Development of ground based and Remote Sensing, automated ‘real-time’ grass quality measurement techniques to enhance grassland management information platforms

Project information

Development of ground based and Remote Sensing, automated ‘real-time’ grass quality measurement techniques to enhance grassland management information platforms

Call: Enabling Precision Farming

Id: 35779

Acronym: GrassQ

Duration: 
1 February, 2016 to 31 December, 2018

Consortium:
No Partner Contact Country Total
1000€
Funded
1000€
Funder
1 Coord.TEAGASC - Agriculture and Food Development AuthorityBERNADETTE OBRIENIreland145.0145.0Department of Agriculture, Food and the Marine (DAFM)
2Maynooth UniversityTim McCarthyIreland100.5100.5Department of Agriculture, Food and the Marine (DAFM)
3Cork Institute of TechnologyMichael Denis MurphyIreland25.025.0Department of Agriculture, Food and the Marine (DAFM)
4SEGES P/SFrank OudshoornDenmark123.0112.0Innovation Fund Denmark
Ministry of Science, Innovation and Higher Education
5TrueNorth TechnologiesPatrick HaltonIreland119.60.0None
6AgroTech A/SPhilipp TrénelDenmark99.259.5Innovation Fund Denmark
Ministry of Science, Innovation and Higher Education
7TreeMetrics LtdEnda KeaneIreland50.00.0None
8Finnish Geospatial Research Institute
National Land Survey
Eija HonkavaaraFinland56.039.2Ministry of Agriculture and Forestry
9Production systems
Natural Resources Institute Finland (LUKE)
Jere KaivosojaFinland51.234.5Ministry of Agriculture and Forestry
10Automation and labour organisation
Agroscope
Christina UmstaetterSwitzerland77.450.0Federal Office for Agriculture - Bundesamt für Landwirtschaft
11ASCENDXYZPeter HemmingsenDenmark140.084.0Innovation Fund Denmark
Ministry of Science, Innovation and Higher Education


Summary: 

The focus of this project is to develop and enable an intelligent system that will apply precision management to whole farm grassland and grazing systems. The goal is to optimize grass quality, utilization efficiency, and ultimately profitability, with minimal labour requirement and maximum objectivity. To precisely allocate to the cow herd the absolutely correct area of grass, it is necessary to have an accurate ‘real-time’ measure of grass quality (as well as quantity). The research proposed here is new and innovative, in that two very different techniques will be used to derive this grass quality measure, either by automated grass quality data capture by a near infrared spectroscopy (NIRS) sensor at ground level or by Remote Sensing image data captured using satellite or unmanned aerial vehicles (UAVs) and subsequent predictive modelling. This project provides a unique opportunity for these two techniques to be operated in parallel. The output or product of this research will be the provision of high quality, ‘real-time’, geo-tagged information in the form of herbage mass, and specifically grass quality, through a user friendly software package on a Smartphone App or web-based decision support system (DSS). The grass quality measure will be defined as % dry matter (DM), % organic matter digestibility (OMD) and % crude protein (CP). This latter parameter information (CP) together with the location specific nature of the data will also hold potential for targeted fertilizer application procedures for the future.

Impact: 

GrassQ enabled recent sensing and computational technology developments to be brought together in order to research and develop prototype information services to support improved grassland management. Some of the main potential impacts of this project arise from the cloud-based GrassQ portal where satellite, drone and in situ data and information tools for operational dairy and beef farms can be easily accessed. The GrassQ portal enables researchers, grassland specialists and farmers alike to use the Discovery Module to access free 10m Copernicus Sentinel-2 data or request drone over-flights. Automated work flows allow these datasets to be integrated with in situ data and produce computed estimates of grass metrics such as dry matter (DM) and crude protein (CP). Additional vegetation indices maps can be used to assess the biomass and general vitality of grass growth. Additional tools enable in situ data to be uploaded and stored. All of these features are available through an easy-to-use prototype smartphone app. The overall impact of this project is not so much an introduction of yet another website or smartphone app, but the incorporation of these new satellite and drone sensing technologies, coupled with online modules, functions and work flows, into existing national grassland management tools and practices.

Outputs: 
  • Provision of high quality, ‘real-time’, geo-tagged information in the form of herbage mass, and specifically grass quality, through a user friendly software package on a Smartphone App or web-based decision support system (DSS).
Topics: 
  • Precision Livestock Farming

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