Abstract
Crop monitoring is important in precision agriculture. Estimating above-ground biomass helps to monitor crop vitality and to predict yield. In this study, we estimated fresh and dry biomass on a summer barley test site with 18 cultivars and two nitrogen (N)-treatments using the plant height (PH) from crop surface models (CSMs). The super-high resolution, multi-temporal (1 cm/pixel) CSMs were derived from red, green, blue (RGB) images captured from a small unmanned aerial vehicle (UAV). Comparison with PH reference measurements yielded an R2 of 0.92. The test site with different cultivars and treatments was monitored during “Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie” (BBCH) Stages 24–89. A high correlation was found between PH from CSMs and fresh biomass (R2 = 0.81) and dry biomass (R2 = 0.82). Five models for above-ground fresh and dry biomass estimation were tested by cross-validation. Modelling biomass between different N-treatments for fresh biomass produced the best results (R2 = 0.71). The main limitation was the influence of lodging cultivars in the later growth stages, producing irregular plant heights. The method has potential for future application by non-professionals, i.e., farmers.
Highlights
Monitoring crops throughout the vegetation period is one prerequisite for precision agriculture [1,2].In addition to natural factors, like water availability or soil quality, knowledge about the health status, nutrient supply and effects of agricultural management practices helps when estimating the predicted yield of a field [3,4,5]
80 kg N/m2; M2b: model for 80 kg N/m2 applied on plots treated with 40 kg N/m2; Model 3a (M3a): model for old cultivars applied on new cultivars; Model 3b (M3b): model of new cultivars applied on old cultivars. n = sample number of validation dataset; SE = standard error; R2 = coefficient of determination; with p < 0.0001; RMSE = root mean square error; RE = relative error
The detail of plot obtained from the CSM (PHCSM) is higher than PH obtained from the reference ground measurements (PHref), because PHCSM contains more than one pixel per plant and, the maximum height
Summary
In addition to natural factors, like water availability or soil quality, knowledge about the health status, nutrient supply and effects of agricultural management practices helps when estimating the predicted yield of a field [3,4,5]. Such knowledge can be obtained from crop parameters, such as plant height (PH), biomass, plant nitrogen content, soil nitrogen content and LAI, amongst other variables [6,7]. Biomass is a crucial parameter for calculating the NNI
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