Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: an applied photogrammetric approach

By Lukas Roth, Bernhard Streit. Precision Agriculture, 2017

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Multi-Temporal Site-Specific Weed Control of Cirsium arvense (L.) Scop. and Rumex crispus L. in Maize and Sugar Beet Using Unmanned Aerial Vehicle Based Mapping (2018, background): “The resulting NDVI raster object was used as a mask layer as proposed by Liebisch et al. [31] and demonstrated in Roth and Streit [32] on the relative RGB image.”
Extracting leaf area index using viewing geometry effects—A new perspective on high-resolution unmanned aerial system photography (2018, methodology): “Workflow steps315 and parameter settings were identical to the one used in Roth and Streit (2018).”
High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms (2020, unknown): “RGB cameras have been widely applied to evaluate crop phenotyping traits in the field to provide measurements at an affordable price [46], [47], [162], [164], [258].”
Unmanned Aircraft System (UAS) Technology and Applications in Agriculture (2019, methodology): “The other main use of RGB imagery is for biomass estimation, where NIR and multispectral cameras are often used in conjunction with RGB cameras in order to improve accuracy [6,23].”