Sorghum is a versatile cereal crop grown the central U.S. especially in Kansas and Texas and is an important grain for feed, biofuel, and food markets. The growing demand for sorghum has increased the need for enhanced hybrids with superior grain composition, such as high protein and starch content, which are key determinants of its nutritional and economic value. The composition of sorghum grain is highly influenced by genetics, growth conditions, and crop management practices, all of which influence the final yield and quality of the crop. To develop new varieties of sorghum with improved grain traits, grain composition must be measured on large sample sets grown at multiple locations and often with different crop practices, which is currently a labor-intensive and time-consuming process. Thus, this research investigated the ability of machine learning to predict plant growth features and grain composition collected by high throughput techniques and determine relationships between crop management practices and grain composition and ultimately end-use value. By identifying optimal variety-management combinations and leveraging non-invasive, high-throughput plant and grain analysis, this study offers a scalable framework for real-time decision-making and targeted field interventions to improve sorghum varieties.

This dataset contains grain composition determined by near-infrared spectroscopy used as part of this research project.