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The opportunities of AI in food and ecosystems

Wageningen research groups are working with artificial intelligence (AI) more and more. What opportunities and dilemmas does that present for our research? And how can WUR invest in so-called ‘responsible AI’?

Wageningen research groups are producing and using more and more data. These data are fundamental to our understanding of food systems, ecosystems, and other processes studied at Wageningen. AI provides many new ways of interpreting these data.

For example, the Netherlands Plant Eco-phenotyping Centre (NPEC) in Wageningen takes enormous amounts of plant measurements under controlled conditions. By growing and measuring multiple crop varieties under different environmental conditions, they try to find out how the interaction between DNA and the environment works. These measurements generate an enormous amount of data, already more than 1,000 terabytes.

NPEC is currently using AI, according to Professor Mark Aarts and Rick van de Zedde of NPEC. Plant researchers take images of the plants in the greenhouse. An AI program provides a filter on these images so that only the relevant plant parts are analyzed. Irrelevant aspects like the background, pots and sticks are filtered out. But now they are discovering that they can extract more information from their amounts of data.

Plant disease detection
In another project, researchers are working on plant disease detection. The researchers grow different varieties in the greenhouse, introduce a pathogen, and then follow the health condition of the plants. They use a range of fully automated imaging systems to determine whether the plants are sick and, if so, how sick they are. Now, they are training an AI system to recognize the disease stages on camera images since, using AI, computers can detect diseases earlier and more accurately than we can with the naked eye.

AI can also help find and explain strange deviations in tests. If the data for some plants deviate from the average, is this because they have received less water (the tube was clogged) or because that genotype responds differently to the treatment? Researchers often only see such deviations at a later stage, and then the meaning can no longer be determined, especially in large data sets. With AI, the computer can immediately identify and analyze this or search for it in the database.

WUR appointed three personal professors in the field of artificial intelligence (AI) and data science over two years ago. Anna Fensel, Ricardo da Silva Torres, and Ioannis Athanasiadis work in various chair groups with support from the Wageningen Data Competence Center. They create, develop, and apply AI solutions for issues in the Wageningen domain.

Digital tomato
Philosopher Vincent Blok tells more about their tomato research. ‘We are doing a project with digital twins, digital copies or representations of real things, to experiment with these copies. As an example, we have made a digital copy of a tomato. But what data do you need for that? The plant and food scientist will say: we need information about the shape, color, water content, and vitamins. The supermarket mentions uniformity and price, and the consumers mention different criteria. You soon discover that there are many implicit definitions and characteristics to capture a digital tomato – it is not a neutral representation, although engineers often think so. Decisions about the desired tomato are often made for commercial reasons, and defining the digital tomato makes those reasons visible.’

But in the next phase of AI, the computer may propose new tomato varieties with limited instructions from humans, using hypotheses from collected observation data. At NPEC, this future is already within reach. Mark Aarts: ‘The next step is to recognize patterns from large data files that the researchers had not yet noticed, if only because of the sheer quantity of data to be interpreted. Then, we can research AI-generated hypotheses. For instance, AI identifies a pattern and asks researchers: is this an interesting pattern? We are convinced that this will play an important role in plant breeding in the future.’

Is there still work for the plant researcher in the next phase? Aarts: ‘Yes, the domain experts remain crucial in the design and application of AI methods for data interpretation together with AI experts so that we can advance domain knowledge as well as AI methods.’

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