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.