Current assessment of non-destructive yield in forage breeding programs relies largely on the visual assessment by experts, who would categorize biomass to a discrete scale. Visual assessment of biomass yield has inherent pitfalls as it can generate bias between experimental repeats and between different experts. Visual assessment is also time-consuming and would be impractical on large-scale field trials. A method has been established to allow for a rapid, non-destructive assessment of biomass yield of forages using aerial based multi-spectral imaging technologies. This method uses aerial surveillance platforms, including a 3DR Solo with Parrot Sequoia sensor and DJI S1000+ with a Tetracam MCA-12 sensor, to take weekly images of a field trial that consists of a global perennial ryegrass reference population of 270 000 individual plants from a 1300 varieties/breeding lines. Multispectral images are processed through Pix4D to create a georeferenced ortho-mosaic image with an average ground sampling distance (GSD) below 2cm. Fifteen ground control points (GCPs) are located across the site and georeferenced with an RTK-GNSS receiver. This allows for accurate and repeatable georeferencing of the ortho-mosaic images. Plant vegetation indices are extracted from the ortho-mosaic image as a point *.*shp file for further processing in QGIS software.  Plant indices are then calculated and processed for single plants, rows or plots based on user-defined georeferenced areas in QGIS software allowing for the quantitative measurements of various vegetation indices. Initial assessment of the correlations between actual biomass yield and NDVI, in spaced-plant and sward ryegrass field trials, have shown significant positive correlations with a correlation coefficient up to 0.94. This screening technique has resulted in a 2000 fold reduction in staff hours for the non-destructive estimation of biomass yield of single plants in this perennial ryegrass field trial.