World’s first exascale supercomputer speeds plant research with new AI

Scientists at Oak Ridge National Laboratory (ORNL) have developed a novel computational method called Distributed Cross-Channel Hierarchical Aggregation (D-CHAG) that significantly enhances the processing of complex hyperspectral plant imaging data. This approach doubles the analysis speed while reducing memory usage by 75%, overcoming a major bottleneck in handling the vast data generated by hyperspectral imaging systems, which capture hundreds of light wavelengths to reveal detailed information about plant health and stress. By distributing workloads across multiple GPUs and employing a staged, hierarchical aggregation of spectral data, D-CHAG enables faster AI training on larger models without sacrificing image resolution or biological detail. The breakthrough was demonstrated using plant data from ORNL’s Advanced Plant Phenotyping Laboratory and weather datasets on Frontier, the world’s first exascale supercomputer. This advancement allows AI models to measure plant traits such as photosynthetic activity directly from images, replacing slow manual methods and accelerating crop innovation. The technology supports DOE initiatives like the Genesis Mission and OPAL, which