Articles
A view from above: can drones be used for image-based phenotyping in garden rose breeding?
Article number
1368_35
Pages
271 – 280
Language
English
Abstract
Despite its importance in breeding, high-throughput field phenotyping is still underused in ornamental plant breeding.
Here, we evaluate the possibilities of image-based phenotyping by unmanned aerial vehicle (UAV) equipped with an RGB sensor for the selection of candidate cultivars in garden rose (Rosa) breeding.
In garden rose breeding the most important selection traits relate to plant habitus, flowering and disease resistance.
Ideal roses flower all summer long with an abundance of flowers, have a uniform plant architecture and are disease resistant e.g., to black spot (Diplocarpon rosae). These parameters were assessed in the rose selection field of a rose breeding program.
The test field comprised 75 genotypes with 7-51 plants genotype‑1. By UAV flights from June until October 2019 data derived from RGB images for plant architecture, flowering and disease resistance were obtained.
Image analysis comprised: 1) canopy height modeling to estimate plant height and uniformity in plant architecture; 2) the quantification of flowering using the Woebbecke vegetation index; 3) the evaluation of disease resistance by the assessment of leaf drop caused by black spot through the Excess Green (ExG) vegetation index.
In this manuscript the research methodology is discussed: which parameters can be evaluated; how can these parameters be obtained by image analysis; when to plan UAV flights; what type of sensors and flight settings are needed, etc.? Also, best practices and recommendations to set up UAV experiments for roses and other ornamental crops in the field are given and ideas for future developments are discussed.
Here, we evaluate the possibilities of image-based phenotyping by unmanned aerial vehicle (UAV) equipped with an RGB sensor for the selection of candidate cultivars in garden rose (Rosa) breeding.
In garden rose breeding the most important selection traits relate to plant habitus, flowering and disease resistance.
Ideal roses flower all summer long with an abundance of flowers, have a uniform plant architecture and are disease resistant e.g., to black spot (Diplocarpon rosae). These parameters were assessed in the rose selection field of a rose breeding program.
The test field comprised 75 genotypes with 7-51 plants genotype‑1. By UAV flights from June until October 2019 data derived from RGB images for plant architecture, flowering and disease resistance were obtained.
Image analysis comprised: 1) canopy height modeling to estimate plant height and uniformity in plant architecture; 2) the quantification of flowering using the Woebbecke vegetation index; 3) the evaluation of disease resistance by the assessment of leaf drop caused by black spot through the Excess Green (ExG) vegetation index.
In this manuscript the research methodology is discussed: which parameters can be evaluated; how can these parameters be obtained by image analysis; when to plan UAV flights; what type of sensors and flight settings are needed, etc.? Also, best practices and recommendations to set up UAV experiments for roses and other ornamental crops in the field are given and ideas for future developments are discussed.
Authors
I. Borra-Serrano, A. Kemeltaeva, K. Van Laere, P. Lootens, L. Leus
Keywords
image analysis, phenotyping, plant selection, Rosa, UAS, UAV, unmanned aerial systems, unmanned aerial vehicle
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