Food security is currently one of the major challenges that we are facing as a species. Understanding plant responses and adaptations to limited water availability is key to maintain or improve crop yield, and this is even more critical considering the different projections of climate change. In this work, we combined two high-throughput -'omic' platforms ('phenomics' and 'ionomics') to begin dissecting time-dependent effects of water limitation in Arabidopsis leaves and ultimately seed yield. As proof of concept, we acquired high-resolution images with visible, fluorescence, and near infrared cameras and used commercial and open source algorithms to extract the information contained in those images. At a defined point, samples were also taken for elemental profiling. Our results show that growth, biomass and photosynthetic efficiency were affected mostly under severe water limitation regimes and these differences were exacerbated at later developmental stages. The elemental composition and seed yield, however, changed across the different water regimes tested and these changes included under- and over- accumulation of elements compared with well-watered plants. Our results demonstrate that the combination of phenotyping techniques can be successfully used to identify specific bottlenecks during plant development that could compromise biomass, yield, and the nutritional quality of plants.