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    GrassQ - A holistic precision grass measurement and analysis system to optimize pasture based livestock production


    Murphy, Darren J. and O'Brien, Bernadette and Askaril, Mohammad Sadegh and McCarthy, Tim and Magee, Aidan and Burke, Rebekah and Murphy, Michael D. (2019) GrassQ - A holistic precision grass measurement and analysis system to optimize pasture based livestock production. In: 2019 ASABE Annual International Meeting, Boston, Massachusetts July 7–10, 2019. ASABE.

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    Abstract

    GrassQ is a holistic grassland decision support system (DSS) that encapsulates a range of measurement technologies to provide yield and quality data to a cloud based platform, which can provide users with real time management information in the field. GrassQ aims to promote precision agricultural concepts within the pasture based livestock industry. Accurate measurement and allocation of fresh pasture to grazing herds on a daily basis is essential in increasing efficiency. Novel systems of measuring grass yield and quality were developed at the Moorepark Animal and Grassland Research Centre in Cork, Ireland, over the grass growing seasons of 2017 and 2018. Measurement systems included ground based and remote sensing techniques. The prototype GrassQ DSS was designed to process datasets uploaded from all proposed measurement systems. Measurement parameters were compressed sward height (CSH) (mm), herbage mass (HM) (kgDM/ha), dry matter (DM) (g/kg) and crude protein (CP) (g/kg). Ground based measurements were recorded using a smart rising plate meter (RPM) and lab based near infrared spectroscopy (NIRS). Multispectral remote sensing was carried out using an unmanned aerial vehicle (UAV), and data from the European Union’s Sentinel-2 satellite (S2). Reference analyses for all prediction models were carried out at Moorpark’s Grassland Laboratory and all sample locations were geotagged to enable spatial mapping of all parameters. The GrassQ prototype DSS is currently operational, including a number of preliminary grass quantity and quality prediction models. The complete Grass DSS will be is launched upon final validation.

    Item Type: Book Section
    Keywords: Decision Support System; Grassland Management; Precision Grazing; Rising Plate Meter; Remote Sensing; Near Infrared Spectroscopy;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 12793
    Identification Number: https://doi.org/10.13031/aim.201900769
    Depositing User: Tim McCarthy
    Date Deposited: 14 Apr 2020 15:05
    Publisher: ASABE
    Refereed: No
    URI:

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