Simultaneous Safety and Surveying for Collaborative Agricultural Vehicles
Project information
Simultaneous Safety and Surveying for Collaborative Agricultural VehiclesCall: Enabling Precision Farming
Id: 29839
Acronym: S3CAV
Consortium:
No | Partner | Contact | Country | Total 1000€ | Funded 1000€ | Funder |
---|---|---|---|---|---|---|
1 Coord. | Danish Technological Institute | Michael Nielsen | Denmark | 217.0 | 137.0 | Innovation Fund Denmark Ministry of Science, Innovation and Higher Education |
2 | Politecnico di Bari | Giulio Reina | Italy | 39.5 | 29.4 | Ministry of Agriculture Food, Forestry & Tourism Policies |
3 | Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS Fraunhofer-Gesellschaft | Stefan Rilling | Germany | 78.0 | 78.0 | Federal Ministry of Food and Agriculture |
4 | Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing Consiglio Nazionale delle Ricerche | Annalisa Milella | Italy | 10.0 | 7.0 | Ministry of Agriculture Food, Forestry & Tourism Policies |
5 | AgriCircle AG | Peter Fröhlich | Switzerland | 56.3 | 22.3 | Federal Office for Agriculture - Bundesamt für Landwirtschaft |
S3-CAV focuses on making the ability to perceive the local environment in 3D accessible to farmers transnationally. We devise a sensor framework which we populate with various vision-based and proprioceptive sensors, and combine their real-time input with stored data to provide short-loop safety responses and data sufficient to precisely control an application device in 3D.
The detailed data from the sensors is also sent to a commercial cloud-based Precision Farming Management Information System, where it is combined with stored data from earlier passes and other sources to produce human-readable maps with semantic overlays showing crop health, crop maturity, field traversability, irrigation networks, etc., whatever is relevant and requested. The versatility of this general sensor framework makes it truly transnational -- sensors and overlays can be adapted to specific crops, climates and geographical conditions.
Factors that hinder widespread adoption of Precision Farming methods include those relating to the perceived cost and complexity in getting started, and myths that PF is only feasible for large row crop farms. We address complexity by proposing a general PF solution with a common interface for data- and farm management, auto guidance, data visualization, and action planning, integrated into an existing commercial FMIS. We address the row crop preconception by building our initial test system to work in vineyards and olive groves.
Adoption of our system will be encouraged by being able to input data from any (open format) map-based source, and by the output from our system being compatible with most currently-used agricultural devices, everything that uses ISOXML, and the maps will be displayed on a standard Android tablet PC. Any map-based sensor data in any open format can be incorporated.
Accurate soil mapping is critical to allow highly automated agricultural vehicles to successfully accomplish important tasks including seeding, ploughing, fertilising and controlled traffic, with limited human supervision and ensuring high safety standards. S3-CAV gathers data from high-value crops in a 3D format; the system uses a sensor fusion of light detection and ranging (lidar), hyperspectral, thermal and RGB stereovision to identify crop status including plant nutrition, diseases and pest damage. The data is then made accessible to farmers though a farm management system. Additionally, in S3-CAV, terrain modelling and improved steering in vineyards has been performed and evaluated. According to Eurostat, 169,256 tonnes of crop protection products (CPPs; pesticides) were sold in 2016. CPPs have the potential to cause environmental damage. S3-CAV delivered an algorithm for volume-based spraying that reduces CPPs by more than 20%. S3-CAV also delivers new identification algorithms for grape ingredients and wine tree diseases with hyperspectral cameras. This tool will also help to better understand, identify and tackle infection diseases. With this new information it is possible to reduce the application of CPPs in vineyards by 50%, preventing potential environmental damage, as well as health costs for wine growers. The technology could potentially be used to develop allterrain self-driving systems in agriculture. It is planned to commercialise the S3-CAV sensor box by 2019.
- Development and implementation of advanced perception systems based on multi-sensor platforms and sensor processing algorithms to be integrated onboard agricultural vehicles, in order to support precision farming (PF) tasks
- A unifying sensor framework
- Seamless integration of complex 3D data through the entire pipeline from sensing, through FMIS, to user interaction
- Farm management information systems (FMIS)
- Sensors
- Usability