This research aims to bridge the gap between plant genomic variations and phenotypic traits using hyperspectral imaging (HSI) and machine learning (ML) for high-throughput plant phenotyping. 
It involves 
(1)Accessing the genetic basis of plant traits by identifying genomic variations (e.g., SNPs, QTLs, GWAS) associated with these traits.
(2) Integrating multi-omics data combined with hyperspectral imaging data for plant trait prediction
(3) Develop advanced machine learning to understanding the link between genomic and hyperspectral -based phenotypic traits for automated plant phenotyping