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

Abstract

An early and precise yield estimation in intensive managed grassland is mandatory for economic management decisions. RGB (red, green, blue) cameras attached on an unmanned aerial vehicle (UAV) represent a promising non-destructive technology for the assessment of crop traits especially in large and remote areas. Photogrammetric structure from motion (SfM) processing of the UAV-based images into point clouds can be used to generate 3D spatial information about the canopy height (CH). The aim of this study was the development of prediction models for dry matter yield (DMY) in temperate grassland based on CH data generated by UAV RGB imaging over a whole growing season including four cuts. The multi-temporal study compared the remote sensing technique with two conventional methods, i.e., destructive biomass sampling and ruler height measurements in two legume-grass mixtures with red clover (Trifolium pratense L.) and lucerne (Medicago sativa L.) in combination with Italian ryegrass (Lolium multiflorum Lam.). To cover the full range of legume contribution occurring in a practical grassland, pure stands of legumes and grasses contained in each mixture were also investigated. The results showed, that yield prediction by SfM-based UAV RGB imaging provided similar accuracies across all treatments (R 2 = 0.59-0.81) as the ruler height measurements (R 2 = 0.58-0.78). Furthermore, results of yield prediction by UAV RGB imaging demonstrated an improved robustness when an increased CH variability occurred due to extreme weather conditions. It became apparent that morphological characteristics of clover-based canopies (R 2 = 0.75) allow a better remotely sensed prediction of total annual yield than for lucerne-grass mixtures (R 2 = 0.64), and that these crop-specific models cannot be easily transferred to other grassland types.Destructive biomass sampling is considered to be the most accurate yield estimation method but can also be considered as the most labor-intensive method [4]. Another approach for estimating biomass in grasslands is the assessment of canopy height (CH), which was frequently found to be positively correlated with crop biomass [5,6]. Traditional height measurements in grassland are often conducted with a rising plate meter, determining the compressed sward height, or with a ruler stick [7,8]. Furthermore, several portable technical devices for non-destructive biomass estimation were developed in the recent years, which so far were not widely distributed in agricultural practice, e.g., leaf area meter to asses leaf area index (LAI) [9], electronic capacitance meter, which measures the difference of capacitance between air and biomass [1] and a reflectometer, which measures intensity of spectral reflectance by light emitting diodes (LED) [10]. Biomass sampling, manual height measurement and the above mentioned technical devices need a substantial number of repetitions in combination with a spatially uniform distributions of the measurements to generate a reliable yield estimation [11]. Therefore, much time an...

Development and Validation of a Model to Combine NDVI and Plant Height for High-Throughput Phenotyping of Herbage Yield in a Perennial Ryegrass Breeding Program

“…Plant height is another widely used structural and physical parameter used to estimate the biomass of various crops [20,23,53,54]. Previous studies on grasslands indicated moderate to high correlation (R 2 = 0.59-0.81) on predicting dry herbage yield from plant height measurements [45]. In the current study, the R 2 of plant height vs. log-transformed DHY was more variable (R 2 = 0.12-0.65) than NDVI across seasons.…”

“…The seasonal HY distribution in the 2017 and 2018 showed that HY from the 2017 harvest dates was higher compared to 2018 in all seasons. The notable decline in dry matter yield as the year progressed may result from the drought stress and repetitive cutting [45].…”

The Benefit of Spectral and Point-Cloud Data for Herbage Yield and Quality Assessment of Grasslands

“…> 1ha) and the spatial coverage is limited, TLS approaches are not appropriate for large-scale estimation of grassland yield. To derive point clouds, photogrammetric approaches based on numerous photographs from different viewing angles might be an alternative (Wijesingha et al 2019, Grüner et al 2019, Forsmoo et al 2018, Viljanen et al 2018, Bareth, Schellberg 2018 to TLS measurements. While point clouds created from TLS have a deeper penetration into the grassland canopy and can also be affected by single plant parts sticking out (e.g.…”

“…height measurements or the rising plate meter) can also be affected by the canopy structure and are not applicable on large fields. To compensate for this effect, SfM approaches have been proven to deliver equal or even better yield estimations than traditional field methods (Grüner et al 2019, Bareth, Schellberg 2018. For example, Grüner et al 2019 showed an improved prediction quality (i.e.…”

“…To compensate for this effect, SfM approaches have been proven to deliver equal or even better yield estimations than traditional field methods (Grüner et al 2019, Bareth, Schellberg 2018. For example, Grüner et al 2019 showed an improved prediction quality (i.e. lower relative prediction error) for a SfM-based DMY prediction for a clover-grass mixture (13 %) than predictions based on a large number of height measurements (rRMSEP = 19 %).…”