In an age of readily and cheaply available genotype data, wheat (Triticum aestivum) breeders continue to be challenged by the collection of accompanying phenotype data; which remains subjective, costly and time consuming to obtain. In recent years many sensor technologies and platforms have been proposed to address the current genotype-phenotype gap within wheat breeding programmes, though reports detailing the application and value of such technologies within breeding programmes remain sparse. Towards addressing this, a high-throughput tractor mounted infrastructure was deployed within Australia’s largest commercial wheat breeding programme, collecting RGB images and 3D point cloud data at throughputs of 7,600 and 2,400 plots per hour, respectively. Images and point cloud data were processed into objective measurements for a range of traits including canopy cover, senescence, canopy height and above ground biomass. Digital measurements correlated strongly to ground-truth measurements (canopy cover, r = 0.88; senescence, r = 0.75; canopy height, r = 0.94 ; above ground biomass, r = 0.86), with raw, genetic and residual correlations being investigated. In general, trait heritability calculated from digital measurements was greater than, or similar to, groundtruth measurements, showing strong potential for digital measurements to be used within breeding programmes. Deployment of this system within a functional breeding programme demonstrates how current and future sensor technologies can be adopted and utilised by breeders, with minimal disruption to current field practices. Further to this, the challenges of collecting, storing and processing such large amounts of phenotypic data have been identified, as well as opportunities to expand upon current data processing techniques; for example, utilizing images taken at flowering for head counting.