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.

Plant phenotyping relevance

UAV-SfM画像と客観的閾値を用いて、オオムギの倒伏率・倒伏程度という植物形質を定量化する手法を開発・検証しており、方法が研究の中心である。

Code and data availability

The supplied blocks describe UAV-based canopy height model generation and lodging assessment, but contain no public phenotype dataset, image deposit, author analysis code, or trained model with an availability statement. The only URLs present (Agisoft PhotoScan, CloudCompare, CC BY license) are generic software/library

No evidence-backed public reproduction asset is currently recorded.