Evaluation of RGB-Based Vegetation Indices From Uav Imagery to Estimate Forage Yield in Grassland

Abstract

ABSTRACT:Monitoring forage yield throughout the growing season is of key importance to support management decisions on grasslands/pastures. Especially on intensely managed grasslands, where nitrogen fertilizer and/or manure are applied regularly, precision agriculture applications are beneficial to support sustainable, site-specific management decisions on fertilizer treatment, grazing management and yield forecasting to mitigate potential negative impacts. To support these management decisions, timely and accurate information is needed on plant parameters (e.g. forage yield) with a high spatial and temporal resolution. However, in highly heterogeneous plant communities such as grasslands, assessing their in-field variability non-destructively to determine e.g. adequate fertilizer application still remains challenging. Especially biomass/yield estimation, as an important parameter in assessing grassland quality and quantity, is rather laborious. Forage yield (dry or fresh matter) is mostly measured manually with rising plate meters (RPM) or ultrasonic sensors (handheld or mounted on vehicles). Thus the in-field variability cannot be assessed for the entire field or only with potential disturbances. Using unmanned aerial vehicles (UAV) equipped with consumer grade RGB cameras in-field variability can be assessed by computing RGB-based vegetation indices. In this contribution we want to test and evaluate the robustness of RGB-based vegetation indices to estimate dry matter forage yield on a recently established experimental grassland site in Germany. Furthermore, the RGBbased VIs are compared to indices computed from the Yara N-Sensor. The results show a good correlation of forage yield with RGBbased VIs such as the NGRDI with R 2 values of 0.62.

Ultra-High Spatial Resolution Uav-Based Imagery to Predict Biomass in Temperate Grasslands

“…The PPR can distinguish the first (0 kg N/ha) and second (50 kg N/ha) treatment, although it fails to distinguish between the third (100 kg N/ha) and fourth (150 kg N/ha) treatment, similarly to the NGRDI and NDVI. The performance of the NGRDI was similar to the results reported by Lussem et al (2018) on the same test site, but for a different year. Hunt et al (2005) reported a good correlation of the NGRDI to alfalfa, corn and soybean biomass, but observed saturation effects for higher biomass yield, which is similar in this study and can be related to Motohka et al (2010), who observed that RGB-based VIs are limited to certain growing stages.…”

“…Bendig et al 2015, Hunt et al 2005, Jannoura et al 2015. Recent studies deployed UAV-based multispectral or RGB cameras to assess grassland biomass with good results (Lussem et al, 2018;Viljanen et al, 2018). In this contribution we want to evaluate simultaneously acquired RGB-based vegetation indices from a consumer-grade RGBcamera and a well-calibrated narrow-band multispectral camera to estimate dry biomass yield on an experimental grassland field in Germany.…”

Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

“…Color indices are acquired through algebraic calculation with reflectance values from the R (red), G (green), and B (blue) bands, respectively. While color indices can be used to segment plants from overall image as mentioned above, the main value of color indices is in facilitating the prediction or estimation of biophysical properties of target plants, such as biomass, leaf area index, and yield [67][68][69]. RGB digital cameras have advantages in their high resolution and low price, but the sensors also have limitations in overlapping the red, green, and blue wavelength spectra ( Table 5).…”

“…A multispectral sensor can collect radiation data from spectral bands with almost no overlapping [26,69]. Further, it can include data of near-infrared wavelength.…”

“…All bodies emit electromagnetic energy in the infrared (IR) wavelength range depending on temperature according to the principle of black body radiation. A thermal sensor detects this invisible energy (with wavelengths from 3-14 µm), then converts it to visible images showing the temperature of the target [69]. A thermal sensor is prone to errors owing to fluctuating environmental conditions in the air and other objects emitting or reflecting thermal infrared radiation.…”