And maybe they also can suggest what to do based on how we trained large language models, as long as we feed it with a lot of information, that will work. I'm very excited for this one. And we also will have the first launch, next month, actually, in November for the mobile app.
00:24:26:03 - 00:24:46:05
Anthony Lockett
November 2025.
Maulana Ikram Wibisana
I can already see that the chatbot is working quite well in the backend. So, I'm very excited for this one too. And for another point, because our company is like a nonprofit organization, we try to give open-source data, especially for researchers, and that we already kind of achieve.
00:24:46:05 - 00:25:05:20
Maulana Ikram Wibisana
So, we have already published European data freely. Everybody can access, everybody can take a look at it. Especially the researchers, they can use it. Aside from the European scale, we also created the global scale so we have another platform for the global soil data. That is also already published; the paper is out, the data is out. Everybody can use it.
00:25:05:22 - 00:25:24:19
Maulana Ikram Wibisana
So, we also achieved some things out of this project specifically. Shout out to my boss, that's their ideology which is very nice. They are researchers. They know how hard researchers find the data sets, and they create that new data set that is completely open. You don't have to pay. It’s a very nice mission, very vision for them.
00:25:24:19 - 00:25:47:12
Anthony Lockett
Before I do a short closing, is there anything that you wanted to mention that you haven't had a chance to?
Maulana Ikram Wibisana
No, I agree; I learned a lot about your own projects as well. It's very interesting for me personally. It's very nice talk.
Jérôme Gasperi
Do you have an Apple application or it will be on Android?
00:25:47:14 - 00:26:07:18
Maulana Ikram Wibisana
Oh, it will not be an Android. It will be on the web, basically a website. The way we created the architecture will mimic the device amazingly. We tried, we were thinking of publishing it on the App Store, but I don't think it's an option for now. Of course, because of registration, authentication, and stuff like that.
00:26:07:18 - 00:26:28:21
Maulana Ikram Wibisana
It's easier on the website, but when you use it on the phone, it will look like a mobile app for you.
Anthony Lockett
So, thanks very much Wibi and Jérôme for joining us. Thanks also to our audience for tuning in to this episode. You can follow us on Spotify and Apple Podcasts and do check out the podcast homepage on the Cordis website.
00:26:28:23 - 00:26:45:02
Anthony Lockett
Subscribe to make sure that the hottest research in EU funded science isn't passing you by. You can also find more information and project examples on the Cordis website. On the European Commission's Research and Innovation website and in our online magazine, Horizon.
This is an AI transcription.
00:00:16:13 - 00:00:44:15
Anthony Lockett
Hello and welcome to this special episode of the CORDIScovery Podcast. In today's episode, we are going to be looking at two key challenges that are deeply connected to our future: food security and the health of our oceans. Researchers are developing innovative solutions to make food systems more resilient and to safeguard marine ecosystems. I'm joined by representatives of two projects that have received funding from the EU Horizon Europe program.
00:00:44:17 - 00:01:10:13
Anthony Lockett
And these projects illustrate how science and innovation can help secure a healthier, more sustainable future, both for people and for the planet. So Wibi, Jérôme, hello.
Jérôme Gasperi
Hello. Nice to meet you.
Anthony Lockett
Thanks for joining us. Wibi is a geo visualization data scientist based at the OpenGeoHub Foundation in the Netherlands and is part of the consortium of the AI for Soil Health project.
00:01:10:15 - 00:01:35:04
Maulana Ikram Wibisana
Yeah, exactly.
Anthony Lockett
Jérôme is an Earth Observation expert and the Product Manager of the European Digital Twin of the Ocean, the EDITO project. So Wibi, if I could start with you, your project aims to use artificial intelligence to improve soil health. And you're working, if I understand correctly, on the development of a soil digital twin.
00:01:35:06 - 00:01:58:15
Anthony Lockett
How does that relate to the EU soil mission, which aims to transition to more healthy soils by 2030?
