Controlled traffic farming (CTF) aims to avoid soil compaction on parts of agricultural fields that are not traversed by the wheels of heavy agricultural machinery. CTF management can play a key role in sustaining soils and future crop production, which are today threatened by heavy machinery traffic and intensive production systems. To play this role in sustainable intensification, CTF needs to be developed to become a mainstream technology rather than a niche practice.
The overall objective of the CTF-OptiMove project is to develop an integrated CTF innovation package based on research, operational tools and decision support systems which will underpin the wider adoption of CTF and related position based technologies. The project involves applied research which will quantify and illustrate the benefits of controlled traffic and related technologies in terms of crop, soil, and machine efficiency and; development of innovative decision support and operational tools which will allow CTF technologies to be optimised. Optimisation of agricultural logistics will be based on the Geospatial Arable field optimization Service (GAOS) and implemented in a Service Oriented Architecture (SoA). Designated field trials will be carried out in four countries: Belgium, Ireland, the Netherlands and Denmark. Using information on field size and machinery size, simple modelling will be used to scale up the research results to individual farm and regional levels.
The project and related activities can benefit from students focussing on software engineering aspects and/or application aspects of the work. Software engineering topics include the transition from services tightly coupled to a database to services operating on volatile information objects that are not centrally stored. Also, the communication with auto-steering consoles on the tractor needs further elaboration. Application topics include taking into account within-field obstacles and vulnerable spots in the paths optimization, automated generation of headlands and dealing with sloping land.
Objectives (choose from):
- Automate headland generation
- Increase the efficiency of path optimization
- Increase support for auto-steering consoles
- Help elaborating the Service Oriented Architecture
- Handle within-field obstacles and vulnerable spots
- Path planning on sloping land
- Assess usability of services
- To be discussed...
- de Bruin, S., Lerink, P., Klompe, A., van der Wal, T., Heijting, S., 2009. Spatial optimisation of cropped swaths and field margins using GIS. Comput. Electron. Agric. 68 (2), 185–190.
- de Bruin, S., Lerink, P., J. La Riviere, I., Vanmeulebrouk, B., 2014. Systematic planning and cultivation of agricultural fields using a geo-spatial arable field optimization service: Opportunities and obstacles. Biosystems Engineering 120, 15-24.
- Jensen, M.F., Bochtis, D., Sørensen, C.G., 2015. Coverage planning for capacitated field operations, part II: Optimisation. Biosystems Engineering, 139, 149-164
- Strong analytical skills
- Knowledge of Python scripting is a must
- Preferably some agronomic knowledge
Theme(s): Sensing & measuring, Modelling & visualisation