Data Science for Sustainable Agricultural and Food Systems

This course is for everyone passionate about applying artificial intelligence, data science, and their applications on sustainable agriculture and food systems. The application is particularly in its nature because it links technology, food production, consumption, sustainability, and society altogether.

Objective

The main objective of this course is to enhance the general and specific knowledge of data science application in understanding  how this be applied in sustainable agriculture and food systems: Specific objectives to:

• Learn how to better understand some of the currently  pressing ecological challenges
• To identify some main data science tools applied in agricultural and  food systems to minimize these challenges
• To put hands-on, real-world projects and exercise the data science cycle produces.

• Technical skills are essential, but not enough, non-technical and domain fields of studies are still essential if you want to understand data science vs its application.
• Current and future global challenges in the sector
• How data science or artificial intelligence would be applied.
• Data science and the necessities to keep learning for life.
• Instructor-led online course
• Real-world, practical assignment(s) leading to project
• Application in sustainable agriculture and food production and consumption

• Basic knowledge of basic mathematics, agricultural science, data analysis.

Part I: Introduction and General Background (6 hours)

• General Background of Data Science in Agricultural and food systems
• Main Global challenges and Data-driven solutions
• Open-source data and analysis
• Available and potential Data Science tools

Part II: Application of AI/Data Science for Sustainable Agriculture and Food Systems  (8 hours)

• Application of Artificial Intelligence for Sustainable food production and consumption
• Satellite imagery, remote sensing, and modelling to solve current agricultural and food production challenges.
• Remote research in remote rural and sustainable solutions
• Some examples of how farmers and consumers would benefit from data science for sustainability and battery society

Part III: Real-World Case Study  (6 +2 hours)

• Learn how to get prepared for any real-world projects
• Regional or local Real-world projects in group assignments
• Instructor-led monitoring and following virtual meetings
• Pair/ group project discussion
• Closing conclusions and home take messages
• Feedback and follow-up needs