Development of ground based and Remote Sensing, automated real-time grass quality measurement techniques to enhance grassland management information platform (GrassQ)

• Unité de recherche: OFAG
• Numéro de projet: 10.20_7
• Titre du projet anglais: Development of ground based and Remote Sensing, automated real-time grass quality measurement techniques to enhance grassland management information platform (GrassQ)

Mots-clé (Allemand)

entfernte Abfragung, Grasland, Information, Gras-Qualität, Gras, Gras-Qualitätsmaßnahme, Grasland-Management, Informationsplattform

Mots-clé (Anglais)

remote sensing, grassland, information, ICT, grass quality, grass, grass quality measurement, grassland management, information platform

Mots-clé (Français)

herbe, gestion de la qualité de l'herbe, gestion des prairies, plateforme d'information, télédétection, prairies, information, qualité de l'herbe

Objectifs du projet (Anglais)

The ultimate goal is to enable an intelligent system that will apply precision management to whole farm grassland and grazing systems for farmers, with the aim to optimize grass quality, utilization efficiency, and ultimately profitability, with minimal labour requirement and maximum objectivity. To achieve this goal, the ability to measure the two crucial parameters of grass, i.e. quantity and quality "in the field" in "real-time" is crucial.

Mise en oeuvre et application (Anglais)

Various methodologies are in place to determine grass quality, including automated data capture. However, it is not yet possible to accurately predict the grass quality measures of DM, OMD and CP instantaneously. This objective will be explored through two alternative techniques. A ground based technique will involve using NIRS sensors to generate grass quality data, which will be integrated with grass height data, thus enabling accurate and immediate decisions to be made on grass allocation and site specific fertilizer application management. This combined data will subsequently be fed into a web-based decision support tool whose function is whole farm grassland management, in order to heighten the accuracy and precision of that management. The second technique will use the methodologies of satellite Remote Sensing and unmanned aircraft systems, also known as drones, to collect multispectral and hyperspectral data, which will then be modeled and analysed in order to produce information outputs in the form of maps and images. These outputs will incorporate grass quantity and quality measures and will be made available through a newly developed Smartphone App, from which grassland management decisions can be made.