Imaging-based phenotyping in leafy greens: a scoping review of progress, cross-domain insights, and emerging challenges

Imaging-based phenotyping has emerged as a key technology for the non-destructive characterization of plants by enabling the quantitative assessment of morphological, physiological, biochemical, and genetic traits. This review aims to map the research landscape of imaging-based phenotyping in leafy greens, synthesize advances across major application domains, identify current research gaps, and propose future directions for the field. A scoping review was conducted following the PRISMA-ScR guidelines and the PCC. Literature searches of the Scopus and Web of Science databases identified 203 records, of which 59 research articles met the inclusion criteria and were included in the final synthesis. The review reveals that research is dominated by lettuce, with greenhouse and open-field experiments serving as the primary research environments, while growth chambers, controlled-environment agriculture, and laboratory settings continue to play important roles in methodological development. Synthesis across four major application domains reveals diverse approaches to spatial plant representation, including two- and three-dimensional methods, alongside longitudinal and high-frequency monitoring and the non-destructive estimation of various biological traits. However, these observational capabilities are not consistently accompanied by comparable capabilities for biological interpretation, cross-domain integration, or the translation of phenotypic information into operational decision-making. Collectively, these findings indicate that major challenges in imaging-based phenotyping include biological interpretation, cross-domain integration, and operational translation, alongside persistent challenges in data acquisition and validation. Future research should therefore move beyond accurate trait measurement toward biologically realistic, explainable, and integrated phenotyping systems capable of supporting next-generation precision horticulture.