Webinar summary - Semantic annotation of images in the FAIR data era CGIAR Platform for Big Data in Agriculture

Digital agriculture increasingly relies on the generation of large quantity of images. These images are processed with machine learning techniques to speed up the identification of objects, their classification, visualization, and interpretation. However, images must comply with the FAIR principles to facilitate their access, reuse, and interoperability. As stated in recent paper authored by the Planteome team (Trigkakis et al, 2018), "Plant researchers could benefit greatly from a trained classification model that predicts image annotations with a high degree of accuracy." In this third Ontologies Community of Practice webinar, Justin Preece, Senior Faculty Research Assistant Oregon State University, presents the module developed by the Planteome project using the Bio-Image Semantic Query User Environment (BISQUE), an online image analysis and storage platform of Cyverse.