Monitoring Forage Mass with Low-Cost UAV Data: Case Study at the Rengen Grassland Experiment

Abstract

Monitoring and predicting above ground biomass yield of grasslands are of key importance for grassland management. Established manual methods such as clipping or rising plate meter measurements provide accurate estimates of forage yield, but are time consuming and labor intensive, and do not provide spatially continuous data as required for precision agriculture applications. Therefore, the main objective of this study is to investigate the potential of sward height metrics derived from low-cost unmanned aerial vehicle-based image data to predict forage yield. The study was conducted over a period of 3 consecutive years (2014–2016) at the Rengen Grassland Experiment (RGE) in Germany. The RGE was established in 1941 and is since then under the same management regime of five treatments in a random block design and two harvest cuts per year. For UAV-based image acquisition, a DJI Phantom 2 with a mounted Canon Powershot S110 was used as a low-cost aerial imaging system. The data were investigated at different levels (e.g., harvest date-specific, year-specific, and plant community-specific). A pooled data model resulted in an R2 of 0.65 with a RMSE of 956.57 kg ha−1, although cut-specific or date-specific models yielded better results. In general, the UAV-based metrics outperformed the traditional rising plate meter measurements, but was affected by the timing of the harvest cut and plant community.

Upscaling and Validation of RTK-Direct Georeferenced Uav-Based RGB Image Data With Planet Imagery Using Polygon Grids for Pasture Monitoring

“…A x,y-RMSE of approx. 5 cm for direct georeferencing using a base station is satisfying because the goal is to use the UAV derived data in spatial resolutions of 0.5 to 10 m (Bareth 2020;Bareth and Hütt 2020).…”

“…Further analysis should be conducted using different satellite data. However, further analysis should also include sward height analysis, which is a robust estimator of forage mass (Lussem et al 2020) and would enable combined analysis of spectral and structural analysis workflows like the one introduced by Viljanen et al (2018).…”

Precision Agriculture for Crop and Livestock Farming—Brief Review

“…Traditional methods of measuring grass yield include destructive grass sampling coupled with weighing systems on mowers, trailers, or balers [ 38 ]. Optical sensing techniques have recently evolved and, according to Schaefer and Lamb [ 39 ], it is now possible to estimate herbage biomass levels by combining a measure of pasture canopy height using light detection and ranging (LiDAR) and pasture canopy reflectance measurements using active optical sensors.…”

“…Current developments in unmanned aerial vehicle-based (UAV) sensing systems allow for the acquisition of image data at a ultrahigh spatial resolution for important stages of plant phenological growth [ 11 , 38 ]. Furthermore, new photogrammetric software products support the analysis of such image data to produce 3D point clouds and digital surface models (DSMs).…”

“…According to the authors, dry biomass yield can be predicted using sward height derived from multi-temporal DSMs derived from low-cost UAV-based imaging with consistent results over three years. The on-going miniaturization and cost efficiency of sensors and platforms as well as powerful algorithms and computer hardware can open new paths to sustainable grassland management [ 38 ]. In addition, research by Oliveira et al [ 41 ] showed that technology drone-based spectral remote sensing and photogrammetry could be used to predict the yield with an adequate degree of accuracy and to study the spatial differences within a field and between different fields, thus indicating great potential for remote sensing and drones to support the management of silage production for animal feeding [ 41 ].…”