Soil monitoring for precision farming using hyperspectral remote sensing and soil sensors

This work describes an approach to calculate pedological parameter maps using hyperspectral remote sensing and soil sensors. These maps serve as information basis for automated and precise agricultural treatments by tractors and field robots. Soil samples are recorded by a handheld hyperspectral sensor and analyzed in the laboratory for pedological parameters. The transfer of the correlation between these two data sets to aerial hyperspectral images leads to 2D-parameter maps of the soil surface. Additionally, rod-like soil sensors provide local 3D-information of pedological parameters under the soil surface. The goal is to combine the area-covering 2D-parameter maps with the local 3D-information to extrapolate large-scale 3D-parameter maps using AI approaches.

Automated summary

This study utilizes hyperspectral remote sensing and soil sensor technology, along with laboratory analysis of soil samples, to generate pedological parameter maps for precise agricultural operations by machinery and robots. The goal is to combine 2D parameter maps with local 3D soil parameter information to extrapolate large-scale 3D parameter maps using artificial intelligence methods.