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.
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.
What you will learn
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 the 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 follow virtual meetings