Quantifying Lodging Percentage and Lodging Severity Using a UAV-Based Canopy Height Model Combined with an Objective Threshold Approach

Abstract

Unmanned aerial vehicles (UAVs) open new opportunities in precision agriculture and phenotyping because of their flexibility and low cost. In this study, the potential of UAV imagery was evaluated to quantify lodging percentage and lodging severity of barley using structure from motion (SfM) techniques. Traditionally, lodging quantification is based on time-consuming manual field observations. Our UAV-based approach makes use of a quantitative threshold to determine lodging percentage in a first step. The derived lodging estimates showed a very high correlation to reference data (R2 = 0.96, root mean square error (RMSE) = 7.66%) when applied to breeding trials, which could also be confirmed under realistic farming conditions. As a second step, an approach was developed that allows the assessment of lodging severity, information that is important to estimate yield impairment, which also takes the intensity of lodging events into account. Both parameters were tested on three ground sample distances. The lowest spatial resolution acquired from the highest flight altitude (100 m) still led to high accuracy, which increases the practicability of the method for large areas. Our new lodging assessment procedure can be used for insurance applications, precision farming, and selecting for genetic lines with greater lodging resistance in breeding research.

Identification of Maize Lodging: A Case Study Using a Remotely Piloted Aircraft System

“…In order to identify the cut threshold between lodging and non-lodging plants, two methods were tested in this study, using both a fixed and variable parameter to establish the threshold. The fixed method followed the one proposed by Wilke et al (2019) for lodging evaluation, in which four lodging percentage thresholds related to the max height 99% percentile were calculated: 80%, 70%, 60%, and 50% of the plant height normalized reduction.…”

“…To analyze the proposed methods, 45 random validation polygons were distributed within the field (Figure 3a), each one with a sampling area of 12 m². In each sample area, lodging was manually quantified using the RGB orthomosaic (GSD = 0.023 cm) ( Figure 3a) and further compared with the lodging area determined by each method based on CSM (Figure 3a and 3b) using regression analysis, following the procedure proposed by Wilke et al (2019). Since lodging areas could be easily identified due to the high spatial resolution orthomosaic, we could precisely quantify the lodging percentage area within each sampled area, which allowed the validation process of the methods tested in this study.…”

“…In addition, these maps can be further used to carry out in situ investigations and help in understanding its relation with environmental factors and management practices (Li et al, 2014;Chu et al, 2017;Liu et al, 2018). The maps are a useful tool for precision farming and applications that rely on spatial information regarding lodging, such as damage assessments for insurance purposes (Wilke et al, 2019).…”

Crop Loss Evaluation Using Digital Surface Models from Unmanned Aerial Vehicles Data

“…These challenges can be addressed with deep learning, but they need a large labelled database to feed the algorithm [41]. While UAV-derived DSM from commercial UAVs have been largely used for small scale terrain mapping and 3D modelling of urban areas [42][43][44], they have been also proved useful for agricultural applications [45][46][47][48][49][50].As such, this study explored four terrain analysis tools, based on point cloud data or DSM, for the delineation and quantification, in hectares, of damages in field crops due to insurable causes, such as weather events and wildlife attacks. For this study, we focused on events that cause severe physical damages in the plant structure, generating abrupt depressions in the surface of the canopy, from which the plant rarely recovers.…”

“…These challenges can be addressed with deep learning, but they need a large labelled database to feed the algorithm [41]. While UAV-derived DSM from commercial UAVs have been largely used for small scale terrain mapping and 3D modelling of urban areas [42][43][44], they have been also proved useful for agricultural applications [45][46][47][48][49][50].…”