Predicting the fitness consequences of mutations, and their concomitant impacts on molecular and cellular function as well as organismal phenotypes, is an important challenge in biology that has new relevance in an era when both functional and population-genomic data are readily available. The ability to construct genomewide maps of fitness consequences in plant genomes is a recent development that has potential implications for our ability to predict the fitness effects of mutations and discover functional elements, especially in the non-coding regions of the genome. We present a method developed recently for the human genome that we are applying to plant genomes. This approach combines population genomics and divergence data to estimate the probability of fitness effects of mutations in classes of sites defined by a common function (e.g. TFBSs) by integrating intra-specific polymorphisms and between-species divergence data with functional genomic information. Its foundation relies on a statistical method called Inference Natural Selection from Interspersed Genomically coHerent elemenTs (INSIGHT), which is conceptually similar to population genetics methods that use patterns of polymorphism and divergence to identify departures from neutral expectations. The contrast between polymorphism and divergence is a powerful approach to inferring recent selection and the INSIGHT approach to pooling dispersed sites enables the characterization of noncoding elements that may have been subject to recent selection. The adaptation of this approach to plant genomes has confirmed major differences by which we can functionally partition the genomes of Arabidopsis thaliana and Oryza sativa. Maps of fitness consequences in plants, combined with traditional genetic approaches, could accelerate discovery of functional elements such as regulatory sequences in non-coding DNA and genetic polymorphisms associated with key traits, including agronomically-important traits.