Maulana Ikram Wibisana
That's correct. But when we’re talking about soil digital twins, we try to adopt the concept of digital twins, but right now we're not exactly in the digital twins for now.
Anthony Lockett
You're sort of working towards.
Maulana Ikram Wibisana
Yeah working towards it. I know it's more of like a decision support tools, if I may say so.
00:01:58:17 - 00:02:24:05
Maulana Ikram Wibisana
We created the data sets in the European scale 30m, which then can be used for decision support information for general people, farmers, researchers. Because we created this data, also it’s fully open source. Everybody can access it. Everybody can use it. No licensing, just CC BY 4.0, everybody can use it basically. And
Anthony Lockett
So, the data was already there or?
Maulana Ikram Wibisana
The data is already there.
00:02:24:07 - 00:02:47:02
Maulana Ikram Wibisana
It's already been published. Then as we know, soil is pretty important stuff in this world. So especially for farmers and stuff like that, by them knowing what kind of soil properties they have. Sorry, I should have explained that from the beginning. So, we have several soil properties.
00:02:47:04 - 00:03:11:17
Maulana Ikram Wibisana
The data set is from the Soil Scientist. So, we have a legacy data from all the different countries, and we collected them, we filtered them and we cleaned them. We also measured new samples in a pilot site with the help of the soil missions community and the living lab, basically. And we use those points to create the map with machine learning algorithms, basically.
00:03:11:17 - 00:03:28:19
Maulana Ikram Wibisana
And combine it with satellite data.
Anthony Lockett
So, this kind of like if I understand correctly, a citizen science dimension to your project in that you're getting people to help you collecting the data that you can then feed into?
Maulana Ikram Wibisana
That was the next data variables basically. But right now, it's collected by a proper soil scientist.
00:03:28:19 - 00:03:46:17
Maulana Ikram Wibisana
So, it's like proper data of measurement of soil properties, right? Carbon acidity and stuff like that. And then we map them. We use one method of AI, which is machine learning, to find a pattern. And they will predict the basically like the value around the points. I will get like a map of the whole of Europe.
00:03:46:19 - 00:04:08:11
Anthony Lockett
Right. And what is the data showing you? I mean, how concerned should we be about the states of Europe soils?
Maulana Ikram Wibisana
As for the state of Europe soils, I think it really depends on the places. But in general, we can see a decline if you visit our website, edito.eu, and check one of the soil properties, because our data is from 2000 to 2022,
00:04:08:11 - 00:04:28:15
Maulana Ikram Wibisana
So, it's temporal, everybody can see the changes. And you can notice, like most parts of Europe have a decline in soil properties, which is of course not good. Like decreasing soil carbons, but mostly this decreasing soil carbon happens in the city because of the impervious surface around the coastal area and stuff like that.
00:04:28:20 - 00:04:49:20
Maulana Ikram Wibisana
But of course, the fields are also affected because of the way farmers hand their fields with the chemical they use, and everything. This also affects the carbon content, for example, in their area. So, this is also useful for them to check later temporal factors like we can get like a statistic basically like a line of years.
00:04:49:22 - 00:05:09:01
Maulana Ikram Wibisana
And they can check “oh I have like a pretty good peak increase in certain years. What did I do at the time? Why do I have like a big decline in the next year?” So that's why I say that more like this is a support tool, because they can check by themselves and they can see and, go back to, how they handle their farms.
00:05:09:01 - 00:05:32:00
Maulana Ikram Wibisana
And they can review their methods further.
Anthony Lockett
Yeah. Yeah. And what actual practical tools are you putting in the hands of farmers? I believe you're working on a smartphone app. Is that already available?
Maulana Ikram Wibisana
The smartphone will be available next month, the first version. So the way we use AI is two things. One is machine learning that I just told you about, and the other one is large language models.
00:05:32:02 - 00:05:49:19
Maulana Ikram Wibisana
This one is pretty common, like chat bots and stuff like that. We make a prompt and ask, and they give the answers back. Basically, if we are not a real soil scientists, we wouldn't be able to tell what does the soil properties mean if it's certain levels, what does it mean? We want to know basically.
