Digital solutions are a critical component to solving the big challenges faced by the agricultural sector such as food security, climate change and sustainability. Digital solutions can ensure companies a competitive advantage as well as market growth. However, traditional research and development activities in companies comes with significant capital requirements as well as long turnaround times.
■ How does the driver get the (complex) processes and relevant information (e.g. obstacles or crop characteristics) visualized in an easy-to-grasp form and how can he interact with them safely and quickly?
■ How can processes outside the cab (e.g. a fleet of field robots) be monitored and controlled - without losing control of their own machine?
■ What can a new form of human-machine interaction look like in this environment?
■ The work tasks of the machine operator are changing: the automated machine is taking over more and more classic operating tasks, and the operator is increasingly organizing the processes around it (e.g. fleet management, farm management, private organizations). BUT: The farmer does not just want to sit in the office, she/he wants to be on site on the machine.
■ The cabin must be a motivating, attractive and modern workplace in order to continue to attract skilled workers to agriculture.
■ Cameras and other sensor technologies generate information, which could help to make better decisions on the field. The prerequisite for this is an intuitive data visualization. Autonomous field robots are set up and managed via PC / tablet - a concept for integration in a smart cabin does not yet exist.
Multi route optimization for autonomous infield machine collaboration
- The narrow ”Harvest window” of fieldwork poses a need for farmers to execute their work across the harvest chain (dept, road, field) as efficiently as possible.
- Complications arise when multiple machines and humans are present on and off the field, as current systems do not support inter-machine communication and planning.
- Development of a multi-route planning algorithm that adjusts to the different levels of automation along the harvest chain.
- Optimization of the complete operation in terms of route and machine needs for efficiency (fastest), economy (cheapest), and environmental impact
- Creation of a 2D simulation to visualize the complete operation of the machines and the depot, showcasing the functionality of the algorithm.
- Illustration how the Farm Manager, the Combine Operator, and the Tractor Operator can interact with the system.
- A system that optimizes the movements of the numerous machines, both autonomous and human-driven, involved in the entire harvest operation (depot, road, fields) and reduces threats from unpredicted behaviour and decisions by humans. A simulation of the harvest process will visualize the developed algorithm.
- Paid PoC for 100 days (3 months)
- Exhibitors and partners at AGRITECHNICA identify a challenge.
- DLG.Prototype.Club selects teams of software developers, engineers and startups to solve these challenges
- The teams enter a rapid prototyping phase two weeks before AGRITECHNICA.
- The teams present their fully functional prototypes together with a business plan to the Challenge partner LIVE during AGRITECHNICA.
- The complete process from challenge announcement to the announcement of the winner of the challenge is accompanied by a high-profile media campaign.