Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery
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 h...
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doaj-78b6c1c5bb9e42088fd103e425a48c722020-11-25T00:07:56ZengMDPI AGRemote Sensing2072-42922018-02-0110226810.3390/rs10020268rs10020268Estimating Barley Biomass with Crop Surface Models from Oblique RGB ImagerySebastian Brocks0Georg Bareth1Institute of Geography, GIS & RS Group, University of Cologne, 50923 Cologne, GermanyInstitute of Geography, GIS & RS Group, University of Cologne, 50923 Cologne, GermanyNon-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.http://www.mdpi.com/2072-4292/10/2/268biomassplant heightcrop surface modelvegetationmonitoringstructure-from-motion |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sebastian Brocks Georg Bareth |
spellingShingle |
Sebastian Brocks Georg Bareth Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery Remote Sensing biomass plant height crop surface model vegetation monitoring structure-from-motion |
author_facet |
Sebastian Brocks Georg Bareth |
author_sort |
Sebastian Brocks |
title |
Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery |
title_short |
Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery |
title_full |
Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery |
title_fullStr |
Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery |
title_full_unstemmed |
Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery |
title_sort |
estimating barley biomass with crop surface models from oblique rgb imagery |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2018-02-01 |
description |
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. |
topic |
biomass plant height crop surface model vegetation monitoring structure-from-motion |
url |
http://www.mdpi.com/2072-4292/10/2/268 |
work_keys_str_mv |
AT sebastianbrocks estimatingbarleybiomasswithcropsurfacemodelsfromobliquergbimagery AT georgbareth estimatingbarleybiomasswithcropsurfacemodelsfromobliquergbimagery |
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