00:05:49:21 - 00:06:13:01
Maulana Ikram Wibisana
So, because of that we created the mobile phone app too. So, this will include the chat bot. This chat bot will try to interpret the soil properties and tell the user what does it mean for them. For example, the farmers can tell “Can you see my location right now through the geo location? Tell me what is the pH of my farm, and what does it mean for me.”
00:06:13:03 - 00:06:36:00
Maulana Ikram Wibisana
Stuff like that. So we tune it, or we train it in a simple terms, to make a simple lines or simple sentences that is easily understood by the general public because we already have, like several other platforms, that of course, the researcher can use easily and it's more complex, but that's not ideal for the other users.
00:06:36:02 - 00:06:55:19
Maulana Ikram Wibisana
Yeah, basically that.
Anthony Lockett
Thanks very much. And I think we can come back to some of those issues, perhaps, in a more general discussion in just a moment. But first of all, let me turn to Jérôme and bring you in. So, one of the main outputs of your project or what you're working on is the digital twin of the ocean.
00:06:55:24 - 00:07:23:00
Anthony Lockett
Can you just explain to us briefly what exactly that is?
Jérôme Gasperi
Yeah. Okay. For us, perhaps, a digital twin of the ocean is like a replica of the ocean, a digital replica of the ocean. So, you can see it like a globe where you can see all the physics of the ocean, which means, for instance, the ocean, it's like, you see the water temperature, the salinity and the winds occurrence, everything related to ocean.
00:07:23:02 - 00:07:42:20
Jérôme Gasperi
And it's interactive. You can play with this globe. So, it's like Google Earth, you see, you know, you see the Earth, and you can play with it. And here we are talking about science and the ocean. It's really about replicating the physics of the ocean, the physics and the biology of the ocean.
00:07:42:22 - 00:08:11:19
Jérôme Gasperi
Meaning, for instance, that you can see past conditions of the ocean, but also, future conditions and forecasts. And, here at Mercato, I work at Mercator Ocean International, we are in charge of the Copernicus Marine Service. And basically, Copernicus Marine Service is about to giving forecast about the condition of the ocean. So, on a daily basis, you have a ten days forecast of all of the ocean, will change the temperature, the salinity, the current, and so on.
00:08:11:21 - 00:08:41:11
Jérôme Gasperi
And the digital twin of the ocean is about taking this data and also other data, from in-situ data, satellite data and so on, and to put in place computing resources on top of that, AI, so we can give and develop applications what we call, what if application, that allows decision maker from the public to ask questions about what if I'm doing this, what is the reason?
00:08:41:11 - 00:09:09:24
Anthony Lockett
What difference does it make, figure out variables and different outcomes. And I think, I mean, Wibi you mentioned that you have data covering a 2- or 3-year period, 2020 to 2022. I think you have much longer time series data for the digital twin of the ocean, right Jérôme?
Jérôme Gasperi
Yeah, exactly. For the physics of the ocean, we have models from the 90s, so 30 years of forecast.
00:09:10:01 - 00:09:37:03
Jérôme Gasperi
It's the forecast we call that hindcast, so 40 years of reanalysis of these forecasts originally. So basically, we have 40 years off sea temperature, current salinity and so on. So it's very important because this way we can see the trends and we can see change, for instance, due to climate change, through all these parameters. And this is very important in particular for AI, for instance, because you know that AI you need to train it with data.
00:09:37:05 - 00:09:53:09
Jérôme Gasperi
And the more the data you have, the better it is. The older they are, the more the data and the more accurate it is if you want to predict change.
Anthony Lockett
Yeah. Oh, sorry. Wibi, please to come in.
Maulana Ikram Wibisana
Is there... No, I just I'm agreeing with it.
00:09:53:11 - 00:10:10:14
Maulana Ikram Wibisana
The data is very important for, for example, for our case soil properties like various. Right. They have chemicals, they have physical, they have biological attributes as well. For us, the biological itself, we didn't map it because the model is not added up so good because of the lack of data and the noise inside of the data.
