Lidar and RGB Image Analysis to Predict Hairy Vetch Biomass in Breeding Nurseries. Issue 1 (1st August 2019) - Wiering, Nicholas P.; Ehlke, Nancy J.; Sheaffer, Craig C.
Abstract : Core Ideas: Early‐season biomass is conventionally phenotyped by subjective visual estimates. RGB image data are highly predictive of biomass in hairy vetch breeding plots. Lidar and RGB image data can be combined to accurately predict sward biomass. Remote sensing could increase genetic gain potential for biomass in cover crops. Hairy vetch ( Vicia villosa Roth) is an annual legume grown as a forage and cover crop. To improve cover crop function, traits such as biomass production are of high interest for cover crop breeders. However, direct phenotypic methods for biomass production are destructive. Breeders have thus relied on subjective, visual scoring of biomass, which is generally correlative but not quantitative or absolute. We evaluated two low‐cost remote sensing tools, lidar and red–green–blue (RGB) image analysis, for their potential to predict biomass in vivo. We evaluated these tools in two common forage breeding scenarios, spaced‐plant and sward‐plot nurseries, at three Minnesota locations following the winter of 2016–2017. Ground cover from RGB image binarization had a significant and linear relationship with aboveground biomass in spaced plants ( R 2 = 0.93) and sward plots ( R 2 = 0.89). However, once the image area from sward plots became saturated with vegetative pixels, a near‐exponential relationship would occur. Because of the prostrate growth habit of hairy vetch, RGB image analysis was more appropriate at lower plant densities, such as spaced‐plant nurseries. Conversely, the dimensionality of lidar sensing gave it greater predictive ability at higher plant densities where RGB analysis could not detect vertical increases in biomass. Lidar measures of sward‐plot height were also linearly and strongly related to dry‐matter biomass in sward plots ( R 2 = 0.80). When we combined RGB and lidar data to predict sward‐plot biomass in a multiple mixed‐effect regression model, we were able to explain more biomass variation than with the use of either phenotypic tool as a single predictor ( R 2 = 0.94).