The objective of plant phenotyping is ta advance plant science for breeding and crop management. Phenotyping platforms automate the measurements of traits from the cell ta the whole plant by using novel sensors and methods in bath controlled environment and in the field. Big data are produced in the form of alphanumeric matrices, images, statistical or 3D models that must ail properly be annotated with metadata, thus enabling meta-analyses and linkage ta the genotypes. However, comparison and interpretation of trait data across phenotyping sites and platforms is impeded by the heterogeneity of variables' names and methods of measurement. Therefore, Plant modelers cannot exchange data produced by models constructed with phenotyping data. To address this problem, an extended use of ontologies is proposed Reference Plant Ontologies (e.g. Plant Ontology, Phenotypic Quality Ontology) are being developed within the NSF-awarded Planteome Project (www.planteome.org), in collaboration with the Crop Ontology, ta increase the interoperability of the phenotyping data. Crop Ontology was developed in the framework of the Generation Challenges Programme, which aimed at identifying promising material for drought-resistance, and is used by the Integrated Breeding Platform (www.integratedbreeding.net). The Crop Ontology (www.cropontology.org ) provides harmonized breeders' trait names, measurement methods and scales for currently 18 crops aside terms for describing trials environmental and experimental conditions and will include management practices. As an example, the Australian Plant Phenomics Facility manages phenomics experiments with the Phenomics Ontology Driven Database (PODD). PODD stores versioned ontologies in the Web Ontology Language (OWL) alongside experimental layouts, data references, and analysed results ta provide semantic querying across a range of heterogeneous experiments.