Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities

Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection. This perspective paper synthesizes current advances, identifies major barriers, and proposes future directions to realize the transformative potential of AI‐enabled plant phenomics. We first provide an overview of AI technologies with the potential to address key challenges in phenomics, from data collection to phenotypic trait extraction and environmental sensing. We then present three case studies focusing on specialty crops (blueberry [ Vaccinium corymbosum L.] mechanical harvestability traits, strawberry [ Fragaria × ananassa (Duchesne ex Weston)] production, and citrus [ Citrus L.] disease) to illustrate practical applications of AI‐driven phenomics. Moreover, we highlight future perspectives and opportunities for further research and innovation. These include large foundation models, real‐time inference on edge devices, explainable AI, generative AI and digital twins, AI‐enhanced multi‐omics, agentic AI, and knowledge‐guided and data‐driven hybrid approaches. Finally, we discuss key challenges and limitations of applying AI to plant phenomics, including data curation, model generalization and bias, and ethical considerations related to equitable access to AI tools.