The rapid advance of technology provides precision agriculture with opportunities. However, these new developments go hand in hand with a (growing) lack of integration between disciplines. Striking: integration is exactly what is needed to make use of opportunities and fit these integrally into business management. The professorship researches how we can integrate precision agriculture opportunities as smart and as sustainably as possible into the business management of arable and live-stock farms. In order to continue sustainable production, it is necessary to apply the right measures at the right time and place and to the right extent.
The professorship focuses on the development of the knowledge that is needed to integrate precision agriculture technology smart into business management. Extracting knowledge from the geographical data and information that is recorded by businesses that use precision agriculture technology. The professorship focuses more and more on strategic choices concerning crop rotation and also on growing crops sustainably.
The professorship has four main themes:
Which information can be used for various business applications? This theme concentrates on the use of task cards to optimize crop protection in time and space. This is elaborates on PL2.0 (translation: Precision Agriculture 2.0). Recent research shows that 10 to 20% of crop farmers in the Netherlands are open to PL2.0, however they have many questions concerning the underpinning of the PL2.0 cultivation recommendations.
How does robotization contribute to solving business economical and societal problems for soil compaction and the provision of human resources. The goal is helping to make robotization facilitate life cycle agriculture. We want to carry out practice oriented research on e.g. weeding robots. We describe the systems that have been used for autonomous navigation, weed recognition and precision weed control. The encompassing research method we use is design-based research, where knowledge is gathered through the systematic development of solutions.
Which applications can be used once an operational data infrastructure for data-driven agriculture and agri-food chains is available? This theme links to the so-called Dutch ‘topsector’ PPS PL4.0 (translation of PPS: Public-Private partnership or PPP). The goal is to give insight into how agricultural businesses go about storing and using data. Subsequently, we work on solving some of the problems. We use specific applications to generate new knowledge via data mining and artificial intelligence methods, such as deep learning.
The objective is providing insights into how effort, costs, revenue and the labour provided by participating agricultural businesses influence business models.
Connection with education
There is a connection with education via the Smart Farming Technology minor. Each year, several students play a part in our research via aptitude tests, graduation projects and internships.
- PPS PL4.0
- POP3 Empowering Precision Farming in Flevoland
- National Testing Ground Precision Agriculture (NPPL)
- Weeds-free crop rows
- POP3 Data farmers