Fusion of multi-source and multi-sensor information on soil and crop for optimised crop production system
Project information
Fusion of multi-source and multi-sensor information on soil and crop for optimised crop production systemCall: ICT and Automation for a Greener Agriculture
Id: 14303
Acronym: FarmFUSE
Consortium:
No | Partner | Contact | Country | Total 1000€ | Funded 1000€ | Funder |
---|---|---|---|---|---|---|
1 Coord. | Faculty of bioscience engineering, Ghent University | Abdul Mouazen | Belgium | 172.0 | 154.8 | Department for Environment, Food and Rural Affairs |
2 | Agricultural Engineering Laboratory Department of Hydraulics, Soil Science and Agricultural Engineering School of Agriculture Faculty of Agriculture, Forestry and Natural Environment Aristotle University of Thessaloniki | Dimitrios Moshou | Greece | 166.2 | 142.2 | Managing Authority of the Rural Development Plan Ministry of Rural Development & Food |
3 | Professorship for Geodesy and Geoinformatics Chair of Geodesy and Geoinformatics Faculty of Agricultural and Environmental Sciences Rostock University | Ralf Bill | Germany | 163.3 | 139.3 | Federal Ministry of Food and Agriculture |
4 | Vocational School of Technical Science Uludag University | Yucel Tekin | Turkey | 92.5 | 92.5 | Scientific and Technological Research Council of Turkey |
5 | tec5 AG | Steffen Piecha | Germany | 46.3 | 34.7 | Federal Ministry of Food and Agriculture |
6 | Professorship for Geodesy and Geoinformatics Chair of Geodesy and Geoinformatics Faculty of Agricultural and Environmental Sciences Rostock University | Jens Wiebensohn | Germany |
Ignoring the inherited spatial variation in soil properties with traditional sampling methods leads to poor crop management, yield loss and excess use of input. The proposed system of FarmFuse addresses these issues in 2 ways: (a) utilising a new and innovative on-line multi-sensor platform for measuring key soil properties at an appropriate resolution. (b) Integrating this improved soil data with other information such as vehicle-borne sensing of crop growth, weather data, soil conductivity and yield maps, to develop algorithms to determine rules for variable rate application. These can then be integrated into FMIS. The vision for the final integrated system would be a server that would allow the end user to access and upload data including maps of soil, crop and yield in addition to recommendations about site specific applications. The resultant treatment maps would then be uploaded to precision agriculture compatible implements for site specific application of inputs.
Ignoring the inherent spatial variation in soil properties with traditional sampling methods has led through the years to poor crop management, yield loss and excess use of inputs. FarmFUSE addresses these issues by fusing a set of data on soil and crops with auxiliary data on topography and weather to delineate management zones for variable rate of nitrogen (VRN) fertilisation. Once the integrated concept is applied by a large number of farmers, a reduction in nitrate leaching into groundwater is foreseen, which supports the EU Water Framework Directive, Nitrates Directive and Integrated Pollution Prevention and Control Directive. It will also reduce fertiliser use and, hence, reduce greenhouse gas emissions and global warming potential. The project’s specific impacts are measured as: (i) contribution to the creation of a new precision farming service provider named FarmingTruth Ltd in the UK; (ii) contribution to the development of a new visible and near infrared spectrometer produced commercially by tec5; and, (iii) contribution to the creation of a new product for VRN fertilisation, which was proven to increase farmer net income by increasing yield at reduced input cost and environmental impact.
FarmFuse is based on the following two innovative technologies and approaches:
a) utilising a new and innovative on-line multi-sensor platform for measuring key soil properties at high sampling resolution.
b) Integrating this improved soil data with other information such as vehicle borne sensing of crop growth, weather data and yield maps, to develop algorithms to determine rules for variable rate N fertilisation.
The following are project output:
- High sampling resolution (> 1000 sample per ha) maps of key soil properties
- Data fusion algorithms based on machine learning and geospatial analysis for variable rate nitrogen fertilisation
- Soil and crop maps and recommendations for variable rate N fertilisation are integrated into a farm management information system
- Variable rate nitrogen fertilisation
- Proximal soil and crop sensing
- Data fusion and geospatial analyses
- Farm management information system