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.

Crop surface models (CSMs) representing plant height above ground level are a useful tool for monitoring in-field crop growth variability and enabling precision agriculture applications. A semiautomated system for generating CSMs was implemented. It combines an Android application running on a set of smart cameras for image acquisition and transmission and a set of Python scripts automating the structure-from-motion (SfM) software package Agisoft Photoscan and ArcGIS. Only ground-control-point (GCP) marking was performed manually. This system was set up on a barley field experiment with nine different barley cultivars in the growing period of 2014. Images were acquired three times a day for a period of two months. CSMs were successfully generated for 95 out of 98 acquisitions between May 2 and June 30. The best linear regressions of the CSM-derived plot-wise averaged plant-heights compared to manual plant height measurements taken at four dates resulted in a coefficient of determination $R2$ of 0.87 and a root-mean-square error (RMSE) of 0.08 m, with Willmott’s refined index of model performance $dr$ equaling 0.78. In total, 103 mean plot heights were used in the regression based on the noon acquisition time. The presented system succeeded in semiautomatedly monitoring crop height on a plot scale to field scale.

Crop Surface Models (CSMs) are 2.5D raster surfaces representing absolute plant canopy height. Using multiple CMSs generated from data acquired at multiple time steps, a crop surface monitoring is enabled. This makes it possible to monitor crop growth over time and can be used for monitoring in-field crop growth variability which is useful in the context of high-throughput phenotyping. This study aims to evaluate several software packages for dense 3D reconstruction from multiple overlapping RGB images on field and plot-scale. A summer barley field experiment located at the Campus Klein-Altendorf of University of Bonn was observed by acquiring stereo images from an oblique angle using consumer-grade smart cameras. Two such cameras were mounted at an elevation of 10 m and acquired images for a period of two months during the growing period of 2014. The field experiment consisted of nine barley cultivars that were cultivated in multiple repetitions and nitrogen treatments. Manual plant height measurements were carried out at four dates during the observation period. The software packages Agisoft PhotoScan, VisualSfM with CMVS/PMVS2 and SURE are investigated. The point clouds are georeferenced through a set of ground control points. Where adequate results are reached, a statistical analysis is performed.