Keynote Presenters' Abstracts | Print |

Kevin McDonnell, University College Dublin, IE
Big data opportunities for food and agriculture: research and education challenges

The progress and adoption of precision agriculture technologies in regards to the development a sustainable agriculture is based on theKmcd-cropped 80pxl implementation of new methods and tools to improve yield results and decrease chemical impacts on the environment. Information and Communications Technology (ICT) systems have been proposed as one of the main tools to improve strategical and operational results. In the last number of years, the continuous enhancement of sensing technologies have produced large quantities of spatial and temporal data with characteristics which would facilitate the development of machine learning systems and decision support tools to assess and improve crop production. Applications of machine learning in agriculture that make use of classification, clustering, regression and prediction algorithms, have been used for activities such as weed detection, canopy cover estimation, yield prediction and disease assessment. At the core of the farming processes, farmers are the main decision-making proxies accountable for increasing local and consequent global production. Decision support systems (DSS) have been developed as key tools prospecting the goal of attaining evidence-based decision-making in agriculture. However, with the increasing availability of agricultural data and the complexity of the data sets, new skills in combining data sciences with biological sciences are required to enable the sector to fully utilise the potential of the digital agricultural era.

Corné Kempenaar, Wageningen Plant Research, Wageningen University and Research & Aeres University of Applied Sciences, Dronten, NL
Precision Farming: advances in high tech, data-driven agriculture at field, farm and regional level

Precision Agriculture is about doing the right thing at the right time and right place in the right amount. Four times right! This rather new farm management concept, also named smart farming or digital farming, is seen as a means to reduce costs and to improve productivity of farming. The Dutch agrifood sector is a front runner in developing and implementing innovative techniques, methods and products to enhance precise and site specific management of crops and livestock. Digitalizing of agriculture supports the development of precision agriculture in the supply of key technologies e.g. satellite navigation systems, earth observation, sensors, robotics, data collection and (big) data science.

Precision Agriculture is also seen as a means to achieve societal goals. Our educational and research expertise on production systems, production techniques, soil, water, climate, ecosystems and geospatial and data technologies can be applied to improve resource efficiency, food security and safety, and to reduce the environmental footprint, energy use and emissions of farming. Food chains will become more transparent and climate smart with precision agriculture.

Wouter Saeys, Department of Biosystems, MeBioS-Biophotonics, KULeuven, BE
High Tech in the Food Value Chain – non-invasive sensing of food quality in manufacturing, distribution and retail

The European Food and Drink Industry is a vital pillar in the global EU economy, being the largest manufacturing sector in the EU both in terms of turnover and employment. The sector remains the largest global exporter of food and drink products. European consumers demand high organoleptic quality, authenticity and food safety. This is also appreciated by the export markets which stimulate further growth. High quality standards have to be met and guaranteed to avoid confidence breaches and image deterioration. This forms a strategic challenge for the Food Value Chain which can only be answered through a high level of quality monitoring. On the other hand, consumers are permanently price sensitive. So, excellent quality has to be guaranteed in the most efficient way.

To reach these goals, quality control of the incoming products, the intermediate products and the final products is crucial. Ideally, every product should be inspected in a rapid and non-destructive way such that proper action can be taken without delay. This means that the industry has to move from sample-based quality control in the laboratory to in-factory measurements. Thanks to their fast and non-destructive character, photonic technologies have large potential to act as a catalyst in this transition.

In this keynote lecture, an overview will be given of the recent trends in spectroscopic sensors and imaging technologies and their potential for In-Factory Food Analysis.

Marc Mauermann, Deputy Director Division Processing Technology Dresden, Fraunhofer Institute for Process Engineering and Packaging IVV, DE
High Tech in the Food Value Chain - Digitisation in industrial food processing and packaging

Digitisation is offering many chances to react on the growing complexity in food processing and packaging by using new tools and methods. The linking of relevant data along the food value chain improves transparency of food production and food safety may benefit from it. Process efficiency can be improved and machine operators may benefit from an assistant system that provides information about the status of the process as well as advice from other operators.


