Big Data for Agri-Food: Principles and Tools

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متاح الآن إلى 2024-06-25
48.00 ساعة تعليمية
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Demystify complex big data technologies
Compared to traditional data processing, modern tools can be complex to grasp. Before we can use these tools effectively, we need to know how to handle big data sets. You will understand how and why certain principles – such as immutability and pure functions – enable parallel data processing (‘divide and conquer’), which is necessary to manage big data.

During this course you will acquire this principal foundation from which to move forward. Namely, how to recognise and put into practice the scalable solution that’s right for your situation.

The insights and tools of this course are regardless of programming language, but user-friendly examples are provided in Python, Hadoop HDFS and Apache Spark. Although these principles can also be applied to other sectors, we will use examples from the agri-food sector.

Data collection and processing in an Agri-food context
Agri-food deserves special focus when it comes to choosing robust data management technologies due to its inherent variability and uncertainty. Wageningen University & Research’s knowledge domain is healthy food and the living environment. That makes our data experts especially equipped to forge the bridge between the agri-food business on the one hand, and data science, artificial intelligence (AI) on the other.

Combining data from the latest sensing technologies with machine learning/deep learning methodologies, allows us to unlock insights we didn’t have access to before. In the areas of smart farming and precision agriculture this allows us to:

  • Better manage dairy cattle by combining animal-level data on behaviour, health and feed with milk production and composition from milking machines.
  • Reduce the amount of fertilisers (nitrogen), pesticides (chemicals) and water used on crops by monitoring individual plants with a robot or drone.
  • More accurately predict crop yields on a continental scale by combining current with historic data on soil, weather patterns and crop yields.

In short, this course’s foundational knowledge and skills for big data prepare you for the next step: to find more effective and scalable solutions for smarter, innovative insights.

For whom?
You are a manager or researcher with a big data set on your hands, perhaps considering investing in big data tools. You’ve done some programming before, but your skills are a bit rusty. You want to learn how to effectively and efficiently manage very large datasets. This course will enable you to see and evaluate opportunities for the application of big data technologies within your domain. Enrol now.

This course has been partially supported by the European Union Horizon 2020 Research and Innovation program (Grant #810 775, Dragon).


Ioannis N. Athanasiadis
Ioannis N. Athanasiadis

Ioannis N. Athanasiadis is a Professor of Artificial Intelligence and Data Science at Wageningen University and Research, in the Netherlands. He investigates the enabling role of artificial intelligence for understanding nature, and big data science applications that relate to environment, agriculture, food and the quality of life. His expertise lies with machine learning, big data, and knowledge engineering.

He has extensive experience leading collaborative, interdisciplinary research projects. He has contributed more than 100 scientific papers related to the science of information in environmental sciences, and have received several awards for as a teacher, researcher and mentor, by iEMSs, IEEE, ACM, SISO, Elsevier, and the Greek Ministry of Defence.

He has contributed actively to the organization of several international conferences, academic workshops, student groups, and capacity-building programs in the developing world. He currently leads the WUR theme on data-driven discoveries in a changing climate.

Sjoukje Osinga
Sjoukje Osinga

Sjoukje Osinga is an assistant professor at the Information Technology group of Wageningen University, within the department of Social Sciences. She studied Artificial Intelligence and Cognitive Science in Groningen and Leuven (1991). Her PhD from Wageningen University was on agent-based modelling of knowledge (ABM) management in the pig sector. As part of her PhD research, she spent six months in China, where she studied information management of pig farmers and pork chain stakeholders.

She was involved in H2020-EU-project "Cybele" (, on big data in agriculture. She was also involved in H2020-EU-project "Dragon" (, on knowledge transfer of ABM and big data tools and techniques.

Sjoukje is member of SiLiCo Centre Wageningen (Simulating Life Science’s Complexity) is a virtual centre that acts as a portal to Wageningen University’s expertise on modelling complex systems through agent-based simulations (

Her research interest lies in finding the match between problem-owners (in agriculture and livestock application domains) and solution-providers from big data and applied artificial intelligence. She is also interested in policy-related questions and decision-making that require ABM, big data or applied artificial intelligence techniques.

Christos Pylianidis
Christos Pylianidis