00:10:10:16 - 00:10:27:09
Maulana Ikram Wibisana
So, in this kind of process where you use AI and the type of machine learning it is very important to have good data. So clean data and enough data for like analysis especially for him, it’s like a whole globe. Yeah, that is like proper data with distribution as well. It’s a good point.
00:10:27:10 - 00:10:49:14
Anthony Lockett
Yeah. And can I ask Jérôme, I mean what is different between this digital twin approach and you know, computer modeling which has been around for quite some time, is it the AI dimension or what? What's new?
Jérôme Gasperi
Yeah. In fact, of course, Digital Twin is using a computing model of course. But you can see it as the computing models are a bit static.
00:10:49:14 - 00:11:22:03
Jérôme Gasperi
You have just forecast data that is a bit static. It's very science related. And the digital twin is about dynamic stuff. It's interactive. You use this model, but you can play with it. And it's easier for non-expert people to understand them. So, it's more so in the ways, there's a dynamic of the stuff. And AI and of course, on top of it, can bring some new stuff about this new way to extract information and to ease the understanding of the ocean.
00:11:22:08 - 00:11:40:06
Jérôme Gasperi
But really, it's really about modeling, of course, because we can use a lot of modeling, AI is training with modeling and so on. So, it's not at all the end of old school modeling, I would say not at all. But it's like taking a snapshot with some modeling versus having an interactive way to play with it.
00:11:40:08 - 00:12:06:04
Jérôme Gasperi
So that will be the main difference with a digital twin of the ocean.
Anthony Lockett
And how can you be sure or check that, I mean the ocean environment is a very complex and multi dimensional ecosystem. How can you be sure that the model correctly reflects the interplay between the different factors?
Jérôme Gasperi
Yeah. As you say, the ocean is an incredibly complex medium.
00:12:06:04 - 00:12:38:23
Jérôme Gasperi
I mean, it interacts with everything and of course there is no one model, a fantastic model that will magically explain everything. So, we have basically a particular model for each of the parameters. And you can see the digital thing of the ocean as multiple layers, like for instance, you have the layers where you have the ship routing, you have the physical information, you have fishing efforts, you have pollution, any kind of information.
00:12:39:04 - 00:13:10:22
Jérôme Gasperi
You can mix everything. And we have then you have a model to track every of these aspects. And with the digital twin, you are able to mix everything to try to extract information from them to do this. So of course, in this area, AI is very useful because it helps you to manage all this data because on the human level it's very difficult to manage when you have a lot of databases and AI can help you.
00:13:10:24 - 00:13:35:14
Jérôme Gasperi
AI can help you find trends, for instance. Subtle changes are extracted from this from this tool.
Anthony Lockett
It sounds like for the digital twin of the ocean, you have more years of data that you can, you can use perhaps more diverse data. In your journey towards a digital twin of the soil
00:13:35:20 - 00:13:58:20
Anthony Lockett
Do you think you need more data?
Maulana Ikram Wibisana
You mean for the other years ?
Anthony Lockett
Other years, I don't know, perhaps other factors.
Maulana Ikram Wibisana
Yeah. We do need more data for that. So, for the current soil properties, the layers, the map that is out, it’s the one with actual clean and enough data for us to model and map it.
00:13:58:22 - 00:14:19:23
Maulana Ikram Wibisana
Of course, from the legacy point itself, the data that's collected by soil scientists in Europe; it also contains data sets starting from 1916, 1970s. But okay, the distribution of this data itself is not comparable to the one in 2000 to 2022. So, if we can model it, we can map it, but the quality will not be the same basically.
00:14:20:00 - 00:14:40:12
Maulana Ikram Wibisana
So that's why we didn't do that. It's the same reason for the biological properties of the soil. And I think, for the digital twin, do you have a what-if scenario, right? This way I'm saying that we are more like a decision support tool, because I know there's a clear line between digital shadow and digital twins.
