Phenomic selection in slash pine multi-temporally using UAV-multispectral imagery.

Area of Science:

• Forestry
• Plant Genetics
• Remote Sensing

Background:

• Genomic selection (GS) is efficient for plant breeding but impractical for long-lived trees with complex genomes.
• Traditional breeding methods for trees are slow due to long generation times.

Purpose of the Study:

• To develop a predictive approach for tree genetic improvement using UAV multispectral imagery.
• To evaluate the feasibility of using temporal multispectral data and vegetation indices (VIs) for genetic variation assessment in slash pine.
• To create a method for phenotypic selection (PS) independent of sequencing or pedigrees.

Main Methods:

• Utilized time-series UAV multispectral imagery to analyze genetic variation in slash pine growth traits.
• Developed a predictive model incorporating multispectral data plus vegetation indices (MV).
• Assessed the predictive ability of the MV approach alone and in combination with pedigree data.

Main Results:

• Temporal factors significantly influenced tree growth traits, with high genetic correlations observed across most months.
• The multispectral + VIs (MV) approach demonstrated promising and reliable predictive ability for selecting optimal slash pine families.
• The highest predictive values (0.52–0.56) were achieved in July, indicating seasonal importance.

Conclusions:

• UAV multispectral imagery offers a viable, high-throughput alternative for phenotypic selection in conifers.
• This approach can potentially reduce generation times and enhance genetic granularity in tree breeding, bypassing the need for sequencing or pedigree information.