Abstract
Phenotyping of crops is important due to increasing pressure on food production. Therefore, an accurate estimation of biomass during the growing season can be important to optimize the yield. The potential of data acquisition by UAV-LiDAR to estimate fresh biomass and crop height was investigated for three different crops (potato, sugar beet, and winter wheat) grown in Wageningen (The Netherlands) from June to August 2018. Biomass was estimated using the 3DPI algorithm, while crop height was estimated using the mean height of a variable number of highest points for each m2. The 3DPI algorithm proved to estimate biomass well for sugar beet (R2 = 0.68, RMSE = 17.47 g/m2) and winter wheat (R2 = 0.82, RMSE = 13.94 g/m2). Also, the height estimates worked well for sugar beet (R2 = 0.70, RMSE = 7.4 cm) and wheat (R2 = 0.78, RMSE = 3.4 cm). However, for potato both plant height (R2 = 0.50, RMSE = 12 cm) and biomass estimation (R2 = 0.24, RMSE = 22.09 g/m2), it proved to be less reliable due to the complex canopy structure and the ridges on which potatoes are grown. In general, for accurate biomass and crop height estimates using those algorithms, the flight conditions (altitude, speed, location of flight lines) should be comparable to the settings for which the models are calibrated since changing conditions do influence the estimated biomass and crop height strongly.
Highlights
Phenotyping of crops is important to estimate biomass and the potential yield of new varieties of agricultural crops
The data for plant heights are from sugar beet, and the data for the biomass data are from the winter
Homan et al [17] showed that using a terrestrial laser scanner (TLS), the root mean squared error (RMSE) was 2.7 cm for wheat, which is comparable to the achieved accuracy by the RiEGL RiCOPTER UAV LiDAR system
Summary
Phenotyping of crops is important to estimate biomass and the potential yield of new varieties of agricultural crops. Due to the increasing need to increase food production and improve the associated quality, it is important to optimize the yield, for which accurate estimation of biomass during the growing season is needed. In this context, phenotyping focuses on the characterization of morphological as well as physiological crop traits. Morphological parameters, such as plant height, stem diameter, leaf area or leaf area index (LAI), leaf angle, stalk length, and in-plant space [1], can be determined with LiDAR (light detection and ranging). A drawback of this setup is the low throughput of the scanning system while there is a need to evaluate phenotypes under field conditions
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