Phenomic Data-Facilitated Rust and Senescence Prediction in Maize Using Machine Learning Algorithms

Abstract

Abstract Current methods in measuring maize ( Zea mays L.) southern rust ( Puccinia polyspora Underw.) and subsequent crop senescence require expert observation which are resource-intensive and prone to subjectivity. In this study, unoccupied aerial system (UAS) field-based high-throughput phenotyping (HTP) was employed to collect high-resolution aerial imagery of elite maize hybrids planted in the 2020 and 2021 growing seasons, with 13 UAS flights obtained from 2020 and 17 from 2021. Vegetation indices (VIs) were extracted from mosaicked aerial images that served as temporal phenomic predictors for southern rust scored in the field and senescence as scored using UAS-acquired mosaic images. Temporal best linear unbiased predictors (TBLUPs) were calculated using a nested model that treated the pedigree performances as nested within flights in terms of rust and senescence. All eight machine learning regressions tested (ridge, lasso, elastic net, random forest, support vector machine with radial and linear kernels, partial least squares, and k-nearest neighbors) outperformed a general linear model with both higher prediction accuracies (92-98%) and lower root mean squared error (RMSE) for rust and senescence scores. UAS-acquired VIs enabled the discovery of novel early quantitative phenotypic indicators of maize senescence and southern rust before being detectable by expert annotation and revealed positive correlations between grain filling time and yield (0.22 and 0.44 in 2020 and 2021), with practical implications for precision agricultural practices.

Plant phenotyping relevance

UAS高スループット画像、植生指数、機械学習を用いてトウモロコシのさび病と老化を推定する方法が研究の中心であり、植物状態の取得・予測手法を実質的に評価している。

Code and data availability

The preprint explicitly states that the authors' phenomic prediction analysis code (machine learning regressions in R caret) is publicly viewable on the first author's GitHub repository, matching an allowed URL. Supplementary Data 1 contains the phenomic data but no public URL is given for it, so only the code asset is