The current rates of crop-yield increase are insufficient to meet the global food demands anticipated by 2050. While crop breeding has been one of the main pillars for securing yield progress in past decades, breeding programs are being confronted with new challenges due to the changing climate, particularly in areas where declining precipitation and increasing temperatures are more evident. Therefore, maximizing crop-yield potential and fortifying genetic resilience against abiotic and biotic stresses are critical to food security. High-throughput crop phenotyping is perceived as a key factor to accelerate crop genetic advance. Phenotyping not only facilitates conventional breeding, but it is necessary to fully exploit the capabilities of molecular breeding, and it can be exploited to predict breeding targets for the years ahead at the regional level through more advanced simulation models and decision support systems. In terms of phenotyping, it is necessary to determined which selection traits are relevant in each situation, and which phenotyping tools/ methods are the most suitable to assess such traits. Remote sensing methodologies are currently the most popular approaches, even when lab-based analyses are still relevant in many circumstances. On top of that, data processing and automation, together with machine learning/deep learning are contributing to a wide range of applications for phenotyping. This presentation will put emphasis on the most popular phenotyping approaches, including examples of phenotyping implementation for different traits and target environments.