Plants around the globe produce a wide variety of specialized metabolites that play key roles in communication and defense. Recently, evidence has been accumulating that (like in microbes) the genes encoding the biosynthetic pathways towards these metabolites are often densely clustered in specific genomic loci: biosynthetic gene clusters (BGCs). This offers great potential for genome-based discovery of plant natural products. However, effective computational tools to identify and analyze plant BGCs have thus far been lacking. Here, we introduce plantiSMASH, a versatile online analysis platform that automates the identification of candidate plant BGCs, as well as their comparative genomic and transcriptomic analysis. The cluster detection logic, validated on a set of all plant BGCs that have been experimentally characterized thus far, is able to pinpoint many complex metabolic loci across the Plant Kingdom. Additionally, interactively visualized coexpression analysis and comparative cluster-cluster alignment allow users to judge multiple sources of evidence for a candidate BGC to encode a group of enzymes that truly functions jointly in a biosynthetic pathway. Furthermore, plantiSMASH finds coexpression correlations between candidate BGCs and genes elsewhere in the genome. Altogether, this new software provides a comprehensive toolkit for plant geneticists to further explore the nature of gene clustering in plant metabolism. Moreover, spurred by the continuing decrease in costs of plant genome sequencing and assembly, it will soon allow natural product chemists to apply genome mining technologies to the discovery of novel medicinal compounds from a wide range of plant species.