High?throughput crop phenotyping, particularlyunderfield conditions, is nowadays perceivedas a key factor limiting crop genetic advance.Phenotyping not only facilitates conventionalbreeding, but it is necessary to fully exploit thecapabilities of molecular breeding, and it can beexploited to predict breeding targets for the yearsahead at the regional level through more ad-vanced simulation models and decision supportsystems. In terms of phenotyping, it is necessaryto determined which selection traits are relevantin each situation, and which phenotyping tools/methods are available to assess such traits. Re-mote sensing methodologies are currently themost popular approaches, even when lab?basedanalyses are still relevant in many circumstances.On top of that, data processing and automation,together with machine learning/deep learning arecontributing to the wide range of applications forphenotyping. This reviewaddresses spectral andred?green?blue sensing as the most popular re-mote sensing approaches, alongside stable iso-tope composition as an example of a lab?basedtool, and root phenotyping, which represents oneof the frontiers forfield phenotyping. Further, weconsider the two most promising forms of aerialplatforms (unmanned aerial vehicle and satellites)and some of the emerging data?processingtechniques. The review includes three Boxesthat examine specificcasestudies.Universidad de Ibagu?