Classification of plant growth-promoting bacteria inoculation status and prediction of growth-related traits in tropical maize using hyperspectral image and genomic data

Abstract

Recent technological advances in high-throughput phenotyping have created new opportunities for the prediction of complex traits. In particular, phenomic prediction using hyperspectral reflectance could capture various signals that affect phenotypes genomic prediction might not explain. A total of 360 inbred maize lines with or without plant growth-promoting bacterial inoculation management under nitrogen stress were evaluated using 150 spectral wavelengths ranging from 386 to 1021 nm and 13,826 single-nucleotide polymorphisms. Six prediction models were explored to assess the predictive ability of hyperspectral and genomic data for inoculation status and plant growth-related traits. The best models for hyperspectral prediction were partial least squares and automated machine learning. The Bayesian ridge regression and BayesB were the best performers for genomic prediction. Overall, hyperspectral prediction showed greater predictive ability for shoot dry mass and stalk diameter, whereas genomic prediction was better for plant height. The prediction models that simultaneously accommodated both hyperspectral and genomic data resulted in a predictive ability as high as that of phenomics or genomics alone. Our results highlight the usefulness of hyperspectral-based phenotyping for management and phenomic prediction studies.

Genome-wide association analysis of hyperspectral reflectance data to dissect growth-related traits genetic architecture in maize under inoculation with plant growth-promoting bacteria

“…Image processing through the Spectral Python (Boggs, 2014) module was performed by applying a mask to remove the background from the image, and the mean reflectance of each pixel was used for further analysis. Hyperspectral imaging and processing details are described in Yassue et al (2022a). In addition, 131 hyperspectral indices (mathematical band combinations) were calculated based on the mean reflectance value for each wavelength using the R package hsdar (Lehnert et al, 2019).…”