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Open AccessJournal ArticleDOI

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

Jelle ten Harkel, +2 more
- 18 Dec 2019 - 
- Vol. 12, Iss: 1, pp 17
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TLDR
The potential of data acquisition by UAV-LiDAR to estimate fresh biomass and crop height was investigated for three different crops grown in Wageningen (The Netherlands) from June to August 2018.
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.

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References
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Journal ArticleDOI

Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging

TL;DR: This study estimated fresh and dry biomass on a summer barley test site with 18 cultivars and two nitrogen (N)-treatments using the plant height (PH) from crop surface models (CSMs), which has potential for future application by non-professionals.
Journal ArticleDOI

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives

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Journal ArticleDOI

High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing

TL;DR: This study proves UAV based SfM has the potential to become a new standard for high-throughput phenotyping of in-field crop heights and provides a novel spatial mapping of crop height variation both at the field scale and also within individual plots.
Journal ArticleDOI

Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data

TL;DR: It is concluded that the combination of machine learning with UAV remote sensing is a promising alternative for estimating AGB and suggests that structural and spectral information can be considered simultaneously rather than separately when estimating biophysical crop parameters.
Journal ArticleDOI

Modeling approaches to estimate effective leaf area index from aerial discrete-return LIDAR

TL;DR: In this paper, four different methods have been proposed to estimate LAI using aerial light detection and ranging (LIDAR), but few systematic approaches have been attempted to assess the performance of these methods using a large, independent dataset with a wide range of LAI in a heterogeneous, mixed forest.
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