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WageningenX: Big Data for Agri-Food: Principles and Tools
Description
During this course, 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. From this fundamental principle, we move forward. Namely, how to recognize 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.
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 fertilizers (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.
Details
URL: https://www.edx.org/learn/big-data/wageningen-university-research-big-data-for-agri-food-principles-and-tools?Communities:Disciplines:Target Groups:
- Researcher
- Competent
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