Time Series GWAS for Iron Deficiency Chlorosis Tolerance in Soybean using Aerial Imagery

Abstract

The use of drones has become a commonly used tool by plant scientists to aid in plant phenotyping endeavors. Iron deficiency chlorosis (IDC) is a commonly observed abiotic stress in soybean fields with high soil pH levels. IDC severity is visually classified, and recent work has shown that digital imaging techniques using both ground and UAS-acquired imagery can be utilized for automated severity ratings. In our study, we compared the classification accuracy of two flight altitudes to determine the optimal flight parameters for IDC phenotyping. In addition to this, we investigated the ability to use image-predicted scores for genome wide association study (GWAS), as well as the effect of IDC on traits such as canopy area and canopy growth and development. We also report a tool for semi-automated plot extraction from orthomosaic images that can be easily integrated with UAS. We noted that 43 days after planting was an ideal time for IDC severity ratings as the highest number of significant SNPs were reported at this timepoint.

Plant phenotyping relevance

UAS画像によるIDC重症度フェノタイピングについて、飛行高度の分類精度を比較検証し、半自動プロット抽出ツールも提示しているため、手法が中心的である。

Code and data availability

The supplied blocks describe drone imagery, XGBoost classification, plot-extraction scripts, and GWAS, but contain no public deposit, availability statement, or authors' URL for any dataset, image, code, or model. The only URL mentioned (SoyBase) is a generic external genotype database, not a paper-specific asset.

No evidence-backed public reproduction asset is currently recorded.