00:14:40:17 - 00:15:02:02
Maulana Ikram Wibisana
And what part of it is the scenario-based situations, which is the what-if that they had, for us we are not there yet. So, this is very interesting. I love what-if scenarios. I really love your setup too.
Jérôme Gasperi
Thank you.
Anthony Lockett
Can I ask you, I mean, would these projects be working amongst others with farmers to provide them with tools they can use?
00:15:02:08 - 00:15:30:20
Anthony Lockett
I mean, you potentially have a lot of uses, for the digital twin of the ocean, you know, everything from politicians and policymakers to the shipping industry, fishermen, the fishing industry. You know, what kind of use are they making of the tool? Do you do anything, in terms of outreach to those different communities to explain to them what's available and encourage them to use it?
00:15:30:22 - 00:15:48:10
Jérôme Gasperi
Yeah. And, so the digital twin, the European Digital Twin of the Ocean, the EDITO project, as we call it, is pretty new. It starts three years ago, but it's based on, as I explained, it's based on data that has been in for a long time. And in particular, it's based on the Copernicus Marine Service.
00:15:48:10 - 00:16:11:18
Jérôme Gasperi
So, the Copernicus Marine Service, that's a European service. So, perhaps I can introduce the Copernicus project. It's a flagship project from the European Commission. So basically, the Copernicus project is a monitoring project on the Earth, and, so it means that we have satellites,
00:16:11:18 - 00:16:29:23
Jérôme Gasperi
we have in-situ data and satellite data to monitor all aspects of the Earth. And there are six services. So, marine service, land service, emergency service, weather service, climate change service and so on. And so, the marine service is about giving forecast data on the ocean.
00:16:29:23 - 00:17:13:15
Jérôme Gasperi
And this is, of course, used by a lot of people from scientists to industry and to private companies that make business of it. There is something like more than 100,000 registered users, million of downloads per month. And so, this is a basic data that is used. So the European Digital Twin of the Ocean is built on top of that and built on top of also another network of which is called EMODnet, which is about in-situ data, is here to fills in gap between just giving the data and help people to make decision and to have information extraction of data.
00:17:13:20 - 00:17:36:23
Jérôme Gasperi
So, we put in place the infrastructure and the tools, so computing resources and tools to do that. And so to, to more directly answer your question, for the moment, for the first three years of the project, it was basically more on the research project, meaning that we are also discussing a lot with a researcher and with scientists that develop models on top of it.
00:17:37:00 - 00:18:01:06
Jérôme Gasperi
And we developed some what-if scenarios just to showcase how it can be used not only for scientists, but also for decision makers and the general public. So for instance, for fishery, we develop scenarios on Sargassum algae. Sargassum is an algae that goes to the Caribbean,
00:18:01:06 - 00:18:20:19
Jérôme Gasperi
And when it goes to the beach, it's a bit of a problem because there are cases that are linked to this sargassum and it's bad for tourism. And we have a model that detects sargassum from satellites and then a modeling of the drifting of the sargassum.
00:18:20:19 - 00:18:39:23
Jérôme Gasperi
And then you know when it will arrive in your economic zone. And then you can go with your boat to fish for it. To fish for it, because it can be also a resource, because if you take it, you can use it to make bricks, for instance, to build houses. So, this is an example of something that is a problem.
00:18:39:23 - 00:19:10:18
Jérôme Gasperi
Sargassum on the beach can be also an opportunity and resource. But to know that, you need to know as a fisherman when the sargassum will come, is there a sufficient sargassum so it's interesting economically to take the boats, and so on. So, this is the kind of example we have. And the idea now with the next phase of the EDITO project is to onboard this kind of, I would say public or private, user to use the platform and to make the best of it.
00:19:10:20 - 00:19:32:11
Anthony Lockett
Okay. And Wibi, is there an equivalent example that you could mention either that has happened or that you could see happening, based on the data you're making available on soil health? So, is there a kind of use case or scenario that you could mention?
