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.
Highlights
The non-destructive monitoring of crop development and determination of crop traits is of key interest in agronomy and crop breeding, and efforts to estimate biomass from ground-based non-destructive measurements have been undertaken since the 1980s [1]
The noon acquisition time shows the highest R2 and lowest root mean square errors (RMSE) as well as the joint-highest Willmott’s refined index of model performance [26] and the best result in estimating the real plant height when compared to the manual plant height measurements performed using a ruler
Is comparable to the RMSE of derived plant height based on UnmannedAerial Vehicles (UAVs) or LiDAR measurements for other plants (e.g., 3.5 cm for LiDAR measurements on wheat [17], 10–13 cm for UAV-derived plant heights on poppy [18], and 10 cm for winter barley and wheat in [27]) and consistent with the measurements based on the 2014 field experiment presented in [16]
Summary
The non-destructive monitoring of crop development and determination of crop traits is of key interest in agronomy and crop breeding, and efforts to estimate biomass from ground-based non-destructive measurements have been undertaken since the 1980s [1]. CSMs are a tool for the multi-temporal monitoring of crop growth patterns and have been introduced as such by Hoffmeister et al [6] They can be created from different types of sensors such as LiDAR or RGB cameras using SfM [22] and Multi-View-Stereo (MVS) [23] techniques. The crop monitoring system presented in [16] was used to generate three CSMs per day during the growing seasons of 2014 and 2015 It uses a pair of smart cameras mounted in an elevated position to acquire oblique RGB imagery of the observed field.
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