Deep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-the-art results for root and shoot feature identification and localisation. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority of (12 out of 14) manuallyidentified QTL were also discovered using our automated approach based on the deep learning detection to locate plant features. We predict a paradigm shift in image-based phenotyping bought\nabout by deep learning approaches.