Barteld Braaksma, Innovation Manager, Statistics Netherlands, NL
Key challenges in the management of Big Data – for the institution and the individual

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The ongoing digitalisation of society comes with tremendous changes in the areas of collection, management, processing and dissemination of data and information. Big data and the Internet of Things have different characteristics than traditional sample surveys and administrative sources. At the same time, needs and expectations of users are changing. They expect easy access to information online, via search engines and social media, instead of going to a library to read a paper publication. All this poses huge challenges in many different ways on an information supplier like CBS, the official Dutch statistical institute. CBS has established a Center for Big Data Statistics (CBDS) to deal with new (big) data sources in a (semi)organised way. We describe the CBDS-approach and illustrate it with concrete examples including our new policy of publishing experimental statistics. Collaboration with public, private and academic partners and stakeholders is an integral part of the approach and takes many different forms, both in the kind of relations we establish (which may either focus on data, expertise or infrastructure sharing) and in the concrete collaboration models we use like co-creation, data camps and Urban Data Centers. Citizen science, crowd sourcing and serious gaming also become increasingly important for us and we are exploring how and to what extent we can use them. To deal with more generic IT and data sharing issues, CBS has recently drafted a new corporate Data Strategy that takes into account developments such as data lakes, data virtualization techniques and advanced remote access/execution methods. A new way to interact with users of statistical information is an important cornerstone of the data strategy. To show that developments at CBS are not isolated we sketch the national and international context in which we operate including some recent developments.

Andreas Jedlitschka, Head of Department Data Engineering, Fraunhofer Institute for Experimental Software Engineering (IESE), DE
Ethical issues relating to the application of Big Data

5D3 2962-photo cropped 80pxlBig Data is omnipresent and used in an increasing number of business models. The explosion of computational power that has led to the re-emergence of artificial intelligence is creating new opportunities, but also new challenges, for society as well as for individuals. Societal challenges do not only arise from the increasing speed and extent of automation but also from potential misuse of available data, biased algorithms, auto-generated fake news, etc. This talk will address the aspects of ethics in relation to the use of Big Data, in particular in combination with artificial intelligence (AI). Recent issues regarding the use of AI and future developments and perspectives will be discussed. The impact on society is comprehensive and complex. The question is what consequences a common understanding of ethics and morality would entail for the further development and application of AI. Would it lead to a situation similar to the one in embryonic stem cell research, where we have a "dilemma between two moral principles: (1) the duty to prevent or alleviate suffering and (2) the duty to respect the value of human life" [1]?. Due to easy access to Big Data and the availability of AI technology, countless people can develop and use products and services that are beyond the control of the state or any kind of regulation. Initiatives such as "Ethics in Action" [2] are necessary and show the willingness of researchers to do good. What does this mean for the future of education at universities? We have to raise awareness for both the opportunities and the risks associated with the use of new (yet unknown) technologies. We have to teach, demonstrate, and live accountable and transparent research to limit (at least unintentional) unethical behavior by developers and users. As a result, education in human values, e.g., mandatory lectures on Philosophy, Ethics, Human/Animal Rights for engineers, are becoming much more important than in the past.
[1] last visited 19.10.2018
[2]  last visited 19.10.2018

Petra Jorasch, Manager Plant Breeding and Innovation Advocacy, European Seed Association, BE
Legal issues relating to the use of high tech – as exemplified by novel plant breeding technologies

Petra Jorasch 1 cropped 80pxlPlant breeders have always strived to create new variations of plant characteristics to provide solutions for disease and pest resistance, to achieve higher yields, to increase tolerance to environmental stress, and to breed new plant varieties that meet consumer expectations. The rediscovery of Mendel's laws of heredity, in the early 1900s, turned the first plant breeding efforts from an art into science, and specialised farmer-breeders emerged, building a business concept on their efforts. From that point in time, scientific breakthroughs in agricultural and biological sciences have accelerated and implemented into breeding programs with the aim to bring innovations on the farm and into the fields.

