AUTHOR=Grüner Esther , Astor Thomas , Wachendorf Michael 

TITLE=Prediction of Biomass and N Fixation of Legume–Grass Mixtures Using Sensor Fusion

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 11 - 2020

YEAR=2021

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2020.603921

DOI=10.3389/fpls.2020.603921

ISSN=1664-462X

ABSTRACT=European farmers and especially organic farmers rely on legume-grass mixtures in their crop rotation as an organic nitrogen (N) source, as legumes can fix atmospheric N, which is the most important element for plant growth. Furthermore, legume-grass serves as valuable fodder for livestock and biogas plants. Therefore, information about aboveground biomass and N fixation are crucial for efficient farm management decisions on field level. Remote sensing, as a non-destructive and fast technique, provides different methods to quantify plant trait parameters. In our study high density point clouds, derived from terrestrial laser scanning (TLS), in combination with unmanned aerial vehicle (UAV)-based multispectral (MS) data were collected to receive information about three plant trait parameter (fresh and dry matter, nitrogen fixation) infor two legume-grass mixtures. terrestrial laser scanning (TLS) for high density point clouds for crop surface height (CSH) measurements in combination with unmanned aerial vehicle (UAV)-based multispectral (MS) data were carried out. Several crop surface height (CSH) metrics based on TLS and vegetation indices (VIs) based on the four MS bands (green, red, red edge, near infrared) were calculated. Furthermore, eight texture features based on mean CSH and the four MS bands were generated to measure horizontal spatial heterogeneity. The aim of this multi-temporal study over two vegetation periods was to create estimation models based on biomass and N fixation for two legume-grass mixtures by sensor fusion, a combination of both sensors. To represent conditions in practical farming, e. g. varying proportion of legumes, the experiment included pure stands of legume and grass of the mixtures. Sensor fusion of TLS and MS data was found to provide better estimates of biomass and NFix then separate data analysis. The study shows the important role of texture based on MS and TLS point cloud data, which contributed greatly to the estimation model generation. The applied approach offers an interesting method for improvements in precision agriculture.