Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar

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.

Plant phenotyping relevance

UAV-LiDARとアルゴリズムにより作物バイオマスと草高を推定し、複数作物で精度評価・較正条件の影響を検証しているため、表現型取得法が研究の中心である。

Code and data availability

The supplied blocks describe UAV-LiDAR phenotyping of potato, sugar beet, and winter wheat, but contain no data availability statement, public repository deposit, or author code/model release. The only URL present is the CC BY license notice, which is not a paper-specific asset.

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