Tackling the hyperspectral data challenge Unlike traditional cameras that capture only red, green, and blue color channels, hyperspectral imaging systems record hundreds of wavelengths of light. This drawback tends to limit the size and complexity of AI models that can be deployed. Scientists demonstrated the new method using hyperspectral plant data from ORNL’s Advanced Plant Phenotyping Laboratory (APPL) and weather datasets on Frontier, the world’s first exascale supercomputer at the Oak Ridge Leadership Computing Facility. By reducing memory usage, AI training tasks can run with fewer computing resources, broadening access to high-performance plant science tools. Using these models, scientists can measure traits such as photosynthetic activity directly from images, replacing slow, labor-intensive manual measurements.