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Remote Sens. 2018, 10(2), 268;

Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery

Institute of Geography, GIS & RS Group, University of Cologne, 50923 Cologne, Germany
Author to whom correspondence should be addressed.
Received: 20 November 2017 / Revised: 15 January 2018 / Accepted: 7 February 2018 / Published: 9 February 2018
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Non-destructive monitoring of crop development is of key interest for agronomy and crop breeding. Crop Surface Models (CSMs) representing the absolute height of the plant canopy are a tool for this. In this study, fresh and dry barley biomass per plot are estimated from CSM-derived plot-wise plant heights. The CSMs are generated in a semi-automated manner using Structure-from-Motion (SfM)/Multi-View-Stereo (MVS) software from oblique stereo RGB images. The images were acquired automatedly from consumer grade smart cameras mounted at an elevated position on a lifting hoist. Fresh and dry biomass were measured destructively at four dates each in 2014 and 2015. We used exponential and simple linear regression based on different calibration/validation splits. Coefficients of determination R 2 between 0.55 and 0.79 and root mean square errors (RMSE) between 97 and 234 g/m2 are reached for the validation of predicted vs. observed dry biomass, while Willmott’s refined index of model performance d r ranges between 0.59 and 0.77. For fresh biomass, R 2 values between 0.34 and 0.61 are reached, with root mean square errors (RMSEs) between 312 and 785 g/m2 and d r between 0.39 and 0.66. We therefore established the possibility of using this novel low-cost system to estimate barley dry biomass over time. View Full-Text
Keywords: biomass; plant height; crop surface model; vegetation; monitoring; structure-from-motion biomass; plant height; crop surface model; vegetation; monitoring; structure-from-motion

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Brocks, S.; Bareth, G. Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery. Remote Sens. 2018, 10, 268.

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