Time-series multispectral imaging in soybean for improving biomass and genomic prediction accuracy.

Abstract

Multispectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield-related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of aboveground biomass (AGB) in soybean [Glycine max (L.) Merr.]. Field experiments with 198 accessions of soybean were conducted with four different irrigation levels. Five vegetation indices (VIs) were calculated using MS images from soybean canopies from early vegetative to early reproductive stage. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multitrait genomic prediction. The prediction accuracy of the genotypic values of AGB from MS and genomic data largely outperformed that of the genomic data alone before the flowering stage (90% of accessions did not flower), suggesting that it would be possible to determine cross-combinations based on the predicted genotypic values of AGB. We compared the prediction accuracy of a model using the five VIs and a model using only one VI to predict the phenotypic values of AGB and found that the difference in prediction accuracy decreased over time at all irrigation levels except for the most severe drought. The difference in the most severe drought was not as small as that in the other treatments. Only the prediction accuracy of a model using the five VIs in the most severe droughts gradually increased over time. Therefore, the optimal timing for MS imaging may depend on the irrigation levels.

Plant phenotyping relevance

大豆の地上部バイオマスを推定するための時系列マルチスペクトル画像と植生指数を中心に、予測精度および撮像時期を評価しているため、植物表現型計測手法の実質的な適用・検証に該当する。

Code and data availability

The paper's data availability statement points to a public GitHub repository containing the datasets generated and analyzed (phenotype/vegetation-index data and analysis materials) for this soybean multispectral imaging study. Supplemental files are only docx summaries, not datasets themselves.