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

Abstract

Easy-to-capture and robust plant status indicators are important factors when implementing precision agriculture techniques on fields. In this study, aerial red, green and blue color space (RGB) photography and near-infrared (NIR) photography was performed on an experimental field site with nine different cover crops. A lightweight unmanned aerial system (UAS) served as platform, consumer cameras as sensors. Photos were photogrammetrically processed to orthophotos and digital surface models (DSMs). In a first validation step, the spatial precision of RGB orthophotos (x and y, ± 0.1 m) and DSMs (z, ± 0.1 m) was determined. Then, canopy cover (CC), plant height (PH), normalized differenced vegetation index (NDVI), red edge inflection point (REIP), and green red vegetation index (GRVI) were extracted. In a second validation step, the PHs derived from the DSMs were compared with ground truth ruler measurements. A strong linear relationship was observed (R 2 = 0.80-0.84). Finally, destructive biomass samples were taken and compared with the remotely-sensed characteristics. Biomass correlated best with plant height (PH), and good approximations with linear regressions were found (R 2 = 0.74 for four selected species, R 2 = 0.58 for all nine species). CC and the vegetation indices (VIs) showed less significant and less strong overall correlations, but performed well for certain species. It is therefore evident that the use of DSM-based PHs provides a feasible approach to a species-independent non-destructive biomass determination, where the performance of VIs is more species-dependent.

Biomass Prediction of Heterogeneous Temperate Grasslands Using an SfM Approach Based on UAV Imaging

“…Moeckel et al [20] achieved substantially higher R 2 values with stands of tomato, eggplant and cabbage (R 2 = 0.89-0.97), which represent more heterogeneous crops. Roth and Streit [32] examined different cover crops, including also two clover species, and achieved an R 2 of 0.58. When plants, which were growing close to the ground or even lodging, were excluded from the regression model, R 2 increased to 0.74.…”

Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery

“…The manned airborne platforms can collect data at higher temporal and spatial resolutions, but their operational complexity and cost hinder the frequent acquisition of multi-angular data for crop monitoring. With the advent of unmanned aerial vehicles (UAVs), aerial remote sensing systems become increasingly available for crop monitoring because of their flexibility, low cost, and ease of operation (Hunt et al, 2010;Roth and Streit, 2017;Zheng et al, 2018a;Lu et al, 2019).…”

BLOB-Based AOMs: A Method for the Extraction of Crop Data from Aerial Images of Cotton

“…Aerial images of crops provide a useful perspective regarding identifying and quantifying crop uniformity [6,11,12]. Recent improvements in tools for aerial imaging in an agricultural setting have led to the increased use of Unmanned Aerial Systems (UASs) in research and production environments [13][14][15][16][17][18][19][20][21]. UAS measurements make it possible to view not only fields and plots, but individual plants [13,14] and individual components of single plants [15][16][17].…”

“…UAS measurements make it possible to view not only fields and plots, but individual plants [13,14] and individual components of single plants [15][16][17]. This improved measurement efficiency presents challenges in terms of the amount of data that can be collected and perhaps, most importantly, how these data streams can be analyzed in an agriculturally relevant setting [18][19][20][21].…”