@article{dobbs_goldsmith_ginn_skovsen_bagavathiannan_mirsky_reberg-horton_leon_2024, title={Mapping predicted biomass in cereal rye using 3D imaging and geostatistics}, volume={10}, ISSN={["1550-2759"]}, url={https://doi.org/10.1017/wsc.2024.62}, DOI={10.1017/wsc.2024.62}, abstractNote={Abstract Cover crops are becoming an increasingly important tool for weed suppression. Biomass production in cover crops is one of the most important predictors of weed suppressive ability. A significant challenge for growers is that cover crop growth can be patchy within fields, making biomass estimation difficult. This study tested ground-based structure-from-motion (SfM) for estimating and mapping cereal rye ( Secale cereale L.) biomass. SfM generated 3D point clouds from red, green, and blue (RGB) videos collected by a handheld GoPro camera over five fields in North Carolina during the 2022 to 2023 winter season. A model for predicting biomass was generated by relating measured biomass at termination using a density–height index (DH) from point cloud pixel density multiplied by crop height. Overall biomass ranged from 320 to 9,200 kg ha −1 , and crop height ranged from 10 to 120 cm. Measured biomass at termination was linearly related to DH (r 2 = 0.813) through levels of 9,000 kg ha −1 . Based on independent data validation, predicted biomass and measured biomass were linearly related (r 2 = 0.713). In the field maps generated by kriging, measured biomass data were autocorrelated at a range of 5.4 to 42.2 m, and predicted biomass data were autocorrelated at a range of 3.4 to 12.0 m. However, the spatial arrangement of high- and low-performing areas was similar for predicted and measured biomass, particularly in fields with greatest patchiness and spatial correlation in biomass values. This study provides proof-of-concept that ground-based SfM can potentially be used to nondestructively estimate and map cover crop biomass production and identify low-performing areas at higher risk for weed pressure and escapes.}, journal={WEED SCIENCE}, author={Dobbs, April M. and Goldsmith, Avi S. and Ginn, Daniel and Skovsen, Soren Kelstrup and Bagavathiannan, Muthukumar V. and Mirsky, Steven B. and Reberg-Horton, Chris S. and Leon, Ramon G.}, year={2024}, month={Oct} }