Can AI help nourish a sustainable food system? — Interview with Prof. Riccardo Valentini
The democratization of technology and the ability of AI driven models to better process both quantitative and qualitative data is opening new opportunities for the food system to become both more productive and sustainable.
“When you think about food, you have to think about both agricultural production and reducing environmental impacts,” and Artificial Intelligence (AI) has the potential to play a deeply disruptive role in leading to more sustainable, healthy and productive food systems.
We talked with Professor Riccardo Valentini about new perspectives on how we manage vast amounts of data through Environmental Intelligence and the Internet of things, in what is starting to look like a doorway to the democratization of technology.
From his experience with inventions such as the TreeTalker, a device that monitors tree physiology parameters (water transport, diameter growth, leaf spectral components and tree stability) transmitting data to cloud servers, and his research into the application of IoT, Big Data and AI algorithms to monitor ecosystems functions, Valentini is uniquely placed to give a complete perspective on the current state, criticalities and future role of digitalization in the food system.
In what ways is AI contributing to a more sustainable food system?
First of all, it is important to define what we mean by AI. There are different aspects that often get thrown into the mix but the key areas we should keep in mind are data analytics and the data itself which forms the backbone of all AI algorithms.
Also, it is worth mentioning that looking at the sustainability aspect of the food system is a relatively innovative approach; we are more used to focusing on other industries, such as the fossil fuel one, when talking about climate change even though the food sector contributes to 37% of global emissions — putting it on a par with the electricity generation sector for example.
As part of the CMCC we coordinate a European project on food called Switch which highlights the connection between technology and the food system. This project looks at how Europeans eat, and the challenges involved in ensuring a sustainable and healthy diet.
It also looks at social factors and the habits of people and in doing so generates a large amount of data: what we call a data lake. This data comes from a plurality of sources and ranges from qualitative data — generated through surveys for example — and quantitative numerical data on things like greenhouse gas emissions — which are gathered using digital tools that then send the data directly to the cloud.
One of the main features of this data lake is that it isn’t a purely numerical database: it also contains a wide range of qualitative data which provides information on crucial sociological factors. Here the role of new systems such as machine learning, deep learning or other AI based systems is key as these are more apt at dealing with a plurality of datasets. By their very nature, AI algorithms such as deep learning or Transformer are better able to absorb both numerical and qualitative data.
This is just one example of how a project generates a data lake based on qualitative and quantitative data to look at the impacts of emissions from food systems, whilst also predicting future outcomes and potential strategies that take into account numerical variables such as age, gender, income and so forth.
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Riccardo Valentini is a professor of Forest Ecology at the University of Tuscia, Italy and Director of the Impacts Division of the Euro-Mediterranean Center on Climate Change. His research focuses on ecology, agricultural-forest systems, and problems connected with implementing international conventions for global environmental protection. He is coordinator of several EU projects aiming to understand and quantify the terrestrial carbon budget and greenhouse gas emissions, and studies the application of IoT, Big Data and AI algorithms to monitor ecosystem functions. Among his inventions is the TreeTalker device that monitors tree physiology parameters (water transport, diameter growth, leaf spectral components and tree stability) and is currently used in over 1,000 trees transmitting data directly to cloud servers.
