Biomass and LiDAR data from wheat and triticale plots grown at Yanco (NSW) in 2019 to improve prediction of digital biomass

Current increases in crop yield may not meet future demands for food in fibre. Crop biomass has been proposed as an effective target for yield increase in temperate crops like wheat. Although measurements of crop biomass are predictors of plant growth and yield, traditional measurements are labour intensive, costly, and not amenable for large screens in breeding programs. For example, a plot section is manually cut, and the tissue is dried until constant weight in an electric oven. The resulting measured dry weight per area is compared among hundreds or thousands of lines to find "winning" varieties. A new, faster and non-invasive approach is to use the information stored in a point cloud from a LiDAR sensor of cereal plot and predict biomass using different machine learning algorithms. However, the models need to be fed with an actual, ground-truth dry weight of aboveground biomass data. The current dataset is a collection of dry weight data from a field trial specifically designed to encompass variation in canopy architecture, biomass and height to feed into models to predict biomass using LiDAR. This variation was achieved by using different varieties of two crops, triticale and wheat, sown at two different rates. Moreover, LiDAR and dry weight measurements were performed at two different developmental stages to enlarge the "trait space".