Time-series image data for the sorghum association panel collected in UNL-GIC in 2017

This dataset includes RGB and hyperspectral sorghum image datasets collected at multiple time points at UNL-GIC. The RGB images are in the normal png format and the hyperspectral image cubes have been converted to a 3d python numpy array file. Researchers may try to extract many of plant morphological traits using general image processing approaches or machine learning based techniques. In the corresponding publication, we measured the plant height in RGB images and conducted the semantic segmentation to measure more plant morphological traits using different machine learning algorithms in hyperspectral images.