Upon successful completion of this course, the students will be able to identify challenges and opportunities in the agri-food sector, and more specifically:
- Understand and explain the role of data science and AI in the agri-food sector including being able to explain the prerequisites for a successful application of data science, the opportunities and potential risks.
- Analyze real data sets, for example from livestock and crop production or food processing, finding useful patterns, relations or deviations.
- Develop new data-driven approaches that create value across the food supply chain.
Ensuring the food supply for a growing, higher consumption world population in the face of finite resources is a major challenge. In industrialized urban societies, however, access to food has become taken for granted. This is only possible because of a sophisticated food supply chain that runs from agriculture to food processing industry, to wholesale, to retail, bound together by a vast logistic network. Together, the agri-food sector is one of the world’s major trades and industries. Here, ancient practices meet agricultural robots, crop improvements based on bio-molecular life sciences, automated processing lines, global logistic planning and targeted marketing campaigns for consumers.
The Netherlands are the second-largest exporter of food in the world: Agri-food generates €50 billion of added value per year and accounts for more than €80 billion of exports. It accounts for 25 percent of Dutch exports, more than 50 percent of its trade surplus, and nearly 10 percent of the national income and employment. The Dutch agri-food sector excels in innovation and productivity. Among the 40 largest food and drink businesses in the world, 12 are based in the Netherlands or have their R&D activities here. The country is also home to a strong agri-food knowledge infrastructure. In addition to well-known companies, a large number of small businesses operate in the agri-food cluster, particularly in the primary sector.
Feeding future generations, while being more friendly to the environment and animal welfare, requires innovations to increase precision and to improve control and efficiency. Increased precision and control inevitably require better measurements and an increase in data intensity in the food value chain and open up new business and technical opportunities.
Some important developments as results of fast developments in digitalization, sensing technology and high-tech in farming include precision farming (e.g. use of drones, soil scanners, autonomously operating equipment) and use of robots and sensors (e.g. for harvesting, weed control or processing and sustainable livestock management). Sensors are also used to keep track of climatic conditions in warehouses and chemical processing in the food industry. The food industry is therefore given new opportunities to produce local, high quality, minimally processed food using (remote) food quality sensing and control.
Finally, research in the agri-food sector and land management creates its own sources of data. This provides information that can be used to improve decisions over the whole supply chain, from “farm to fork”.
Teaching methods / assessment
- Interactive classroom lectures
- Directed student dialogue
- Group assignments and individual exercises
The final grade will be weighted:
- 50% individual written exam
- 30% group report
- 20% group presentation
- Statistical data analysis using Python/Scikit/Pandas or R
- Programming skills in Python or R
- Familiarity with business models
This course will provide students with the knowledge and the skills to detect and use new data sources, apply different artificial intelligence techniques and derive challenging questions for business model innovation in the agri-food sector. This course is part of the minor Agrofood and has a companion second course called “Servitization and Data-Driven Innovation” (JM2000).
The courses from the Data Science and Entrepreneurship program require specific prior knowledge. It is only possible to participate in this course if approved by the admission committee and if you are enrolled for the program. Please note that this course will be taught in Mariënburg, ‘s-Hertogenbosch (JADS).