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My name is Sami Yacoubi and I am French. I have a degree in Computer Science and Engineering and I've always been fascinated by innovation. I worked for two and a half years at SNCF, the French national railway company, running their startup accelerator and working in their innovation management to solve a wide range of problems, one of which was the vegetation management, in fact I learnt a lot about agriculture there.
While working at the SNCF, I did a Master in Entrepreneurship at HCE Paris. I was very passionate about space and in the summer of 2019, I met Jyotsna at a European Space Agency event. She told me what she had in mind and that's how we decided to work together and launch SpaceSense. Then we were accepted into an incubator at Polytechnique, which is an excellent engineering school in France, and we got some funding to actually start the company.
We thought that geospatial AI could potentially be extremely useful for a lot of people, not just in agriculture but in all industries. However, because of its complexity, only a few companies could afford to actually access and use the data, so with SpaceSense we wanted to create a software that would make this easier. We started developing products for agriculture, which is how we met xFarm Technologies, one of our first customers. We also managed to raise € 1,5 million from some VCs and business angels, which helped us hire the team we have today and develop our products.
Well, let's start with geospatial: this is the science of gathering data out of georeferenced information, that is information that can be located somewhere on a map. So, it means extracting data from satellite images and georeferenced information, and this has been around for a while. The AI part is a bit more recent; before, each image had to be manually monitored by someone who'd try to identify certain elements or run a script on it and so on, but that was very time consuming. With AI, you can do that at scale because it allows you to automate and replicate the observation that you would otherwise have to do manually.
The satellite imagery market for agriculture is quite challenging at the moment: to be really effective, satellite imagery and AI need a lot of data to train the models. That's true for AI in any industry, but it's especially true in agriculture because there's so much variation in things like climate, crops and soils. Now, xFarm Technologies has hundreds of thousands of farmers each entering information into the app, so we have access to an amount of information that almost no other company in the world (or maybe no other company in the world) has. It's a great opportunity and I think we'll be able to build more accurate models than the competition. Obviously, there are a lot of challenges. As I said, there's a lot of variation in terms of climate, soils and crops, so there's not going to be a single solution for every country or region in the world, we need to find a way to easily adapt our solutions to the different contexts where people need them.
That's right, MRV, which stands for Monitoring, Reporting, Verification, is very important. The idea is that satellite imagery can allow stakeholders to monitor what's happening in a particular place and make sure that something that's been declared has actually been done. So MRV is a great tool to open up a lot of environmental practices that weren't possible before, especially for the carbon market. For this technology to really work, there has to be a good way to ensure accountability, to make sure that what is being reported is actually being done. Well, with satellites we can do that verification on a very large scale, like a global scale, at very low cost. And with xFarm Technologies we have so much data that I'm pretty confident that in 3 to 4 years we'll have an excellent MRV system.
By monitoring what is happening in the field at a more granular level, agri-food companies can understand exactly what practices are being used and link this to an estimate of the carbon that is being stored or released into the atmosphere. This gives them a level of control and detail that was previously impossible. We even have satellites that monitor greenhouse gases using atmospheric sensors that detect the presence of certain gases.
The great thing about satellite imagery is that there is data going back at least to the 1990s. That means we have historical information that's unbiased, so we can look at each individual field in the supply chain and see how it is now and how it's been over the last few years, and of course we have some algorithms to process that data. So, for example, we can prove whether certain products come from a field that has been deforested in the last three or five years. It's obviously a very good way for agri-food companies to check their existing supply chain, but it's also a good way for them to check their potential new suppliers and make sure they're up to the standards they're looking for.
I'd have to say the one we started when we started working with xFarm Technologies. It's about providing farmers with reliable crop information so they can know what's going on in their fields, check the health of their crops and make their lives easier. At the moment, I think we process about 50 million hectares a month for about half a million farmers in Europe: when I'm travelling on the train and I see crops around me, it's quite amazing to know that maybe we're making a small contribution to some of those fields.
I hope we'll be the first farm management system, the number one digital tool for agriculture in the world. I really believe that what we are doing is positive, also for the environment, and that it needs to be pushed further. We have the right tools, the right mindset, the right team and I hope that in a few years most farmers in the world will be using the xFarm app to simplify their work and make their lives better.
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