Winners

Winners 2018

First prize:
Team Teagasc, Ireland (Richa Marwaha, Azucena Jimenez Castaneda, Gabriela Afrasinei). "Feed On DemanD - wEdge & gRazing App” - FODDERApp

Second prize:
Team TREASURE, pan European (Brian Weaver, Paula Testa, Kai Guo and Jon Bruno). "GALILEO for automated transplanting of crop seedlings"

Third prize:
Team Space Junk, Italy (Ahmed Galal, Marco Sozzi, Domenico Giora). "Copernicus Satellites Data Fusion for Management Zones Definition".

 
SCROLL DOWN FOR MORE INFORMATION ABOUT THE WINNING ENTRIES


» Brochure will be available soon (.pdf)

First prize: Teagasc – Ireland

The Team is composed of two PhD students and a post-doctoral research fellow at Teagasc Agriculture and Food Development Authority of Ireland, the national body providing integrated research, advisory and training services to the agriculture and food industry and rural communities.


Title of Entry: “Feed On DemanD - wEdge & gRazing App” - FODDERApp

About Teagasc

The Team is composed of two PhD students and a post-doctoral research fellow at Teagasc Agriculture and Food Development Authority of Ireland, the national body providing integrated research, advisory and training services to the agriculture and food industry and rural communities.

Azucena Jiménez Castañeda: PhD student (2nd year) at Teagasc Food Research Centre, Ashtown in Dublin and Maynooth University, Ireland. Her research interests include meteorological radar, and statistics. Currently, she is working on chain radar signal processing to use meteorological nowcasting techniques to detect rainfall for real time agricultural advice.

Gabriela Mihaela Afrasinei: is a geographer from a picturesque city on the Danube in south-east Romania, with a PhD in Environmental Sciences awarded in Italy. Currently she is a post-doctoral research fellow at Teagasc Food Research Centre, Ashtown in Dublin. Her research focuses on the use of remote sensing and geospatial analysis for land cover, land use and soil mapping and classification, and land degradation assessment.

Richa Marwaha: is a PhD student (2nd year) at Teagasc Food Research Centre, Ashtown in Dublin and UCC, Cork. Her research interests include remote sensing and satellite image processing. Currently, she is working on grassland biomass estimation using machine learning algorithms.

Mentor: Stuart Green 

About their Entry

In the meat and dairy sectors across Atlantic Europe sustainable livestock farming requires proper grassland management. We propose “Feed On DemanD - wEdge & gRazing App” - FODDERApp, a satellite-based mobile application to support efficient farm management. This webGIS platform automatically acquires and processes latest available Copernicus Sentinel-1, Sentinel-2 and meteorological data (near real-time) to estimate above-ground biomass on a per-field basis employing proven machine learning algorithms.

FODDERApp delivers grass budget, management options and growth scenarios over a season. This reduces the time spent by farmers manually measuring or estimating grass growth and provides explicit data on grazing rotation, grass intake, fertiliser application, grass production supply and demand, allowing planning and managing no-grazing-season fodder stocks. The app also serves as two-way crowd-sourcing tool, providing input and acquiring ground truth data through user feedback

Judges’ comments

“This team presented an idea with a strong focus on practical issues and use by farmers. During their final presentation we liked being asked to put ourselves in the shoes (or boots!) of the farmer. The idea has high market potential, the team already have a working prototype, and good examples of how it could work.”

How is Teagasc describing the experience

 

 

 

 



Second prize: TREASURE - pan European

The authors are Early Stage Researchers (ESRs) of the TREASURE project, a prestigious Marie Sklodowska-Curie Actions (MSCA) Innovative Training Network (ITN), funded through the European Union’s Horizon 2020 Research and Innovation Programme. 


Title of Entry: Galileo for automated transplanting of crop seedlings

About TREASURE

Jon Bruno is a PhD candidate in ionospheric tomography at the University of Bath and conducts research on ionospheric tomography and data assimilation.

Brian Weaver and Kai Guo are both PhDs in civil engineering at the University of Nottingham, Kai conducts research on ionospheric scintillation sensitive tracking models and mitigation tools, While Brian studies PPP and RTK algorithm development.

Paola Testa is a PhD student in strategy at the Toulouse Business School, where she conducts research on the diffusion of innovation and strategic marketing in the context of the space economy.

The authors are Early Stage Researchers (ESRs) of the TREASURE project, a prestigious Marie Sk?odowska-Curie Actions (MSCA) Innovative Training Network (ITN), funded through the European Union’s Horizon 2020 Research and Innovation Programme. TREASURE is a very international team composed of PhD students spread all around Europe.


About Team TREASURE Entry

Automated transplanting is a modern farming practice where young crops are first germinated in greenhouses then automatically planted in the field. This technique can provide significant benefits: increased yields, better crop quality and resilience, reduced costs and impact on natural resources.

Automated transplanting through GNSS-based guidance allows precise placement of crops during specific planting intervals to ensure optimal environmental conditions and crop survival rates. In order to make the benefits that such practice offers more affordable even for small farmers, the TREASURE team proposes an EGNSS-based PPP-RTK positioning algorithm augmented with ionospheric corrections to improve precision and reduce the Time-To-First-Fix (TTFF) for a single-receiver GNSS user. The developed method can reduce automated transplanting downtime, increase economic efficiency, and maximize operational productivity. These benefits may help the agriculture industry in sustainably bridging the current yield gap, especially for small farmers.

Judges’ comments

“This was a very professional presentation which addressed a hot topic. It proposed innovative, fast converging of PPP solutions. The team showed solid knowledge of precision technologies and their idea has many other potential applications.”

How is TREASURE describing the experience

 

 



Third prize: Space Junk – Italy

The team consists of three postgraduate students at the Digital Agriculture Lab, University of Padua, Italy.

Title of Entry: Copernicus Satellites Data Fusion for Management Zones Definition 

About Space Junk

The team consists of three postgraduate students at the Digital Agriculture Lab, University of Padua, Italy.

Marco Sozzi (centre): His work focus on data fusion and remote sensing in precision viticulture.

Ahmed Kayad (left): His work focus on yield monitoring, prediction and evaluation of yield limiting factors in field scale.

Domenico Giora (right): His work focus on a case study of field spatial variability and the detention of management zones.

Mentor: Dr Francesco Marinello

About their Entry

The European Earth Observation satellites are powerful tools which can help to perform precision agriculture. Space and time variability of crops and soil features can be detected by several instruments mainly represented by proximal and remote sensing. Proximal sensors allow detection of crop or soil features with a sub-metre resolution, but they are often expensive. On the other hand, remote sensing allows detection of a large quantity of information quickly and relatively cheaply.

Several studies have shown the usefulness of Sentinel-1 or Sentinel-2 satellites, but a methodology that takes advantage of both sensors is not clear.  Electromagnetic sensor (EMI) data can be integrated with soil sampling and vegetation mapping by NDVI to draw a prescription map for nitrogen fertilisation. Data fusion from different satellites can take advantage of increased spectral and temporal resolutions. 

This work shows two innovative data management approaches: (1) The use of backscattering coefficient for soil properties in centre pivot system, and (2) the use of the correlation coefficient to evaluate the NDVI trend during the growing season.

This method can be easily applied all over the world since it is based on free of charge software (SNAP-ESA, GeoFis and QGis) and free of charge satellite images (Sentinel-2).

Judges’ comments

“This entry was about data fusion, a hot topic today. It was a nice end-to-end concept with a good technical background that included both a case study and a business angle. A rounded and well-delivered entry.”

How is Space Junk describing the experience