4.7 Article

Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms

期刊

SCIENTIFIC REPORTS
卷 12, 期 1, 页码 -

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NATURE PORTFOLIO
DOI: 10.1038/s41598-022-11591-0

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资金

  1. USDA-NIFA-AFRI Award [2020-68013-32371, 2021-67013-33915]
  2. USDA-NIFA Hatch funds
  3. Texas A&M AgriLife Research
  4. Texas Corn Producers Board
  5. Iowa Corn Promotion Board
  6. Eugene Butler Endowed Chair in Biotechnology
  7. Texas A&M University/Association of Former Students (TAMU/AFS) Graduate Merit Fellowship
  8. Republic of Turkey, Ministry of National Education and Ministry of Agriculture and Forestry

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In this study, unoccupied aerial system (UAS) field-based high-throughput phenotyping (HTP) was used to collect high-resolution aerial imagery of maize hybrids to detect southern rust and senescence. Machine learning regressions outperformed a general linear model in terms of prediction accuracy and provided new quantitative indicators for maize senescence and southern rust. These findings have practical implications for precision agricultural practices.
Current methods in measuring maize (Zea mays L.) southern rust (Puccinia polyspora Underw.) and subsequent crop senescence require expert observation and 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. In total, 36 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 hybrid performance 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 (linear model RMSE ranged from 65.8 to 2396.5 across all traits, machine learning regressions RMSE ranged from 0.3 to 17.0). 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.

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