00:19:32:17 - 00:19:53:06
Maulana Ikram Wibisana
Not a scenario-based situation. But if you want me to give an example of the usage of this data. I can tell you that because we are also like, within the soil missions. So, we also have collaboration with the Living Lab and stuff like that. In fact, yesterday we have like, course in Spain at the Living Lab as well.
00:19:53:08 - 00:20:18:23
Maulana Ikram Wibisana
So, in this course we also use this platform that we currently have published,the Eco Data Cube, and we educate farmers basically on how can they use this. And I think yesterday they were testing it on the scale of their farm, for example, one person's farm and they checked in the temporal factors by the statistics by time, from 2000 to 2022.
00:20:19:00 - 00:20:35:06
Maulana Ikram Wibisana
And they do exactly what I told you the first time, like oh, there is like a step increase and step decline in these certain years. And they go back to their farming methods and they check what kind of chemicals they use, what kind of stuff they used to take care of their farms.
00:20:35:08 - 00:20:58:20
Maulana Ikram Wibisana
And this actually makes sense and correlated a lot with the data sets that we have, the layers that we have, and they can actually tell which part of their farming methods that caused this. And then they can plan for the future, what kind of method they should have done for certain kind of plants, for example. Because our data is also based on depth as well.
00:20:58:22 - 00:21:18:14
Maulana Ikram Wibisana
So, depending on what type of plant they were planting, they need to see which depth does it correlate to. So of course, each step has different pattern.
Anthony Lockett
I see. Perhaps, just a last question for both of you. I mean things are obviously evolving very quickly with artificial intelligence and so on.
00:21:18:14 - 00:21:36:24
Anthony Lockett
If you could look into your crystal balls, into the future, perhaps five years ahead, where would you see, ideally, your project being? What further breakthroughs do you think you may be able to make? And what difference could it bring? Perhaps Jérôme, you would like to start off?
00:21:37:01 - 00:21:59:10
Jérôme Gasperi
Yeah. I think, ideally, it would be that EDITO is the reference for oceanography. Like, always I take this similarity, it's not a good one, but if you think of video, you think YouTube, if you think picture, you think Instagram. Let’s say you think I need oceanography information and data; you go to the EDITO platform.
00:21:59:12 - 00:22:24:08
Jérôme Gasperi
And, with the artificial intelligence coming in. Ideally, what would happen I think, is that now we start with chatbots that when you ask a question, you say “I would like to see the temperature in the Gulf of Naples” for instance, “next week”, the chatbot will tell you how to get the results.
00:22:24:10 - 00:22:52:24
Jérôme Gasperi
So, the next phase will be the assistant, the AI will give you the result, meaning it is able to launch processes, right? To use all this data, all these models, to combine them, to give you the result, and to really give you information. And this could be, I think, a game changer to move this amount of data, which is enormous, that is really embedded by a kind of ocean assistant, an ocean intelligence.
00:22:52:24 - 00:23:20:21
Jérôme Gasperi
That gives you an answer directly. Because a very important thing is, I think, if there is only one thing to remember from this is that the healthier the oceans are, the healthier our future in fact. And it's important that we make better choices. And the digital twin is not just a scientific tool, but it's like a window to open to people to see the ocean, to understand it.
00:23:20:21 - 00:23:45:07
Jérôme Gasperi
Yeah. And better knowledge means better choices and better choices, it means a better planet.
Anthony Lockett
So better knowledge means better choices. Do you share a similar vision for your project Wibi?
Maulana Ikram Wibisana
Absolutely. We're also very excited with the mobile app including the chatbot, because when we did the first feedback round with the farmers for example, they were clearly divided into two parts.
