Unlocking the potential of plant phenotyping data through integration and data-driven approaches

Abstract

Plant phenotyping has emerged as a comprehensive field of research as the result of significant advancements in the application of imaging sensors for high-throughput data collection. The flip side is the risk of drowning in the massive amounts of data generated by automated phenotyping systems. Currently, the major challenge lies in data management, on the level of data annotation and proper metadata collection, and in progressing towards synergism across data collection and analyses. Progress in data analyses includes efforts towards the integration of phenotypic and -omics data resources for bridging the phenotype-genotype gap and obtaining in-depth insights into fundamental plant processes.

Code and data availability

This is a review article on plant phenotyping data management and integration. It describes no paper-specific phenotype datasets, images, analysis code, or models, and contains no availability/deposit statements for the authors' own data or code. All mentioned resources (PIPPA, PlantCV, IAP, AraPheno, etc.) are cited,

No evidence-backed public reproduction asset is currently recorded.