Sorghum is an important grain crop in the central plains of the United States and is used for feed, fuel, and food. Sorghum has a wide degree of genetic and phenotypic diversity which can be exploited to improve the agronomic performance and end-use quality and value of the crop. Grain yield is a primary trait that the sorghum breeding industry is working on improving as increased yields directly relate to the value and productivity. Therefore, to take advantage of the genetic diversity of sorghum and develop new lines with improved yield, methods for rapidly determining and predicting yield are necessary. This research evaluated the use of deep learning algorithms to predict yield from images of sorghum and found that yield could be forecast using deep learning processing of images.

Data includes physical measurements of individual sorghum kernel length and thickness (diameter) used as "ground truth" measurements to help verify seed size measurements determined from bulk grain image analysis.