Unmanned Aerial Vehicles (UAVs) open new opportunities in precision agriculture and phenotyping because data acquisition is timely flexible and causes only low monetary costs. In this study the potential of high spatially resolved UAV image data is investigated to quantify the lodging percentage and lodging severity using Structure from Motion (SfM) techniques and the derived canopy height (CH). The term lodging is defined as the permanent displacement of a plant from the upright position and the yield loss is strongly affected by the lodging severity. Traditionally lodging quantification is based on field observations that are neither cost-efficient nor objective. The lodging percentage can be determined by using a predefined threshold at which CH lodging occurs to enable the spatial assessment of lodging in an automatic manner. However, the used thresholds applied in different studies were defined by subjective inspections rather than by mathematical approaches. For that reason, an objective threshold approach is proposed in this study to overcome subjective estimates and improve the accuracy in lodging determination. Compared to manual reference data the UAVbased lodging percentage provided a very high correlation (R2 = 0.96,RMSE = 7.66%) when applied to breeding trials, which could also be confirmed under realistic farming conditions.

Based on the parameter lodging percentage an approach was developed to enable the assessment of lodging severity, an information that is important to estimate the yield impairment. The lodging severity variation cannot be determined by applying a single threshold approach, because that only represents a binary distinction between lodged and non-lodged areas. Furthermore, a single threshold cannot distinguish between slightly affected areas where a low yield impairment can be expected and heavily affected areas that necessarily cause yield loss. As a second step, the developed lodging severity parameter took the CH variations into account by its inbuilt weighting procedure and therefore can be used as an indicator for yield impairment.The UAV-based approach for the assessment of lodging percentage and severity was tested on three different ground sampling distances (0.54 cm, 1.09 cm and 1.57 cm). The lowest spatial resolution from highest flight altitude (100m) still let to high accuracy, which increases the practicability of the method for large areas.The lodging assessment can be used for insurance applications, precision farming and in breeder research. Besides the selection for genetic lines with greater lodging resistance, the lodging severities and yield impairments can be quantified as additional traits.