Governmental policy should be firmly based on sound scientific principles to avoid the risk of impeding innovation in plant breeding. Therefor seed industry takes the position that plant varieties developed through the latest breeding methods should not be subject to different or additional regulatory oversight if they could have been produced through traditional breeding methods or might also have been obtained from natural processes without human intervention.

With the decision of the European Court of Justice in the Case C-528/16 a more than 10 years ongoing legal discussion about the regulatory status of certain new breeding techniques was decided. Plants resulting from new mutagenesis methods need to be treated and approved as genetically modified organisms according to EU law. The presentation summarizes the outcome of the ruling, discusses its impact on science, breeding and agriculture as well as options for a way forward.

Martin Gerzabek, University of Natural Resources and Life Sciences, Vienna, AT
Strategic Industry 4.0 – agriculture/forestry 4.0 – university 4.0? Strategic policy at BOKU to address digitisation

Life Science Universities face several megatrends. The first is the increasing hunger of humankind for biomass and the increasing challenges including climate change to secure food, feed, fibre and fuel from primary production. The second megatrand is the digitisation. The scientific and digital revolution shapes the society today – the second large turning point of humankind after the neolithic revolution and introduction of agriculture 10,000 years ago. Available information increases by ~30% per year, inducing a terrific speed of change and putting pressure on higher education.

Research and science driven bio-economy seems to be the only way to master human kinds future. The complexity of a global bio-economy system can only be mastered by digitisation.

Several innovations are needed for HEI, one is the development of competences to shape change processes, which needs „digital skills", critical faculties and social competences - more important than ever. Trends like the massive open online courses make it necessary for HEI to differentiate and further develop their core competencies. Research based education is the major core-competence of universities as research establishments, which create new knowledge and feed it into educational offers.
Universities need a digitisation strategy helping them to shape their institutional identity. This strategy should include: (i) new educational and learning forms, (ii) infrastructure (server capacities, access to super computers), (iii) Use of digitisation for renewing and modernizing of education, research and third mission. Chances are manifold. Digitisation can be the basis to support a more and more heterogeneous students collective by fostering personalized learning and creating new competence profiles for graduates.

BOKU maintains in her strategy of a three pillar system of natural, technical and socio-economical sciences. By that, the university has a good basis to master the big challenges at present and ahead. Digitisation has now to be implemented as a cross cutting topic (like sustainability and bio-economy) across all competence fields of the university and across all educational programs. The strategy is not to duplicate the offers of the universities of technologies, but to focus on core competences with respect to digitisation. BOKU started now by implementing an externally sponsored full professorship on "digitisation and automation of the traffic and mobility system" and by defining a new full professorship on "Digitisation and automation in bio-economy. Furtheron, BOKU can use new career pathways like assoc. professorships as tenure to foster the digitisation topic in order to complement the established educational offers.

Vik Vandecaveye, Precision Solutions & Telematics division, Case IH Agricultural Business
Strategic Policy Industry 4.0
ABSRTACT to follow

Lukasz Grus, Wageningen Data Competence Centre & Laboratory of Geo-information Science and Remote Sensing, Wageningen University and Research, NL
Objectives and considerations of (Big) data science education in Life Sciences domain

Domain specific research involving big data requires skills and knowledge in the field of data science. This varies from technical skills of data, software, databases, and programming, to more advanced knowledge of model-based calculations, visualizations, and advanced analyse methods. Also ethical and privacy aspects of data collection and use play here a crucial role. Wageningen University is currently enriching its education portfolio to offer present and future students, PhDs, staff and external professionals education in those data science aspects. WUR finds it essential that the data science skills and knowledge will be combined and applied in specific domains such as plant, animal, environmental, social and agrotechnology & food sciences. The WUR education offering of data science courses focusses on this domain application ambition. In this presentation the objectives and challenges of combining data and life sciences into education programmes will be presented and discussed.