00:23:45:07 - 00:24:02:23
Maulana Ikram Wibisana
The one that likes visualization and the other one that doesn't really care about it at all. They just want to know what is happening. And chatbot for sure, as he explained, will help a lot because nobody knows what the speech means, nobody knows what carbon content in the soil means. What does it do with my farm? With the chatbot
00:24:03:01 - 00:24:25:24
Maulana Ikram Wibisana
With the chatbot they can simplify the terms, and they can give like a simple explanation of how it works. And maybe they also can suggest what to do based on how we trained large language models, as long as we feed it with a lot of information, that will work. I'm very excited for this one. And we also will have the first launch, next month, actually, in November for the mobile app.
00:24:26:03 - 00:24:46:05
Anthony Lockett
November 2025.
Maulana Ikram Wibisana
I can already see that the chatbot is working quite well in the backend. So, I'm very excited for this one too. And for another point, because our company is like a nonprofit organization, we try to give open-source data, especially for researchers, and that we already kind of achieve.
00:24:46:05 - 00:25:05:20
Maulana Ikram Wibisana
So, we have already published European data freely. Everybody can access, everybody can take a look at it. Especially the researchers, they can use it. Aside from the European scale, we also created the global scale so we have another platform for the global soil data. That is also already published; the paper is out, the data is out. Everybody can use it.
00:25:05:22 - 00:25:24:19
Maulana Ikram Wibisana
So, we also achieved some things out of this project specifically. Shout out to my boss, that's their ideology which is very nice. They are researchers. They know how hard researchers find the data sets, and they create that new data set that is completely open. You don't have to pay. It’s a very nice mission, very vision for them.
00:25:24:19 - 00:25:47:12
Anthony Lockett
Before I do a short closing, is there anything that you wanted to mention that you haven't had a chance to?
Maulana Ikram Wibisana
No, I agree; I learned a lot about your own projects as well. It's very interesting for me personally. It's very nice talk.
Jérôme Gasperi
Do you have an Apple application or it will be on Android?
00:25:47:14 - 00:26:07:18
Maulana Ikram Wibisana
Oh, it will not be an Android. It will be on the web, basically a website. The way we created the architecture will mimic the device amazingly. We tried, we were thinking of publishing it on the App Store, but I don't think it's an option for now. Of course, because of registration, authentication, and stuff like that.
00:26:07:18 - 00:26:28:21
Maulana Ikram Wibisana
It's easier on the website, but when you use it on the phone, it will look like a mobile app for you.
Anthony Lockett
So, thanks very much Wibi and Jérôme for joining us. Thanks also to our audience for tuning in to this episode. You can follow us on Spotify and Apple Podcasts and do check out the podcast homepage on the Cordis website.
00:26:28:23 - 00:26:45:02
Anthony Lockett
Subscribe to make sure that the hottest research in EU funded science isn't passing you by. You can also find more information and project examples on the Cordis website. On the European Commission's Research and Innovation website and in our online magazine, Horizon.
The seas and our soil – improving their conditions
This special episode of CORDIScovery comes to you from the EU’s Research and Innovation Days(opens in new window). This episode looks at two key challenges that are deeply connected to our future – food security and the health of our oceans. Researchers, all of whom benefited from research and innovation funding from the EU, are developing innovative solutions to make food systems more resilient and to safeguard marine ecosystems. What is a soil digital twin, and how can that help determine the health of our soils? And can a maritime digital twin help protect our oceans? Two cutting-edge projects that have received funding from the EU’s Horizon Europe programme, will explore these ideas. These projects illustrate how science and innovation can help secure a healthier, more sustainable future for people and the planet. Maulana Ikram Wibisana (Wibi), a geovisualisation data scientist, is an employee of the OpenGeoHub(opens in new window) Foundation in the Netherlands, a non-profit foundation that holds the vision for open-source datasets, especially satellite-based data for Earth observation. Wibi works as a geovisualisation data scientist and is part of the AI4SoilHealth project. Jérôme Gasperi is an Earth observation expert and the product manager of the European Digital Twin(opens in new window) of the Ocean (EDITO) at Mercator Ocean International(opens in new window). He helps develop tools and services that bring together satellite data, ocean models and digital technologies to support science, sustainability and society, and was involved in the EDITO 2 project.
Happy to hear from you!
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