From aerial drone to QTL: Leveraging next-generation phenotyping to reveal the genetics of color and height in field-grown Lactuca sativa

Abstract

Abstract In recent years, the automation of genotyping has significantly enhanced the efficiency of genome-wide association studies. As a result, phenotyping rather than genotyping is now the rate-limiting step, especially in field experiments. For this reason, there is a strong need to further automate in-field phenotyping. Here we present a GWAS study on 194 field-grown accessions of lettuce ( Lactuca sativa ). These accessions were non-destructively phenotyped at two time points 15 days apart using an unmanned aerial vehicle. Our high throughput phenotyping approach integrates an RGB camera, a multispectral camera to measure the reflectance at 5 wavelengths (blue, green, red, red edge, near-infrared), and precise height estimation. We used the mean and other descriptives such as median, quantiles, minimum and maximum to quantify different aspects of color and height variation in lettuce from the drone images. Using this approach, we confirm several previously described QTLs, now in populations grown under field conditions, and identify several new QTLs for plant-height and color.

Plant phenotyping relevance

ドローン搭載RGB・マルチスペクトルカメラと高さ推定を統合した圃場フェノタイピング手法を開発・適用し、画像からレタスの色と草丈を定量化しているため、方法が研究の中心です。

Code and data availability

The paper explicitly states that analysis scripts are publicly available on GitHub (SnoekLab/Dijkhuizen_etal_2025_Drone) and that extended data (raw data, intermediate steps, figure data, weather data) is deposited at the Utrecht University repository DOI 10.24416/UU01-S5FCM9. Both are paper-specific, public, and verbi