Agriculture is among the sectors most affected by digital transition, given the amount of data it possesses. But for the industry to benefit from its full potential, it must be able to find a sound business model for sharing this data. Anne-Sophie Taillandier, the Director of TeraLab — IMT’s big data and AI platform — outlines the digital challenges facing this sector in five answers.
How important of an issue is data in the agricultural sector?
Anne-Sophie Taillandier: It’s one of the most data-intensive sectors and has been for a long time. This data comes from tools used by farmers, agricultural cooperatives and distribution operations, up to the border with the agrifood industry behind it. Data is therefore found at every step. It’s an extremely competitive industry, so the economic stakes for using data are huge.
How can this great quantity of data in the sector be explained?
AST: Agriculture has used sensors for a long time. The earliest IoT (Internet of Things) systems were dedicated to collecting weather data, and were therefore quickly used in farming to make forecasts. And tractors are state-of-the-art vehicles in terms of intelligence – they were among the earliest autonomous vehicles. Farms also use drones to survey land. And precision agriculture is based on satellite imagery to optimize harvests while using as few resources as possible. On the livestock farming side, infrastructures also have a wealth of data about the quality and health of animals. And all of these examples only have to do with the production portion.
What challenges does agriculture face in relation to data?
AST: The tricky question is determining who has access to which data and in what context. These data sharing issues arise in other sectors too, but there are scientific hurdles that are specific to agriculture. The data is heterogeneous: it comes from satellites, ground-based sensors, information about markets etc. It comes in the form of texts, images and measurements. We must find a way for this data to communicate with each other. And once we’ve done so, we have to make sure that all the stakeholders in the industry can get benefit from it, by accessing a level of data aggregation that does not exceed what the other stakeholders wish to make available.
How can the owners of the data be convinced to share it?
AST: Everyone has to find a business model they find satisfactory. For example, a supermarket already knows its sales volumes – it has its own processing plants and different qualities of products. What it’s interested in is obtaining data from slaughterhouses about product quality. Similarly, livestock farmers are interested in sales forecasts for different qualities of meat in order to optimize their prices. So we gave to find these kinds of virtuous business models to motivate the various stakeholders. At the same time, we have to work on spreading the word that data sharing is not just a cost. Farmers must not spend hours every day entering data without understanding its value.
What role can research play in all this? What can a platform like TeraLab contribute?
AST: We help highlight this value, by demonstrating proof of concept for business models and considering potential returns on investment. This makes it possible to overcome the natural hurdles to sharing data in this sector. When we carry out tests, we see where the value lies for each party and which tools build trust between stakeholders — which is important if we want things to go well after the research stage. And with IMT, we provide all the necessary digital expertise in terms of infrastructure and data processing.
Learn more about Teralab, IMT’s big data and AI platform.