Plant phenomics has emerged as a critical bridge between genotype and phenotype, addressing a significant bottleneck in crop breeding and functional genomics studies. Hyperspectral imaging, a key technology in this field, has been instrumental in high-throughput, non-destructive phenotyping. Compared to other imaging technologies, hyperspectral imaging stands out for its continuous and fine spectral resolution, capturing subtle changes in plant biochemical and physiological states, which is essential for precise identification and analysis of plant characteristics. Recent advances in deep learning have further expedited hyperspectral data analysis, fostered multi-omics research and enhanced our ability to integrate diverse datasets. Despite challenges in establishing standards of data acquisition and processing, a significant proposal has emerged for the scientific community to collaboratively build a vast hyperspectral database. Integrated with reducing the cost of hyperspectral sensors and promoting more open-source analysis pipelines for hyperspectral data, these initiatives promise to lay the groundwork for robust big data analytics, potentially revolutionising plant research and breeding.
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