Data-driven and high-tech innovations

 Self-learning devices that identify possible food contamination and food fraud at an early stage. Smart software that allows consumers to make food choices that are healthiest in their situation. Analyzing robots in agriculture and horticulture that give animals and plants exactly the right treatment at the right time.

Wageningen University & Research is fully committed to data-driven and high-tech innovations
Technology can contribute enormously to solutions for complex social issues. Thanks to data-driven and high-tech innovations, we can make agriculture, horticulture and food production more efficient, resilient and sustainable. In the future, they will make it possible to respond quickly and effectively to changes in climate, nature, the environment and society. Wageningen University & Research will be fully committed to scientific research in the coming years to ensure the desired technological breakthroughs.

Research into data-driven and high-tech innovations is one of the spearheads on the Dutch science agenda. Wageningen University & Research makes a substantial contribution to this in the Data Driven & High Tech research program. We focus on the digital transformation of the agri-food system: from plants, livestock and aquaculture to the environment, nutrition, climate and society. For a sustainable and resilient future of this system, we need systems that learn from the data they analyze. At the same time, we must ensure that we can always explain to society how intelligent systems (AI) arrive at their solutions and answers. So that as humans we always keep a grip on data and technology.

In Data Driven & High Tech we conduct research in four sub-themes:

Artificial intelligence (AI) based on data-driven analyses
The power of artificial intelligence is that devices can make autonomous decisions based on data analyses. This makes many more customized solutions and applications possible. We will use this strength in the coming years to develop technologically advanced solutions. We are also working on reliable instruments to scientifically assess algorithms, data and applications. And we develop freely available datasets that allow machines to discover patterns and adapt to changing situations.

Robotics and decision support
By discovering patterns in large amounts of data, robots can make better, automated decisions. We want to better understand how classic robots can evolve from programmable devices to self-learning systems that can also work together very well. With the knowledge this provides, we want to take the first steps to adapt robots and drones to local conditions. For example, we have developed a harvesting robot for pepper growers that quickly recognizes and harvests ripe peppers based on a 3D environment and 100,000 virtual photos. We formulate new views on the interaction between robots and humans and between robots themselves and identify the ethical dilemmas involved. We develop new methods to learn to perceive and control robots better. And we make our knowledge available to companies, policy and research.

Data sharing infrastructures
It is expected that by 2030 the agri-food industry will make large-scale decisions based on data from shared information sources. Consider platforms (FAIR guidelines) on which data from researchers, machines, sensors and other data sources are linked and shared. WUR is working on several such platforms for growers, agriculture & land use and economic analyses. For example, we support 200,000 Javanese farmers with geodata, based on satellite signals and other sources. With this geodata we help farmers to assess deviations in the physical conditions of their plots. Another example is the use of open data systems to understand the complex logic of the circular economy. We want to gain insight into how data can be securely obtained, linked and shared. On this basis, we develop socially accepted data models that we can use widely, from agriculture and horticulture to aquaculture and food production. We also map out the requirements that research infrastructures must meet to share privacy-sensitive data and how data ownership must be arranged.

Society and business aspects
Data-driven and high-tech research requires clarity about how exactly data and applications will be used. For example, what ethical dilemmas are at play? Who is responsible? And what about data privacy? With these questions as a background, we will test new, data-driven techniques. We map out the legal requirements that these new techniques must meet and develop guidelines for privacy protection, data ownership and ethical dilemmas. Promising possibilities, careful solutions Developments in artificial intelligence (AI), robotics and data-driven analyzes are moving at lightning speed. The possibilities are promising, but ethics, laws and regulations require careful solutions. With this research program, Wageningen University & Research wants to contribute to the successful and responsible use of data-driven and high-tech innovations.

Ethical and social impact of digitalization in the Agrifood domain
Digital technology is not neutral. It may contain hidden preferences; During the development of technical solutions, implicit assumptions are made and decisions are made. By making these assumptions and decisions explicit, it becomes clear which human values are included and which are not. This can be a starting point for incorporating certain stakeholder values into the development and application of new technology. In this way we can make a positive contribution to society and the environment as a whole. The solutions offered become more sustainable, inclusive and fair; the impact becomes more relevant.

To stimulate awareness about the ethical and social impact of digitalization in the agrifood domain among WUR scientists and students, we have created video clips in which various teachers present and discuss ethical issues. These lectures and podcasts cover the following areas:

animal welfare
sharing data
personalized health and nutrition
balance of power
We have invited two lecturers from Wageningen UR to give their views on each of these four topics. The first used the perspective of the development and applications of digital technology, and the second focused on the philosophical perspective. This approach led to lively and interesting follow-up conversations.

https://www.wur.nl/nl/onderzoek-resultaten/onderzoekprogrammas/datagestuurd-en-hightech.htm