Heterogeneity Assessment of Kenaf Breeding Field through Spatial Dependence Analysis on Crop Growth Status Map Derived by Unmanned Aerial Vehicle.

Abstract

The investigation of quantitative phenotypic traits resulting from the interaction between targeted genotypic traits and environmental factors is essential for breeding selection. Therefore, plot-wise controlled environmental factors must be invariable for accurate identification of phenotypes. However, the assumption of homogeneous variables within the open-field is not always accepted, and requires a spatial dependence analysis to determine whether site-specific environmental factors exist. In this study, spatial dependence within the kenaf breeding field was assessed in a geo-tagged height map derived from an unmanned aerial vehicle (UAV). Local indicators of spatial autocorrelation (LISA) were applied to the height map using Geoda software, and the LISA map was generated in order to recognize the existence of kenaf height status clusters. The spatial dependence of the breeding field used in this study appeared in a specific region. The cluster pattern was similar to the terrain elevation pattern of this field and highly correlated with drainage capacity. The cluster pattern could be utilized to design random blocks based on regions that have similar spatial dependence. We confirmed the potential of spatial dependence analysis on a crop growth status map, derived by UAV, for breeding strategy design with a tight budget.

Plant phenotyping relevance

UAVから作成したケナフの圃場内高さマップを対象に空間依存性分析を行い、育種圃場設計に利用するフェノタイピング関連ワークフローが研究の中心である。

Code and data availability

The paper's UAV-derived height maps, orthoimages, extracted individual heights, and LISA analysis outputs are not deposited anywhere. The Data Availability Statement reads 'Not applicable.' No author code, models, or datasets with public URLs are mentioned. The only URL (genebank.rda.go.kr) is the source of kenaf seeds

No evidence-backed public reproduction asset is currently recorded.