Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery

Abstract

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.

Plant phenotyping relevance

低コストのRGB画像とSfM/MVSによるCSMから作物バイオマスを推定する取得・解析手法を開発し、検証しており、表現型計測が研究の中心である。

Code and data availability

The supplied blocks describe the barley biomass/CSM phenotyping study (oblique RGB imagery, CSM-derived plant heights, destructive biomass measurements, R-based regression analysis) but contain no public phenotype/trait dataset, imagery deposit, author analysis code, or supplement with an explicit availability URL. The

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