Articles
3D imaging and quantitative analysis of peach tree architecture via TreeQSM
Article number
1352_41
Pages
307 – 314
Language
English
Abstract
Peach tree morphology and vigor has seen renewed interest since development of new semi-dwarfing rootstocks.
Even so, fruit quality, yield, and disease resistance remain the primary traits of interest in most fruit breeding programs.
As such, canopy morphology and tree architecture remain relatively untapped areas of improvement.
These traits are considered challenging to study and phenotype because of their inherent complexity and quantitative nature.
While challenging, devising methodologies that can quantify canopy morphology and tree architecture would greatly aid in alleviating agronomic burdens felt by growers and researchers.
Better characterizing tree architecture would allow for easier identification of superior cultivars/genotypes that would innately require less pruning and/or training.
This in turn would optimize resource utilization.
To accomplish this, large amounts of branching data will be required to sufficiently study and quantify tree architecture.
Traditional means of collecting branching data however are difficult.
Most traditional methods are destructive and/or require manual counting/recording.
Manually collecting branching data are labor-intensive, repetitive, and prone to human error.
A more modern and novel approach to collecting this data are via 3D terrestrial laser scanning (TLS) technology, such as terrestrial LiDAR (tLiDAR). 3D tLiDAR scanners can generate point clouds of scanned trees that can be virtually modeled.
Running multiple iterations of these modeling simulations should yield the necessary branching data to begin better characterizing tree architecture.
Our goal was to test these 3D reconstructive models and assess their overall fit when compared to our scanned data.
The field data, alongside the general fit of these models, provided clarity as to the reliability of the quantitative data recovered from our 3D scans/reconstructions.
As a result, further studies into architecture and morphology will be made demonstrably more feasible with the provided methods.
Even so, fruit quality, yield, and disease resistance remain the primary traits of interest in most fruit breeding programs.
As such, canopy morphology and tree architecture remain relatively untapped areas of improvement.
These traits are considered challenging to study and phenotype because of their inherent complexity and quantitative nature.
While challenging, devising methodologies that can quantify canopy morphology and tree architecture would greatly aid in alleviating agronomic burdens felt by growers and researchers.
Better characterizing tree architecture would allow for easier identification of superior cultivars/genotypes that would innately require less pruning and/or training.
This in turn would optimize resource utilization.
To accomplish this, large amounts of branching data will be required to sufficiently study and quantify tree architecture.
Traditional means of collecting branching data however are difficult.
Most traditional methods are destructive and/or require manual counting/recording.
Manually collecting branching data are labor-intensive, repetitive, and prone to human error.
A more modern and novel approach to collecting this data are via 3D terrestrial laser scanning (TLS) technology, such as terrestrial LiDAR (tLiDAR). 3D tLiDAR scanners can generate point clouds of scanned trees that can be virtually modeled.
Running multiple iterations of these modeling simulations should yield the necessary branching data to begin better characterizing tree architecture.
Our goal was to test these 3D reconstructive models and assess their overall fit when compared to our scanned data.
The field data, alongside the general fit of these models, provided clarity as to the reliability of the quantitative data recovered from our 3D scans/reconstructions.
As a result, further studies into architecture and morphology will be made demonstrably more feasible with the provided methods.
Publication
Authors
J. Knapp-Wilson, R. Bohn Reckziegel, A. Bucksch, D.J. Chavez
Keywords
computational biology, phenomics, tLiDAR, horticulture, bioinformatics
Groups involved
Online Articles (87)